Detailed Step by Step configuration guide for Sovereign AI Cloud

Implementation Guide for Australian Government Departments

Executive Summary

This comprehensive guide provides detailed, step-by-step instructions for establishing a sovereign AI cloud solution specifically designed for Australian government use.

The solution prioritises data sovereignty, security, compliance with Australian regulations, and operational independence while maintaining scalability and performance.

Table of Contents

1. Understanding Sovereign AI Cloud

2. Pre-Implementation Planning

3. Infrastructure Setup

4. Security Implementation

5. AI Platform Configuration

6. Data Management and Storage

7. Compliance and Governance

8. Monitoring and Operations

9. Disaster Recovery and Business Continuity

10. Testing and Validation

11. Go-Live and Maintenance

1. Understanding Sovereign AI Cloud

1.1 Definition and Importance

A sovereign AI cloud is a cloud computing infrastructure that ensures complete control over data, applications, and AI models within national boundaries. For Australian government agencies, this means:

• Data Sovereignty: All data remains within Australian borders

• Legal Compliance: Adherence to Australian Privacy Act, GDPR, and government regulations

• Security Control: Full oversight of security protocols and access controls

• Operational Independence: Reduced dependency on foreign cloud providers

• Audit Capability: Complete transparency and auditability of all operations

1.2 Key Components

The sovereign AI cloud solution consists of:

• Infrastructure Layer: Physical servers, networking, and storage hosted in Australia

• Platform Layer: Kubernetes orchestration, container management, and service mesh

• AI/ML Layer: Machine learning frameworks, model serving, and training platforms

• Data Layer: Databases, data lakes, and analytics platforms

• Security Layer: Identity management, encryption, and compliance tools

• Governance Layer: Policy enforcement, audit trails, and compliance monitoring

2. Pre-Implementation Planning

2.1 Stakeholder Identification and Engagement

Step 1: Identify Key Stakeholders

Create a comprehensive stakeholder list including:

• Chief Information Officer (CIO)

• Chief Technology Officer (CTO)

• Chief Security Officer (CSO)

• Data Protection Officer (DPO)

• IT Operations Manager

• Compliance Manager

• Budget/Finance Manager

• End-user representatives from each department

Step 2: Establish Governance Structure

Set up a steering committee with:

• Executive sponsor (typically CIO or CTO)

• Project manager

• Technical lead

• Security lead

• Compliance lead

• Change management lead

Step 3: Define Roles and Responsibilities

Document specific responsibilities for each role:

• Project Manager: Timeline, budget, resource coordination

• Technical Lead: Architecture decisions, implementation oversight

• Security Lead: Security architecture, compliance validation

• Operations Lead: Day-to-day operations, monitoring, maintenance

2.2 Requirements Gathering

Step 4: Conduct Requirements Analysis

Create detailed requirements documentation covering:

Functional Requirements:

• AI/ML workload types (training, inference, data processing)

• Expected user base and concurrent users

• Performance requirements (latency, throughput)

• Integration requirements with existing systems

• Data processing and storage requirements

Non-Functional Requirements:

• Security requirements (encryption, access controls)

• Compliance requirements (specific regulations)

• Availability requirements (uptime, disaster recovery)

• Scalability requirements (growth projections)

• Performance requirements (response times, throughput)

Step 5: Create Technical Specifications

Document technical specifications including:

• Compute requirements (CPU, GPU, memory)

• Storage requirements (capacity, performance, redundancy)

• Network requirements (bandwidth, latency, security)

• Security requirements (encryption standards, access controls)

• Compliance requirements (audit trails, data retention)

2.3 Budget Planning

Step 6: Develop Comprehensive Budget

Create detailed budget covering:

Initial Setup Costs:

• Hardware procurement: $500,000 - $2,000,000

• Software licenses: $100,000 - $500,000

• Implementation services: $200,000 - $800,000

• Training and certification: $50,000 - $150,000

Ongoing Operational Costs:

• Staff salaries: $300,000 - $800,000 annually

• Maintenance and support: $100,000 - $300,000 annually

• Utility and facility costs: $50,000 - $200,000 annually

• Software renewals: $50,000 - $200,000 annually

Step 7: Secure Funding Approval

Prepare business case including:

• Cost-benefit analysis

• Risk assessment

• Implementation timeline

• Expected return on investment

• Comparison with alternative solutions

2.4 Vendor Selection

Step 8: Evaluate Australian Data Center Providers

Research and evaluate providers such as:

• NextDC: Primary Australian data center provider

• Digital Realty: International provider with Australian presence

• Equinix: Global provider with Australian facilities

• NEXTDC: Tier III/IV data centers in major Australian cities

Evaluation Criteria:

• Australian ownership and control

• Security certifications (ISO 27001, SOC 2)

• Compliance with Australian regulations

• Physical security measures

• Redundancy and disaster recovery capabilities

• Proximity to your primary location

Step 9: Select Infrastructure Partners

Choose partners for:

• Hardware: Dell, HPE, Cisco, Lenovo

• Software: Red Hat, VMware, Microsoft, Canonical

• Security: Fortinet, Palo Alto Networks, Check Point

• Monitoring: Splunk, Datadog, New Relic

3. Infrastructure Setup

3.1 Physical Infrastructure Preparation

Step 10: Data Center Site Selection

Select appropriate data center facilities based on:

• Location within Australia (preferably multiple sites)

• Tier III or IV certification

• Power redundancy (N+1 or 2N)

• Cooling systems (redundant HVAC)

• Physical security (biometric access, 24/7 monitoring)

• Connectivity options (multiple ISPs, dark fiber)

Step 11: Hardware Procurement

Procure hardware components:

Compute Nodes:

• Quantity: 20-50 servers (depending on scale)

• Specification: 2x Intel Xeon or AMD EPYC processors

• Memory: 256GB-1TB DDR4 ECC RAM

• Storage: 2x 480GB SSD (OS) + 4x 1.92TB NVMe SSD (data)

• Network: 2x 25GbE or 100GbE interfaces

• Recommended models: Dell PowerEdge R750, HPE ProLiant DL380

GPU Nodes (for AI workloads):

• Quantity: 5-20 servers

• GPUs: 4-8x NVIDIA A100, H100, or V100 per server

• CPU: 2x Intel Xeon or AMD EPYC processors

• Memory: 512GB-2TB DDR4 ECC RAM

• Storage: NVMe SSD for high-performance data access

• Recommended models: Dell PowerEdge R750xa, HPE ProLiant DL380a

Storage Systems:

• Primary storage: All-flash array (NetApp, Dell EMC, HPE)

• Capacity: 500TB-2PB usable

• Performance: 100,000+ IOPS, <1ms latency

• Backup storage: High-capacity disk arrays or tape libraries

Network Infrastructure:

• Core switches: 100GbE spine switches

• Top-of-rack switches: 25GbE/100GbE leaf switches

• Firewalls: Next-generation firewalls (Fortinet, Palo Alto)

• Load balancers: Hardware or software-based (F5, HAProxy)

3.2 Network Configuration

Step 12: Design Network Architecture

Implement a secure, high-performance network:

Network Segmentation:

• Management network (isolated for administrative access)

• Compute network (inter-node communication)

• Storage network (high-performance storage traffic)

• External network (internet and external connections)

IP Address Planning:

• Management: 10.1.0.0/16

• Compute: 10.2.0.0/16

• Storage: 10.3.0.0/16

• External: Public IP ranges as assigned

Step 13: Configure Network Security

Implement network security measures:

Firewall Configuration:

# Example firewall rules (adapt to your firewall platform)
# Allow management access from authorized networks
allow tcp from 10.0.0.0/8 to any port 22  # SSH
allow tcp from 10.0.0.0/8 to any port 443 # HTTPS

# Allow compute node communication
allow tcp from 10.2.0.0/16 to 10.2.0.0/16 port 6443  # Kubernetes API
allow tcp from 10.2.0.0/16 to 10.2.0.0/16 port 2379:2380  # etcd
allow tcp from 10.2.0.0/16 to 10.2.0.0/16 port 10250  # kubelet

# Block all other traffic by default
deny all

VPN Configuration:

• Deploy site-to-site VPN for multi-site connectivity

• Configure client VPN for remote administrative access

• Use IPsec with AES-256 encryption

• Implement certificate-based authentication

3.3 Operating System Installation

Step 14: Install Base Operating System

Install Ubuntu 20.04 LTS or Red Hat Enterprise Linux 8 on all nodes:

Automated Installation Process:

# Create automated installation script
#!/bin/bash

# Set hostname
hostnamectl set-hostname $NODE_NAME

# Update system
apt update && apt upgrade -y

# Install essential packages
apt install -y curl wget vim git htop iotop nmap

# Configure SSH
sed -i 's/#PasswordAuthentication yes/PasswordAuthentication no/' /etc/ssh/sshd_config
systemctl restart ssh

# Configure firewall
ufw enable
ufw allow 22/tcp
ufw allow 443/tcp
ufw allow 80/tcp

# Install Docker
curl -fsSL https://get.docker.com -o get-docker.sh
sh get-docker.sh
usermod -aG docker $USER

# Install Kubernetes tools
curl -s https://packages.cloud.google.com/apt/doc/apt-key.gpg | apt-key add -
echo "deb https://apt.kubernetes.io/ kubernetes-xenial main" | tee -a /etc/apt/sources.list.d/kubernetes.list
apt update
apt install -y kubectl kubeadm kubelet

Step 15: Configure System Security

Implement security hardening:

System Hardening Script:

#!/bin/bash

# Disable unnecessary services
systemctl disable bluetooth
systemctl disable cups
systemctl disable avahi-daemon

# Configure audit logging
apt install -y auditd
systemctl enable auditd
systemctl start auditd

# Configure log rotation
cat > /etc/logrotate.d/system-logs << EOF
/var/log/*.log {
    daily
    rotate 30
    compress
    delaycompress
    missingok
    notifempty
    create 0644 root root
}
EOF

# Set up automatic security updates
apt install -y unattended-upgrades
dpkg-reconfigure -plow unattended-upgrades

# Configure fail2ban
apt install -y fail2ban
systemctl enable fail2ban
systemctl start fail2ban

4. Security Implementation

4.1 Identity and Access Management

Step 16: Deploy Identity Management System

Install and configure OpenLDAP or Active Directory:

OpenLDAP Installation:

# Install OpenLDAP
apt install -y slapd ldap-utils

# Configure basic LDAP structure
cat > base.ldif << EOF
dn: ou=People,dc=example,dc=com
objectClass: organizationalUnit
ou: People

dn: ou=Groups,dc=example,dc=com
objectClass: organizationalUnit
ou: Groups

dn: cn=admins,ou=Groups,dc=example,dc=com
objectClass: groupOfNames
cn: admins
member: cn=admin,ou=People,dc=example,dc=com
EOF

ldapadd -x -D "cn=admin,dc=example,dc=com" -W -f base.ldif

Step 17: Configure Multi-Factor Authentication

Deploy MFA solution using tools like:

• FreeOTP: Open-source OTP solution

• privacyIDEA: Enterprise MFA platform

• Duo Security: Cloud-based MFA service

FreeOTP Configuration:

# Install FreeOTP server
apt install -y privacyidea privacyidea-apache2

# Configure Apache for privacyIDEA
a2enmod wsgi
a2enmod headers
a2enmod ssl
a2ensite privacyidea

# Start services
systemctl restart apache2
systemctl enable privacyidea

4.2 Encryption Implementation

Step 18: Deploy Certificate Authority

Set up internal PKI infrastructure:

Create Root CA:

# Generate root CA private key
openssl genrsa -out ca-key.pem 4096

# Create root CA certificate
openssl req -new -x509 -days 3650 -key ca-key.pem -out ca.pem \
  -subj "/C=AU/ST=NSW/L=Sydney/O=Australian Government/CN=Root CA"

# Generate server certificate
openssl genrsa -out server-key.pem 4096
openssl req -new -key server-key.pem -out server.csr \
  -subj "/C=AU/ST=NSW/L=Sydney/O=Australian Government/CN=*.example.com"

# Sign server certificate with CA
openssl x509 -req -days 365 -in server.csr -CA ca.pem -CAkey ca-key.pem \
  -CAcreateserial -out server.pem

Step 19: Configure Encryption at Rest

Implement full disk encryption:

LUKS Encryption Setup:

# Install cryptsetup
apt install -y cryptsetup

# Create encrypted partition
cryptsetup luksFormat /dev/sdb
cryptsetup luksOpen /dev/sdb encrypted_disk

# Create filesystem
mkfs.ext4 /dev/mapper/encrypted_disk

# Mount encrypted partition
mkdir /encrypted
mount /dev/mapper/encrypted_disk /encrypted

# Add to fstab for automatic mounting
echo "encrypted_disk /encrypted ext4 defaults 0 0" >> /etc/fstab
echo "encrypted_disk /dev/sdb none luks" >> /etc/crypttab

4.3 Network Security

Step 20: Configure Network Intrusion Detection

Deploy Suricata for network monitoring:

Suricata Installation:

# Install Suricata
apt install -y suricata

# Configure Suricata
cat > /etc/suricata/suricata.yaml << EOF
vars:
  address-groups:
    HOME_NET: "[10.0.0.0/8]"
    EXTERNAL_NET: "!$HOME_NET"
    
af-packet:
  - interface: eth0
    cluster-id: 99
    cluster-type: cluster_flow
    defrag: yes
    
outputs:
  - eve-log:
      enabled: yes
      filetype: regular
      filename: eve.json
      
rule-files:
  - /var/lib/suricata/rules/suricata.rules
  - /var/lib/suricata/rules/emerging-threats.rules
EOF

# Start Suricata
systemctl enable suricata
systemctl start suricata

Step 21: Deploy Web Application Firewall

Install and configure ModSecurity:

ModSecurity Configuration:

# Install ModSecurity
apt install -y libapache2-mod-security2

# Enable ModSecurity
a2enmod security2

# Configure ModSecurity
cat > /etc/modsecurity/modsecurity.conf << EOF
SecRuleEngine On
SecRequestBodyAccess On
SecResponseBodyAccess On
SecResponseBodyMimeType text/plain text/html text/xml application/json
SecDefaultAction "phase:1,log,auditlog,pass"
SecDefaultAction "phase:2,log,auditlog,pass"
EOF

# Install OWASP Core Rule Set
cd /etc/modsecurity
wget https://github.com/coreruleset/coreruleset/archive/v3.3.0.tar.gz
tar -xzf v3.3.0.tar.gz
mv coreruleset-3.3.0 crs
cp crs/crs-setup.conf.example crs/crs-setup.conf

# Enable CRS
echo "Include /etc/modsecurity/crs/crs-setup.conf" >> /etc/modsecurity/modsecurity.conf
echo "Include /etc/modsecurity/crs/rules/*.conf" >> /etc/modsecurity/modsecurity.conf

systemctl restart apache2

5. AI Platform Configuration

5.1 Container Orchestration Setup

Step 22: Install Kubernetes

Deploy Kubernetes cluster for container orchestration:

Master Node Setup:

# Initialize Kubernetes cluster
kubeadm init --pod-network-cidr=10.244.0.0/16 --apiserver-advertise-address=<MASTER_IP>

# Configure kubectl for admin user
mkdir -p $HOME/.kube
cp -i /etc/kubernetes/admin.conf $HOME/.kube/config
chown $(id -u):$(id -g) $HOME/.kube/config

# Install Flannel network plugin
kubectl apply -f https://raw.githubusercontent.com/coreos/flannel/master/Documentation/kube-flannel.yml

# Remove master node taint (if running workloads on master)
kubectl taint nodes --all node-role.kubernetes.io/master-

Worker Node Setup:

# Join worker nodes to cluster (get token from master)
kubeadm join <MASTER_IP>:6443 --token <TOKEN> --discovery-token-ca-cert-hash <HASH>

Step 23: Configure GPU Support

Install NVIDIA GPU support for AI workloads:

GPU Driver Installation:

# Install NVIDIA drivers
apt install -y nvidia-driver-470
reboot

# Install NVIDIA Container Toolkit
distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | apt-key add -
curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | tee /etc/apt/sources.list.d/nvidia-docker.list

apt update
apt install -y nvidia-container-toolkit
systemctl restart docker

# Install NVIDIA Device Plugin for Kubernetes
kubectl apply -f https://raw.githubusercontent.com/NVIDIA/k8s-device-plugin/v0.12.0/nvidia-device-plugin.yml

5.2 AI/ML Framework Deployment

Step 24: Deploy MLflow

Set up MLflow for experiment tracking and model management:

MLflow Deployment:

# mlflow-deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: mlflow-server
spec:
  replicas: 1
  selector:
    matchLabels:
      app: mlflow-server
  template:
    metadata:
      labels:
        app: mlflow-server
    spec:
      containers:
      - name: mlflow-server
        image: mlflow/mlflow:latest
        ports:
        - containerPort: 5000
        env:
        - name: MLFLOW_BACKEND_STORE_URI
          value: "postgresql://mlflow:password@postgres:5432/mlflow"
        - name: MLFLOW_DEFAULT_ARTIFACT_ROOT
          value: "s3://mlflow-artifacts"
        command:
        - mlflow
        - server
        - --host
        - 0.0.0.0
        - --port
        - "5000"
        - --backend-store-uri
        - $(MLFLOW_BACKEND_STORE_URI)
        - --default-artifact-root
        - $(MLFLOW_DEFAULT_ARTIFACT_ROOT)
---
apiVersion: v1
kind: Service
metadata:
  name: mlflow-service
spec:
  selector:
    app: mlflow-server
  ports:
  - port: 5000
    targetPort: 5000
  type: LoadBalancer

Step 25: Deploy JupyterHub

Set up JupyterHub for data science workflows:

JupyterHub Installation:

# Install JupyterHub
pip install jupyterhub
pip install jupyterlab
pip install dockerspawner

# Configure JupyterHub
cat > /etc/jupyterhub/jupyterhub_config.py << EOF
c.JupyterHub.spawner_class = 'dockerspawner.DockerSpawner'
c.DockerSpawner.image = 'jupyter/datascience-notebook:latest'
c.DockerSpawner.network_name = 'jupyterhub-network'
c.Authenticator.admin_users = {'admin'}
c.JupyterHub.hub_ip = '0.0.0.0'
c.JupyterHub.port = 8000
EOF

# Create systemd service
cat > /etc/systemd/system/jupyterhub.service << EOF
[Unit]
Description=JupyterHub
After=syslog.target network.target

[Service]
User=jupyterhub
ExecStart=/usr/local/bin/jupyterhub -f /etc/jupyterhub/jupyterhub_config.py
Restart=always

[Install]
WantedBy=multi-user.target
EOF

systemctl enable jupyterhub
systemctl start jupyterhub

Step 26: Deploy Kubeflow

Install Kubeflow for ML workflow management:

Kubeflow Installation:

# Install kfctl
wget https://github.com/kubeflow/kfctl/releases/download/v1.2.0/kfctl_v1.2.0-0-gbc038f9_linux.tar.gz
tar -xvf kfctl_v1.2.0-0-gbc038f9_linux.tar.gz
mv kfctl /usr/local/bin/

# Create Kubeflow deployment
export KF_NAME=kubeflow
export BASE_DIR=/opt/kubeflow
export KF_DIR=${BASE_DIR}/${KF_NAME}
export CONFIG_URI="https://raw.githubusercontent.com/kubeflow/manifests/v1.2-branch/kfdef/kfctl_k8s_istio.v1.2.0.yaml"

mkdir -p ${KF_DIR}
cd ${KF_DIR}
kfctl apply -V -f ${CONFIG_URI}

# Wait for deployment to complete
kubectl get pods -n kubeflow

6. Data Management and Storage

6.1 Database Setup

Step 27: Deploy PostgreSQL Cluster

Set up high-availability PostgreSQL for metadata storage:

PostgreSQL HA Configuration:

# postgresql-ha.yaml
apiVersion: v1
kind: ConfigMap
metadata:
  name: postgres-config
data:
  postgresql.conf: |
    listen_addresses = '*'
    max_connections = 100
    shared_buffers = 128MB
    effective_cache_size = 4GB
    maintenance_work_mem = 64MB
    checkpoint_completion_target = 0.9
    wal_buffers = 16MB
    default_statistics_target = 100
    random_page_cost = 1.1
    effective_io_concurrency = 200
    work_mem = 4MB
    min_wal_size = 80MB
    max_wal_size = 1GB
    max_worker_processes = 8
    max_parallel_workers_per_gather = 4
    max_parallel_workers = 8
    max_parallel_maintenance_workers = 4
    
---
apiVersion: apps/v1
kind: StatefulSet
metadata:
  name: postgres-primary
spec:
  serviceName: postgres-primary
  replicas: 1
  selector:
    matchLabels:
      app: postgres-primary
  template:
    metadata:
      labels:
        app: postgres-primary
    spec:
      containers:
      - name: postgres
        image: postgres:13
        env:
        - name: POSTGRES_DB
          value: "postgres"
        - name: POSTGRES_USER
          value: "postgres"
        - name: POSTGRES_PASSWORD
          value: "SecurePassword123!"
        - name: PGDATA
          value: "/var/lib/postgresql/data/pgdata"
        ports:
        - containerPort: 5432
        volumeMounts:
        - name: postgres-storage
          mountPath: /var/lib/postgresql/data
        - name: config-volume
          mountPath: /etc/postgresql/postgresql.conf
          subPath: postgresql.conf
      volumes:
      - name: config-volume
        configMap:
          name: postgres-config
  volumeClaimTemplates:
  - metadata:
      name: postgres-storage
    spec:
      accessModes: ["ReadWriteOnce"]
      resources:
        requests:
          storage: 100Gi

Step 28: Configure Data Lake Storage

Deploy MinIO for object storage:

MinIO Deployment:

# minio-deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: minio
spec:
  replicas: 1
  selector:
    matchLabels:
      app: minio
  template:
    metadata:
      labels:
        app: minio
    spec:
      containers:
      - name: minio
        image: minio/minio:latest
        args:
        - server
        - /data
        - --console-address
        - :9090
        env:
        - name: MINIO_ROOT_USER
          value: "admin"
        - name: MINIO_ROOT_PASSWORD
          value: "SecureMinioPassword123!"
        ports:
        - containerPort: 9000
        - containerPort: 9090
        volumeMounts:
        - name: minio-storage
          mountPath: /data
      volumes:
      - name: minio-storage
        persistentVolumeClaim:
          claimName: minio-pvc
---
apiVersion: v1
kind: PersistentVolumeClaim
metadata:
  name: minio-pvc
spec:
  accessModes:
    - ReadWriteOnce
  resources:
    requests:
      storage: 1Ti
---
apiVersion: v1
kind: Service
metadata:
  name: minio-service
spec:
  selector:
    app: minio
  ports:
  - name: api
    port: 9000
    targetPort: 9000
  - name: console
    port: 9090
    targetPort: 9090
  type: LoadBalancer

6.2 Data Pipeline Configuration

Step 29: Deploy Apache Airflow

Set up Airflow for data pipeline orchestration:

Airflow Installation:

# Install Airflow
pip install apache-airflow[celery,postgres,redis,s3]==2.3.0

# Initialize Airflow database
airflow db init

# Create admin user
airflow users create \
  --username admin \
  --firstname Admin \
  --lastname User \
  --role Admin \
  --email admin@example.com \
  --password admin123

# Configure Airflow
cat > /opt/airflow/airflow.cfg << EOF
[core]
dags_folder = /opt/airflow/dags
base_log_folder = /opt/airflow/logs
remote_logging = False
remote_base_log_folder = 
remote_log_conn_id = 
encrypt_s3_logs = False
logging_level = INFO
fab_logging_level = WARN
logging_config_class = 
colored_console_log = True
colored_log_format = [%%(blue)s%%(asctime)s%%(reset)s] {%%(blue)s%%(filename)s:%%(reset)s%%(lineno)d} %%(log_color)s%%(levelname)s%%(reset)s - %%(log_color)s%%(message)s%%(reset)s
colored_formatter_class = airflow.utils.log.colored_log.CustomTTYColoredFormatter
log_format = [%%(asctime)s] {%%(filename)s:%%(lineno)d} %%(levelname)s - %%(message)s
simple_log_format = %%(asctime)s %%(levelname)s - %%(message)s
executor = CeleryExecutor
sql_alchemy_conn = postgresql+psycopg2://airflow:airflow@postgres:5432/airflow
sql_engine_encoding = utf-8
sql_alchemy_pool_enabled = True
sql_alchemy_pool_size = 5
sql_alchemy_max_overflow = 10
sql_alchemy_pool_recycle = 1800
sql_alchemy_pool_pre_ping = True
sql_alchemy_schema = 
parallelism = 32
dag_concurrency = 16
dags_are_paused_at_creation = True
non_pooled_task_slot_count = 128
max_active_runs_per_dag = 16
load_examples = False
plugins_folder = /opt/airflow/plugins
fernet_key = 
donot_pickle = True
dagbag_import_timeout = 30
dagbag_import_error_tracebacks = True
dagbag_import_error_traceback_depth = 2
dag_file_processor_timeout = 50
task_runner = StandardTaskRunner
default_impersonation = 
security = 
unit_test_mode = False
enable_xcom_pickling = True
killed_task_cleanup_time = 60
dag_run_conf_overrides_params = True
dag_discovery_safe_mode = True
default_task_retries = 0
default_task_retry_delay = 300
default_task_weight_rule = downstream
min_serialized_dag_update_interval = 30
min_serialized_dag_fetch_interval = 10
max_serialized_dag_fetch_tries = 5
allowed_deserialization_classes = airflow\..*
store_serialized_dags = False
store_dag_code = False
max_num_rendered_ti_fields_per_task = 30
check_slas = True
xcom_backend = airflow.models.xcom.BaseXCom
dag_ignore_file_syntax = regexp
dag_orientation = LR
dag_default_view = grid
dag_dependencies_view = grid
render_template_as_native_obj = False
default_ui_timezone = UTC
hide_sensitive_var_conn_fields = True
sensitive_var_conn_names = 
default_wrap_method_names = 
EOF

# Create systemd services
cat > /etc/systemd/system/airflow-webserver.service << EOF
[Unit]
Description=Airflow webserver daemon
After=network.target postgresql.service mysql.service redis.service rabbitmq-server.service
Wants=postgresql.service mysql.service redis.service rabbitmq-server.service

[Service]
EnvironmentFile=/etc/sysconfig/airflow
User=airflow
Group=airflow
Type=notify
ExecStart=/usr/local/bin/airflow webserver
Restart=on-failure
RestartSec=5s
PrivateTmp=true

[Install]
WantedBy=multi-user.target
EOF

systemctl enable airflow-webserver
systemctl start airflow-webserver

Step 30: Configure Data Ingestion

Set up data ingestion pipelines using Apache Kafka:

Kafka Cluster Deployment:

# kafka-cluster.yaml
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
  name: data-cluster
spec:
  kafka:
    version: 3.2.0
    replicas: 3
    listeners:
      - name: plain
        port: 9092
        type: internal
        tls: false
      - name: tls
        port: 9093
        type: internal
        tls: true
      - name: external
        port: 9094
        type: nodeport
        tls: false
    config:
      offsets.topic.replication.factor: 3
      transaction.state.log.replication.factor: 3
      transaction.state.log.min.isr: 2
      default.replication.factor: 3
      min.insync.replicas: 2
      inter.broker.protocol.version: "3.2"
    storage:
      type: jbod
      volumes:
      - id: 0
        type: persistent-claim
        size: 100Gi
        deleteClaim: false
  zookeeper:
    replicas: 3
    storage:
      type: persistent-claim
      size: 10Gi
      deleteClaim: false
  entityOperator:
    topicOperator: {}
    userOperator: {}

Data Ingestion Script:

# data_ingestion.py
from kafka import KafkaProducer, KafkaConsumer
import json
import pandas as pd
from datetime import datetime
import logging

class DataIngestor:
    def __init__(self, kafka_servers, topic_name):
        self.kafka_servers = kafka_servers
        self.topic_name = topic_name
        self.producer = KafkaProducer(
            bootstrap_servers=kafka_servers,
            value_serializer=lambda x: json.dumps(x).encode('utf-8')
        )
        
    def ingest_csv_data(self, csv_file_path):
        """Ingest data from CSV file"""
        try:
            df = pd.read_csv(csv_file_path)
            for index, row in df.iterrows():
                message = {
                    'timestamp': datetime.now().isoformat(),
                    'data': row.to_dict(),
                    'source': csv_file_path
                }
                self.producer.send(self.topic_name, value=message)
            self.producer.flush()
            logging.info(f"Successfully ingested {len(df)} records from {csv_file_path}")
        except Exception as e:
            logging.error(f"Error ingesting data: {str(e)}")
            
    def ingest_api_data(self, api_endpoint):
        """Ingest data from API endpoint"""
        try:
            response = requests.get(api_endpoint)
            if response.status_code == 200:
                data = response.json()
                message = {
                    'timestamp': datetime.now().isoformat(),
                    'data': data,
                    'source': api_endpoint
                }
                self.producer.send(self.topic_name, value=message)
                self.producer.flush()
                logging.info(f"Successfully ingested API data from {api_endpoint}")
        except Exception as e:
            logging.error(f"Error ingesting API data: {str(e)}")

# Usage example
if __name__ == "__main__":
    ingestor = DataIngestor(['kafka-broker:9092'], 'government-data')
    ingestor.ingest_csv_data('/data/census_data.csv')

7. Compliance and Governance

7.1 Data Governance Framework

Step 31: Implement Data Classification

Create data classification policies and automated tagging:

Data Classification Policy:

# data-classification-policy.yaml
apiVersion: v1
kind: ConfigMap
metadata:
  name: data-classification-policy
data:
  policy.yaml: |
    classification_levels:
      - name: "OFFICIAL"
        description: "Standard business information"
        retention_days: 2555  # 7 years
        encryption_required: false
        access_controls:
          - "authenticated_users"
      - name: "OFFICIAL:Sensitive"
        description: "Personal or sensitive business information"
        retention_days: 2555  # 7 years
        encryption_required: true
        access_controls:
          - "authorized_personnel"
      - name: "PROTECTED"
        description: "Information that could cause damage if disclosed"
        retention_days: 3650  # 10 years
        encryption_required: true
        access_controls:
          - "cleared_personnel"
      - name: "SECRET"
        description: "Information that could cause serious damage if disclosed"
        retention_days: 7300  # 20 years
        encryption_required: true
        access_controls:
          - "security_cleared"
    
    auto_classification_rules:
      - pattern: ".*ssn.*|.*tax.*|.*medicare.*"
        classification: "OFFICIAL:Sensitive"
      - pattern: ".*classified.*|.*confidential.*"
        classification: "PROTECTED"
      - pattern: ".*secret.*|.*national.*security.*"
        classification: "SECRET"

Data Classification Service:

# data_classifier.py
import re
import yaml
from typing import Dict, List, Optional

class DataClassifier:
    def __init__(self, policy_config_path: str):
        with open(policy_config_path, 'r') as f:
            self.policy = yaml.safe_load(f)
        
    def classify_data(self, data: Dict, metadata: Dict = None) -> str:
        """Classify data based on content and metadata"""
        content_str = str(data).lower()
        
        # Check auto-classification rules
        for rule in self.policy['auto_classification_rules']:
            if re.search(rule['pattern'], content_str):
                return rule['classification']
        
        # Default classification
        return "OFFICIAL"
    
    def get_retention_policy(self, classification: str) -> Dict:
        """Get retention policy for classification level"""
        for level in self.policy['classification_levels']:
            if level['name'] == classification:
                return {
                    'retention_days': level['retention_days'],
                    'encryption_required': level['encryption_required'],
                    'access_controls': level['access_controls']
                }
        return None
    
    def apply_data_controls(self, data_id: str, classification: str):
        """Apply data controls based on classification"""
        policy = self.get_retention_policy(classification)
        if policy:
            # Apply encryption if required
            if policy['encryption_required']:
                self.encrypt_data(data_id)
            
            # Set retention schedule
            self.set_retention_schedule(data_id, policy['retention_days'])
            
            # Configure access controls
            self.configure_access_controls(data_id, policy['access_controls'])
    
    def encrypt_data(self, data_id: str):
        """Encrypt data using AES-256"""
        # Implementation for data encryption
        pass
    
    def set_retention_schedule(self, data_id: str, retention_days: int):
        """Set automatic deletion schedule"""
        # Implementation for retention scheduling
        pass
    
    def configure_access_controls(self, data_id: str, access_controls: List[str]):
        """Configure RBAC for data access"""
        # Implementation for access control configuration
        pass

Step 32: Deploy Data Loss Prevention

Implement DLP using OpenDLP or similar tools:

DLP Configuration:

# Install OpenDLP
git clone https://github.com/opendlp/opendlp.git
cd opendlp
./configure
make install

# Configure DLP policies
cat > /etc/opendlp/dlp-policies.conf << EOF
# Australian Privacy Act compliance
policy "australian_privacy" {
    name = "Australian Privacy Act Compliance"
    description = "Detect personal information under Australian Privacy Act"
    
    rules = [
        {
            name = "medicare_number"
            pattern = "[0-9]{10}\\s[0-9]"
            severity = "high"
            action = "block"
        },
        {
            name = "tax_file_number"
            pattern = "[0-9]{3}\\s[0-9]{3}\\s[0-9]{3}"
            severity = "high" 
            action = "block"
        },
        {
            name = "drivers_license"
            pattern = "[A-Z]{2}[0-9]{6,8}"
            severity = "medium"
            action = "alert"
        },
        {
            name = "email_address"
            pattern = "[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\\.[a-zA-Z]{2,}"
            severity = "low"
            action = "log"
        }
    ]
}

# Government classification markings
policy "classification_markings" {
    name = "Government Classification Markings"
    description = "Detect government classification markings"
    
    rules = [
        {
            name = "secret_marking"
            pattern = "SECRET|CONFIDENTIAL|TOP\\sSECRET"
            severity = "critical"
            action = "block"
        },
        {
            name = "protected_marking" 
            pattern = "PROTECTED|OFFICIAL:Sensitive"
            severity = "high"
            action = "encrypt"
        }
    ]
}
EOF

# Start DLP service
systemctl enable opendlp
systemctl start opendlp

7.2 Audit and Compliance Monitoring

Step 33: Deploy Audit Logging System

Set up comprehensive audit logging using ELK stack:

Elasticsearch Deployment:

# elasticsearch.yaml
apiVersion: apps/v1
kind: StatefulSet
metadata:
  name: elasticsearch
spec:
  serviceName: elasticsearch
  replicas: 3
  selector:
    matchLabels:
      app: elasticsearch
  template:
    metadata:
      labels:
        app: elasticsearch
    spec:
      containers:
      - name: elasticsearch
        image: docker.elastic.co/elasticsearch/elasticsearch:8.5.0
        ports:
        - containerPort: 9200
        - containerPort: 9300
        env:
        - name: cluster.name
          value: "audit-cluster"
        - name: node.name
          valueFrom:
            fieldRef:
              fieldPath: metadata.name
        - name: discovery.seed_hosts
          value: "elasticsearch-0.elasticsearch,elasticsearch-1.elasticsearch,elasticsearch-2.elasticsearch"
        - name: cluster.initial_master_nodes
          value: "elasticsearch-0,elasticsearch-1,elasticsearch-2"
        - name: ES_JAVA_OPTS
          value: "-Xms1g -Xmx1g"
        - name: xpack.security.enabled
          value: "true"
        - name: xpack.security.transport.ssl.enabled
          value: "true"
        - name: xpack.security.http.ssl.enabled
          value: "true"
        volumeMounts:
        - name: elasticsearch-storage
          mountPath: /usr/share/elasticsearch/data
        resources:
          limits:
            memory: 2Gi
            cpu: 1000m
          requests:
            memory: 2Gi
            cpu: 1000m
  volumeClaimTemplates:
  - metadata:
      name: elasticsearch-storage
    spec:
      accessModes: ["ReadWriteOnce"]
      resources:
        requests:
          storage: 100Gi

Logstash Configuration:

# logstash-config.yaml
apiVersion: v1
kind: ConfigMap
metadata:
  name: logstash-config
data:
  logstash.conf: |
    input {
      beats {
        port => 5044
      }
      syslog {
        port => 514
      }
      http {
        port => 8080
        codec => json
      }
    }
    
    filter {
      if [fields][log_type] == "audit" {
        mutate {
          add_tag => ["audit"]
        }
        
        # Parse audit logs
        grok {
          match => { "message" => "%{TIMESTAMP_ISO8601:timestamp} %{LOGLEVEL:level} %{GREEDYDATA:audit_message}" }
        }
        
        # Extract user information
        if [audit_message] =~ /user=/ {
          grok {
            match => { "audit_message" => "user=%{USERNAME:audit_user}" }
          }
        }
        
        # Extract action information
        if [audit_message] =~ /action=/ {
          grok {
            match => { "audit_message" => "action=%{WORD:audit_action}" }
          }
        }
        
        # Add compliance tags
        if [audit_action] in ["login", "logout", "access", "modify", "delete"] {
          mutate {
            add_tag => ["privacy_act"]
          }
        }
      }
      
      # Government data classification
      if [message] =~ /PROTECTED|SECRET|CONFIDENTIAL/ {
        mutate {
          add_tag => ["classified"]
        }
      }
      
      # Add geolocation for IP addresses
      if [client_ip] {
        geoip {
          source => "client_ip"
          target => "geoip"
        }
      }
      
      # Enrich with threat intelligence
      if [client_ip] {
        translate {
          source => "client_ip"
          target => "threat_intel"
          dictionary_path => "/etc/logstash/threat_intel.yml"
          fallback => "clean"
        }
      }
    }
    
    output {
      elasticsearch {
        hosts => ["elasticsearch:9200"]
        index => "audit-logs-%{+YYYY.MM.dd}"
        template_name => "audit-template"
        template => "/etc/logstash/audit-template.json"
        template_overwrite => true
      }
      
      # Send critical alerts to SIEM
      if "critical" in [tags] or "classified" in [tags] {
        http {
          url => "https://siem.gov.au/api/alerts"
          http_method => "post"
          format => "json"
          headers => {
            "Authorization" => "Bearer ${SIEM_API_TOKEN}"
          }
        }
      }
      
      # Backup to long-term storage
      s3 {
        access_key_id => "${AWS_ACCESS_KEY}"
        secret_access_key => "${AWS_SECRET_KEY}"
        region => "ap-southeast-2"
        bucket => "audit-logs-backup"
        prefix => "logs/%{+YYYY/MM/dd}/"
        time_file => 60
      }
    }

Step 34: Implement Compliance Reporting

Create automated compliance reports:

Compliance Reporting Service:

# compliance_reporter.py
import pandas as pd
from elasticsearch import Elasticsearch
from datetime import datetime, timedelta
import jinja2
import smtplib
from email.mime.multipart import MIMEMultipart
from email.mime.text import MIMEText
from email.mime.application import MIMEApplication

class ComplianceReporter:
    def __init__(self, es_host, es_port=9200):
        self.es = Elasticsearch([{'host': es_host, 'port': es_port}])
        self.template_env = jinja2.Environment(
            loader=jinja2.FileSystemLoader('templates/')
        )
    
    def generate_privacy_act_report(self, start_date, end_date):
        """Generate Privacy Act compliance report"""
        query = {
            "query": {
                "bool": {
                    "must": [
                        {"range": {"@timestamp": {"gte": start_date, "lte": end_date}}},
                        {"terms": {"tags": ["privacy_act"]}}
                    ]
                }
            },
            "aggs": {
                "by_action": {
                    "terms": {"field": "audit_action.keyword"}
                },
                "by_user": {
                    "terms": {"field": "audit_user.keyword", "size": 100}
                },
                "by_classification": {
                    "terms": {"field": "data_classification.keyword"}
                }
            }
        }
        
        result = self.es.search(index="audit-logs-*", body=query)
        
        report_data = {
            'report_period': f"{start_date} to {end_date}",
            'total_events': result['hits']['total']['value'],
            'actions': result['aggregations']['by_action']['buckets'],
            'users': result['aggregations']['by_user']['buckets'],
            'classifications': result['aggregations']['by_classification']['buckets'],
            'generated_at': datetime.now().isoformat()
        }
        
        return report_data
    
    def generate_security_report(self, start_date, end_date):
        """Generate security incident report"""
        query = {
            "query": {
                "bool": {
                    "must": [
                        {"range": {"@timestamp": {"gte": start_date, "lte": end_date}}},
                        {"terms": {"level": ["ERROR", "CRITICAL", "ALERT"]}}
                    ]
                }
            },
            "aggs": {
                "by_severity": {
                    "terms": {"field": "level.keyword"}
                },
                "by_source": {
                    "terms": {"field": "source.keyword"}
                },
                "security_events": {
                    "filter": {
                        "terms": {"tags": ["security", "authentication", "authorization"]}
                    },
                    "aggs": {
                        "by_event_type": {
                            "terms": {"field": "event_type.keyword"}
                        }
                    }
                }
            }
        }
        
        result = self.es.search(index="audit-logs-*", body=query)
        
        report_data = {
            'report_period': f"{start_date} to {end_date}",
            'total_incidents': result['hits']['total']['value'],
            'by_severity': result['aggregations']['by_severity']['buckets'],
            'by_source': result['aggregations']['by_source']['buckets'],
            'security_events': result['aggregations']['security_events']['by_event_type']['buckets'],
            'generated_at': datetime.now().isoformat()
        }
        
        return report_data
    
    def generate_html_report(self, report_data, template_name):
        """Generate HTML report from template"""
        template = self.template_env.get_template(template_name)
        return template.render(report_data)
    
    def send_report(self, report_html, recipients, subject):
        """Send report via email"""
        msg = MIMEMultipart()
        msg['From'] = 'compliance@gov.au'
        msg['To'] = ', '.join(recipients)
        msg['Subject'] = subject
        
        msg.attach(MIMEText(report_html, 'html'))
        
        # Add CSV attachment
        csv_data = self.generate_csv_report(report_data)
        csv_attachment = MIMEApplication(csv_data)
        csv_attachment.add_header('Content-Disposition', 'attachment', filename='compliance_report.csv')
        msg.attach(csv_attachment)
        
        # Send email
        with smtplib.SMTP('smtp.gov.au', 587) as server:
            server.starttls()
            server.login('compliance@gov.au', 'password')
            server.send_message(msg)

# Automated report generation
if __name__ == "__main__":
    reporter = ComplianceReporter('elasticsearch.gov.au')
    
    # Generate weekly reports
    end_date = datetime.now()
    start_date = end_date - timedelta(days=7)
    
    # Privacy Act compliance report
    privacy_report = reporter.generate_privacy_act_report(
        start_date.isoformat(),
        end_date.isoformat()
    )
    
    privacy_html = reporter.generate_html_report(privacy_report, 'privacy_report.html')
    reporter.send_report(
        privacy_html,
        ['privacy.officer@gov.au', 'cio@gov.au'],
        'Weekly Privacy Act Compliance Report'
    )
    
    # Security incident report
    security_report = reporter.generate_security_report(
        start_date.isoformat(),
        end_date.isoformat()
    )
    
    security_html = reporter.generate_html_report(security_report, 'security_report.html')
    reporter.send_report(
        security_html,
        ['security.officer@gov.au', 'ciso@gov.au'],
        'Weekly Security Incident Report'
    )

8. Monitoring and Operations

8.1 Infrastructure Monitoring

Step 35: Deploy Prometheus and Grafana

Set up comprehensive monitoring stack:

Prometheus Configuration:

# prometheus-config.yaml
apiVersion: v1
kind: ConfigMap
metadata:
  name: prometheus-config
data:
  prometheus.yml: |
    global:
      scrape_interval: 15s
      evaluation_interval: 15s
    
    rule_files:
      - "alert_rules.yml"
    
    alerting:
      alertmanagers:
        - static_configs:
            - targets:
              - alertmanager:9093
    
    scrape_configs:
      - job_name: 'prometheus'
        static_configs:
          - targets: ['localhost:9090']
      
      - job_name: 'kubernetes-pods'
        kubernetes_sd_configs:
          - role: pod
        relabel_configs:
          - source_labels: [__meta_kubernetes_pod_annotation_prometheus_io_scrape]
            action: keep
            regex: true
          - source_labels: [__meta_kubernetes_pod_annotation_prometheus_io_path]
            action: replace
            target_label: __metrics_path__
            regex: (.+)
          - source_labels: [__address__, __meta_kubernetes_pod_annotation_prometheus_io_port]
            action: replace
            regex: ([^:]+)(?::\d+)?;(\d+)
            replacement: $1:$2
            target_label: __address__
      
      - job_name: 'kubernetes-nodes'
        kubernetes_sd_configs:
          - role: node
        relabel_configs:
          - action: labelmap
            regex: __meta_kubernetes_node_label_(.+)
      
      - job_name: 'gpu-metrics'
        static_configs:
          - targets: ['nvidia-dcgm-exporter:9400']
      
      - job_name: 'minio-metrics'
        static_configs:
          - targets: ['minio:9000']
        metrics_path: /minio/v2/metrics/cluster
      
      - job_name: 'postgres-metrics'
        static_configs:
          - targets: ['postgres-exporter:9187']
  
  alert_rules.yml: |
    groups:
    - name: infrastructure
      rules:
      - alert: HighCPUUsage
        expr: 100 - (avg by(instance) (irate(node_cpu_seconds_total{mode="idle"}[5m])) * 100) > 80
        for: 5m
        labels:
          severity: warning
        annotations:
          summary: "High CPU usage detected"
          description: "CPU usage is above 80% for instance {{ $labels.instance }}"
      
      - alert: HighMemoryUsage
        expr: (node_memory_MemTotal_bytes - node_memory_MemAvailable_bytes) / node_memory_MemTotal_bytes * 100 > 90
        for: 5m
        labels:
          severity: critical
        annotations:
          summary: "High memory usage detected"
          description: "Memory usage is above 90% for instance {{ $labels.instance }}"
      
      - alert: DiskSpaceLow
        expr: (node_filesystem_avail_bytes{mountpoint="/"} / node_filesystem_size_bytes{mountpoint="/"}) * 100 < 10
        for: 5m
        labels:
          severity: critical
        annotations:
          summary: "Disk space is running low"
          description: "Disk space is below 10% for instance {{ $labels.instance }}"
      
      - alert: PodCrashLooping
        expr: rate(kube_pod_container_status_restarts_total[15m]) > 0
        for: 5m
        labels:
          severity: warning
        annotations:
          summary: "Pod is crash looping"
          description: "Pod {{ $labels.pod }} in namespace {{ $labels.namespace }} is crash looping"
      
      - alert: GPUTemperatureHigh
        expr: DCGM_FI_DEV_GPU_TEMP > 80
        for: 5m
        labels:
          severity: warning
        annotations:
          summary: "GPU temperature is high"
          description: "GPU {{ $labels.gpu }} temperature is above 80°C"

Grafana Dashboard Configuration:

{
  "dashboard": {
    "id": null,
    "title": "Sovereign AI Cloud Overview",
    "tags": ["kubernetes", "ai", "government"],
    "timezone": "Australia/Sydney",
    "panels": [
      {
        "id": 1,
        "title": "Cluster Resource Usage",
        "type": "stat",
        "targets": [
          {
            "expr": "sum(kube_node_status_capacity{resource=\"cpu\"})",
            "legendFormat": "Total CPU Cores"
          },
          {
            "expr": "sum(kube_node_status_capacity{resource=\"memory\"}) / 1024 / 1024 / 1024",
            "legendFormat": "Total Memory (GB)"
          }
        ],
        "gridPos": {"h": 8, "w": 12, "x": 0, "y": 0}
      },
      {
        "id": 2,
        "title": "AI Workload Performance",
        "type": "graph",
        "targets": [
          {
            "expr": "rate(container_cpu_usage_seconds_total{namespace=\"kubeflow\"}[5m])",
            "legendFormat": "CPU Usage - {{pod}}"
          },
          {
            "expr": "container_memory_usage_bytes{namespace=\"kubeflow\"} / 1024 / 1024",
            "legendFormat": "Memory Usage (MB) - {{pod}}"
          }
        ],
        "gridPos": {"h": 8, "w": 12, "x": 12, "y": 0}
      },
      {
        "id": 3,
        "title": "GPU Utilization",
        "type": "graph",
        "targets": [
          {
            "expr": "DCGM_FI_DEV_GPU_UTIL",
            "legendFormat": "GPU {{gpu}} Utilization %"
          }
        ],
        "gridPos": {"h": 8, "w": 24, "x": 0, "y": 8}
      },
      {
        "id": 4,
        "title": "Data Storage Usage",
        "type": "graph",
        "targets": [
          {
            "expr": "minio_cluster_usage_total_bytes / 1024 / 1024 / 1024",
            "legendFormat": "MinIO Storage Used (GB)"
          },
          {
            "expr": "pg_stat_database_size{datname=\"postgres\"} / 1024 / 1024",
            "legendFormat": "PostgreSQL Database Size (MB)"
          }
        ],
        "gridPos": {"h": 8, "w": 12, "x": 0, "y": 16}
      },
      {
        "id": 5,
        "title": "Network Traffic",
        "type": "graph",
        "targets": [
          {
            "expr": "rate(container_network_receive_bytes_total[5m]) / 1024 / 1024",
            "legendFormat": "Network In (MB/s) - {{pod}}"
          },
          {
            "expr": "rate(container_network_transmit_bytes_total[5m]) / 1024 / 1024",
            "legendFormat": "Network Out (MB/s) - {{pod}}"
          }
        ],
        "gridPos": {"h": 8, "w": 12, "x": 12, "y": 16}
      }
    ],
    "time": {
      "from": "now-1h",
      "to": "now"
    },
    "refresh": "5s"
  }
}

Step 36: Configure Application Performance Monitoring

Deploy APM for AI application monitoring:

APM Configuration with Elastic APM:

# apm-server.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: apm-server
spec:
  replicas: 2
  selector:
    matchLabels:
      app: apm-server
  template:
    metadata:
      labels:
        app: apm-server
    spec:
      containers:
      - name: apm-server
        image: docker.elastic.co/apm/apm-server:8.5.0
        ports:
        - containerPort: 8200
        env:
        - name: output.elasticsearch.hosts
          value: "elasticsearch:9200"
        - name: apm-server.host
          value: "0.0.0.0:8200"
        - name: apm-server.secret_token
          value: "your-secret-token"
        volumeMounts:
        - name: config-volume
          mountPath: /usr/share/apm-server/apm-server.yml
          subPath: apm-server.yml
      volumes:
      - name: config-volume
        configMap:
          name: apm-server-config
---
apiVersion: v1
kind: ConfigMap
metadata:
  name: apm-server-config
data:
  apm-server.yml: |
    apm-server:
      host: "0.0.0.0:8200"
      secret_token: "your-secret-token"
      
    output.elasticsearch:
      hosts: ["elasticsearch:9200"]
      
    setup.kibana:
      host: "kibana:5601"
      
    logging.level: info
    logging.to_files: true
    logging.files:
      path: /var/log/apm-server
      name: apm-server
      keepfiles: 7
      permissions: 0644

AI Application Instrumentation:

# ml_model_monitoring.py
from elasticapm import Client
import time
import logging
import numpy as np
from sklearn.metrics import accuracy_score, precision_score, recall_score

class MLModelMonitor:
    def __init__(self, service_name, apm_server_url, secret_token):
        self.apm_client = Client({
            'SERVICE_NAME': service_name,
            'SERVER_URL': apm_server_url,
            'SECRET_TOKEN': secret_token,
            'ENVIRONMENT': 'production'
        })
        
    def monitor_prediction(self, model_name, input_data, prediction, actual=None):
        """Monitor model prediction performance"""
        with self.amp_client.capture_span(
            name=f"ml_prediction_{model_name}",
            span_type="ml.prediction"
        ) as span:
            start_time = time.time()
            
            # Add custom labels for monitoring
            span.label('model_name', model_name)
            span.label('input_size', len(str(input_data)))
            span.label('prediction_value', str(prediction))
            
            # Calculate inference time
            inference_time = time.time() - start_time
            span.label('inference_time_ms', inference_time * 1000)
            
            # Monitor prediction quality if actual value is available
            if actual is not None:
                accuracy = 1 if prediction == actual else 0
                span.label('prediction_accuracy', accuracy)
                
                # Log prediction quality metrics
                self.apm_client.capture_message(
                    message=f"Model {model_name} prediction accuracy: {accuracy}",
                    level="info",
                    custom={
                        'model_name': model_name,
                        'prediction': prediction,
                        'actual': actual,
                        'inference_time': inference_time
                    }
                )
            
            return prediction
    
    def monitor_batch_predictions(self, model_name, predictions, actuals):
        """Monitor batch prediction performance metrics"""
        try:
            # Calculate comprehensive metrics
            accuracy = accuracy_score(actuals, predictions)
            precision = precision_score(actuals, predictions, average='weighted')
            recall = recall_score(actuals, predictions, average='weighted')
            
            # Send metrics to APM
            self.apm_client.capture_message(
                message=f"Batch prediction metrics for {model_name}",
                level="info",
                custom={
                    'model_name': model_name,
                    'batch_size': len(predictions),
                    'accuracy': accuracy,
                    'precision': precision,
                    'recall': recall,
                    'timestamp': time.time()
                }
            )
            
            # Alert if performance degrades
            if accuracy < 0.8:  # Threshold for acceptable accuracy
                self.apm_client.capture_message(
                    message=f"Model {model_name} accuracy below threshold: {accuracy}",
                    level="warning",
                    custom={
                        'model_name': model_name,
                        'accuracy': accuracy,
                        'threshold': 0.8
                    }
                )
                
        except Exception as e:
            self.apm_client.capture_exception()
            logging.error(f"Error monitoring batch predictions: {str(e)}")

# Example usage in ML application
class GovernmentAIService:
    def __init__(self):
        self.monitor = MLModelMonitor(
            service_name="government-ai-service",
            apm_server_url="http://apm-server:8200",
            secret_token="your-secret-token"
        )
        
    def predict_citizen_service_category(self, inquiry_text):
        """Predict the appropriate government service category for citizen inquiry"""
        # This would be your actual ML model prediction logic
        prediction = self.ml_model.predict(inquiry_text)
        
        # Monitor the prediction
        monitored_prediction = self.monitor.monitor_prediction(
            model_name="service_categorization_model",
            input_data=inquiry_text,
            prediction=prediction
        )
        
        return monitored_prediction

8.2 Security Monitoring

Step 37: Deploy Security Information and Event Management (SIEM)

Understanding SIEM implementation is crucial for maintaining security oversight in your sovereign AI cloud. Think of SIEM as the central nervous system of your security infrastructure - it collects, correlates, and analyzes security events from across your entire environment to detect threats and ensure compliance.

The key concept here is that modern cyber threats are sophisticated and often involve multiple attack vectors across different systems. A SIEM solution provides the comprehensive visibility needed to connect these dots and identify patterns that might indicate a security incident.

SIEM Configuration with Wazuh:

# wazuh-manager.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: wazuh-manager
  namespace: security
spec:
  replicas: 1
  selector:
    matchLabels:
      app: wazuh-manager
  template:
    metadata:
      labels:
        app: wazuh-manager
    spec:
      containers:
      - name: wazuh-manager
        image: wazuh/wazuh-manager:4.4.0
        ports:
        - containerPort: 1514  # Agent communication
        - containerPort: 1515  # Agent enrollment
        - containerPort: 514   # Syslog
        - containerPort: 55000 # API
        env:
        - name: WAZUH_MANAGER_IP
          valueFrom:
            fieldRef:
              fieldPath: status.podIP
        volumeMounts:
        - name: wazuh-config
          mountPath: /wazuh-config-mount/etc/ossec.conf
          subPath: ossec.conf
        - name: wazuh-rules
          mountPath: /wazuh-config-mount/etc/rules/
        - name: wazuh-data
          mountPath: /var/ossec/data
        resources:
          requests:
            memory: "1Gi"
            cpu: "500m"
          limits:
            memory: "2Gi"
            cpu: "1000m"
      volumes:
      - name: wazuh-config
        configMap:
          name: wazuh-config
      - name: wazuh-rules
        configMap:
          name: wazuh-rules
      - name: wazuh-data
        persistentVolumeClaim:
          claimName: wazuh-data-pvc

Let me explain the security monitoring approach step by step. First, we need to understand that security monitoring in a government environment requires multiple layers of detection.

The Wazuh SIEM acts as our central collection point, but it needs specific rules tailored to government security requirements.

Custom Security Rules for Government Environment:

<!-- government_security_rules.xml -->
<group name="government_compliance,">
  <!-- Privacy Act Violations -->
  <rule id="100001" level="12">
    <if_group>web,</if_group>
    <match>personal_information|medicare|tax_file_number|drivers_license</match>
    <description>Potential Privacy Act violation - Personal information accessed</description>
    <group>privacy_act,compliance,</group>
  </rule>

  <!-- Classified Information Access -->
  <rule id="100002" level="15">
    <if_group>authentication,</if_group>
    <match>PROTECTED|SECRET|CONFIDENTIAL</match>
    <description>Access to classified information detected</description>
    <group>classification,security_clearance,</group>
  </rule>

  <!-- Unusual Data Access Patterns -->
  <rule id="100003" level="10" frequency="10" timeframe="300">
    <if_matched_sid>100001</if_matched_sid>
    <description>Multiple privacy-sensitive data access attempts in short timeframe</description>
    <group>privacy_act,suspicious_activity,</group>
  </rule>

  <!-- Failed Security Clearance Authentication -->
  <rule id="100004" level="8" frequency="3" timeframe="180">
    <if_group>authentication_failed,</if_group>
    <match>security_clearance_required</match>
    <description>Multiple failed attempts to access security clearance required resources</description>
    <group>authentication,security_clearance,</group>
  </rule>

  <!-- Data Exfiltration Indicators -->
  <rule id="100005" level="12">
    <if_group>network,</if_group>
    <match>large_data_transfer|bulk_download|export</match>
    <field name="data_size">^[5-9][0-9]{7,}|[1-9][0-9]{8,}</field> <!-- >50MB -->
    <description>Large data transfer detected - potential data exfiltration</description>
    <group>data_exfiltration,dLP,</group>
  </rule>

  <!-- AI Model Access Monitoring -->
  <rule id="100006" level="8">
    <if_group>ai_model,</if_group>
    <match>model_download|weights_access|training_data_access</match>
    <description>AI model or training data access detected</description>
    <group>ai_security,intellectual_property,</group>
  </rule>
</group>

The beauty of this rule configuration lies in its ability to detect patterns specific to government operations.

Notice how we're not just looking for generic security events, but for activities that could indicate violations of Australian privacy laws or unauthorised access to classified information.

Step 38: Implement Threat Intelligence Integration

Threat intelligence integration transforms your security monitoring from reactive to proactive.

Instead of only detecting known attacks, you're now equipped to identify emerging threats and attack patterns that might target government infrastructure specifically.

Threat Intelligence Feed Integration:

# threat_intelligence.py
import requests
import json
import time
from datetime import datetime, timedelta
import hashlib
import logging
from typing import Dict, List, Optional

class ThreatIntelligenceManager:
    """
    Manages threat intelligence feeds and integrates them with security monitoring.
    This class demonstrates how to consume threat intelligence and apply it to
    your security monitoring pipeline.
    """
    
    def __init__(self, feeds_config: Dict, wazuh_api_url: str, api_key: str):
        self.feeds_config = feeds_config
        self.wazuh_api_url = wazuh_api_url
        self.api_key = api_key
        self.threat_indicators = {}
        
        # Initialize logging for threat intelligence activities
        logging.basicConfig(level=logging.INFO)
        self.logger = logging.getLogger(__name__)
    
    def fetch_government_threat_feeds(self):
        """
        Fetch threat intelligence specifically relevant to government infrastructure.
        This includes APT groups known to target government, nation-state indicators,
        and government-specific vulnerabilities.
        """
        try:
            # Australian Cyber Security Centre (ACSC) threat feed
            acsc_feed = self._fetch_acsc_indicators()
            
            # US-CERT government indicators
            uscert_feed = self._fetch_uscert_indicators()
            
            # Commercial government-focused threat intel
            commercial_feed = self._fetch_commercial_indicators()
            
            # Combine and deduplicate indicators
            all_indicators = {
                **acsc_feed,
                **uscert_feed, 
                **commercial_feed
            }
            
            self.threat_indicators = all_indicators
            self.logger.info(f"Updated threat intelligence with {len(all_indicators)} indicators")
            
            return all_indicators
            
        except Exception as e:
            self.logger.error(f"Error fetching threat intelligence: {str(e)}")
            return {}
    
    def _fetch_acsc_indicators(self) -> Dict:
        """Fetch indicators from Australian Cyber Security Centre"""
        # Note: This would integrate with actual ACSC feeds when available
        # For now, we simulate the structure
        indicators = {}
        
        try:
            # Government-specific APT indicators
            apt_indicators = {
                "apt1_government": {
                    "ips": ["192.168.100.1", "10.0.50.25"],
                    "domains": ["govt-fake-portal.com", "tax-office-fake.org"],
                    "hashes": ["d41d8cd98f00b204e9800998ecf8427e"],
                    "tactics": ["credential_harvesting", "data_exfiltration"],
                    "severity": "high",
                    "description": "APT group targeting Australian government agencies"
                }
            }
            
            indicators.update(apt_indicators)
            
            # Government service impersonation indicators
            impersonation_indicators = {
                "govt_impersonation": {
                    "domains": ["fake-centrelink.com", "fraudulent-ato.org"],
                    "keywords": ["urgent tax notice", "government benefit suspended"],
                    "severity": "medium",
                    "description": "Domains impersonating Australian government services"
                }
            }
            
            indicators.update(impersonation_indicators)
            
        except Exception as e:
            self.logger.error(f"Error fetching ACSC indicators: {str(e)}")
        
        return indicators
    
    def _fetch_uscert_indicators(self) -> Dict:
        """Fetch US-CERT indicators relevant to government infrastructure"""
        indicators = {}
        
        try:
            # Example structure for US-CERT integration
            # In production, this would connect to actual US-CERT STIX/TAXII feeds
            uscert_indicators = {
                "nation_state_apt": {
                    "ips": ["203.0.113.5", "198.51.100.10"],
                    "user_agents": ["GovBot/1.0", "OfficialCrawler/2.1"],
                    "techniques": ["T1566.001", "T1078.004"],  # MITRE ATT&CK techniques
                    "severity": "critical",
                    "description": "Nation state actors targeting government infrastructure"
                }
            }
            
            indicators.update(uscert_indicators)
            
        except Exception as e:
            self.logger.error(f"Error fetching US-CERT indicators: {str(e)}")
        
        return indicators
    
    def _fetch_commercial_indicators(self) -> Dict:
        """Fetch commercial threat intelligence focused on government targets"""
        indicators = {}
        
        try:
            # This would integrate with commercial threat intel providers
            # like Recorded Future, ThreatConnect, etc.
            commercial_indicators = {
                "government_targeted_malware": {
                    "file_hashes": ["e3b0c44298fc1c149afbf4c8996fb924"],
                    "registry_keys": ["HKLM\\Software\\GovMalware"],
                    "network_signatures": ["POST /api/exfiltrate"],
                    "severity": "high",
                    "description": "Malware specifically designed to target government networks"
                }
            }
            
            indicators.update(commercial_indicators)
            
        except Exception as e:
            self.logger.error(f"Error fetching commercial indicators: {str(e)}")
        
        return indicators
    
    def update_wazuh_rules(self, indicators: Dict):
        """
        Update Wazuh rules with new threat intelligence indicators.
        This creates dynamic rules based on current threat intelligence.
        """
        try:
            for threat_name, threat_data in indicators.items():
                # Create IP-based rules
                if 'ips' in threat_data:
                    self._create_ip_rules(threat_name, threat_data['ips'], threat_data.get('severity', 'medium'))
                
                # Create domain-based rules  
                if 'domains' in threat_data:
                    self._create_domain_rules(threat_name, threat_data['domains'], threat_data.get('severity', 'medium'))
                
                # Create hash-based rules
                if 'hashes' in threat_data or 'file_hashes' in threat_data:
                    hashes = threat_data.get('hashes', threat_data.get('file_hashes', []))
                    self._create_hash_rules(threat_name, hashes, threat_data.get('severity', 'medium'))
                
                self.logger.info(f"Updated Wazuh rules for threat: {threat_name}")
                
        except Exception as e:
            self.logger.error(f"Error updating Wazuh rules: {str(e)}")
    
    def _create_ip_rules(self, threat_name: str, ips: List[str], severity: str):
        """Create Wazuh rules for malicious IP addresses"""
        severity_level = {'low': 5, 'medium': 8, 'high': 12, 'critical': 15}.get(severity, 8)
        
        rule_xml = f"""
        <rule id="{self._generate_rule_id()}" level="{severity_level}">
            <if_group>network,</if_group>
            <srcip>{"|".join(ips)}</srcip>
            <description>Connection from known threat IP - {threat_name}</description>
            <group>threat_intelligence,{threat_name},</group>
        </rule>
        """
        
        self._deploy_rule_to_wazuh(rule_xml)
    
    def _create_domain_rules(self, threat_name: str, domains: List[str], severity: str):
        """Create Wazuh rules for malicious domains"""
        severity_level = {'low': 5, 'medium': 8, 'high': 12, 'critical': 15}.get(severity, 8)
        
        rule_xml = f"""
        <rule id="{self._generate_rule_id()}" level="{severity_level}">
            <if_group>web,dns,</if_group>
            <match>{"|".join(domains)}</match>
            <description>Access to known malicious domain - {threat_name}</description>
            <group>threat_intelligence,{threat_name},dns,</group>
        </rule>
        """
        
        self._deploy_rule_to_wazuh(rule_xml)
    
    def _create_hash_rules(self, threat_name: str, hashes: List[str], severity: str):
        """Create Wazuh rules for malicious file hashes"""
        severity_level = {'low': 5, 'medium': 8, 'high': 12, 'critical': 15}.get(severity, 8)
        
        rule_xml = f"""
        <rule id="{self._generate_rule_id()}" level="{severity_level}">
            <if_group>syscheck,</if_group>
            <match>{"|".join(hashes)}</match>
            <description>Known malicious file detected - {threat_name}</description>
            <group>threat_intelligence,{threat_name},malware,</group>
        </rule>
        """
        
        self._deploy_rule_to_wazuh(rule_xml)
    
    def _generate_rule_id(self) -> str:
        """Generate unique rule ID for dynamic rules"""
        timestamp = str(int(time.time()))
        return f"200{timestamp[-6:]}"  # Use last 6 digits of timestamp
    
    def _deploy_rule_to_wazuh(self, rule_xml: str):
        """Deploy rule to Wazuh manager via API"""
        try:
            headers = {
                'Authorization': f'Bearer {self.api_key}',
                'Content-Type': 'application/xml'
            }
            
            response = requests.post(
                f"{self.wazuh_api_url}/rules",
                data=rule_xml,
                headers=headers
            )
            
            if response.status_code == 200:
                self.logger.info("Successfully deployed rule to Wazuh")
            else:
                self.logger.error(f"Failed to deploy rule: {response.status_code}")
                
        except Exception as e:
            self.logger.error(f"Error deploying rule to Wazuh: {str(e)}")

# Automated threat intelligence update service
class ThreatIntelligenceUpdater:
    """
    Service that automatically updates threat intelligence on a scheduled basis.
    This ensures your security monitoring stays current with emerging threats.
    """
    
    def __init__(self, ti_manager: ThreatIntelligenceManager):
        self.ti_manager = ti_manager
        self.update_interval = 3600  # Update every hour
        
    def start_automated_updates(self):
        """Start the automated threat intelligence update process"""
        self.logger.info("Starting automated threat intelligence updates")
        
        while True:
            try:
                # Fetch latest indicators
                indicators = self.ti_manager.fetch_government_threat_feeds()
                
                # Update security monitoring rules
                if indicators:
                    self.ti_manager.update_wazuh_rules(indicators)
                    
                # Wait for next update cycle
                time.sleep(self.update_interval)
                
            except Exception as e:
                self.logger.error(f"Error in automated update cycle: {str(e)}")
                time.sleep(300)  # Wait 5 minutes before retrying

This threat intelligence implementation is particularly powerful because it creates a feedback loop.

As new threats emerge targeting government infrastructure, your security monitoring automatically adapts to detect these threats.

The key insight here is that government environments face unique threat landscapes, and generic security monitoring isn't sufficient.

9. Disaster Recovery and Business Continuity

9.1 Backup Strategy Implementation

Understanding disaster recovery for a sovereign AI cloud requires thinking about multiple failure scenarios simultaneously.

Unlike commercial cloud environments where you might accept some data loss, government operations demand comprehensive protection with minimal tolerance for data loss or extended downtime.

Step 39: Implement Multi-Tier Backup Strategy

The concept of multi-tier backup strategy revolves around the understanding that different types of data have different recovery requirements.

Critical AI models and government data require immediate recovery capabilities, while historical training data might tolerate longer recovery times but needs long-term retention for compliance.

Comprehensive Backup Configuration:

# backup-strategy.yaml
apiVersion: v1
kind: ConfigMap
metadata:
  name: backup-policy
data:
  backup-config.yaml: |
    backup_tiers:
      tier_1_critical:
        description: "Critical AI models and active government data"
        rpo: 15m  # Recovery Point Objective - max 15 minutes data loss
        rto: 30m  # Recovery Time Objective - max 30 minutes downtime
        backup_frequency: "*/15 * * * *"  # Every 15 minutes
        retention_policy:
          daily: 30
          weekly: 12
          monthly: 24
          yearly: 7
        storage_locations:
          - local_ssd
          - remote_datacenter
          - offline_tape
        encryption: aes_256
        compression: enabled
        
      tier_2_important:
        description: "Training data and model artifacts"
        rpo: 4h
        rto: 2h
        backup_frequency: "0 */4 * * *"  # Every 4 hours
        retention_policy:
          daily: 14
          weekly: 8
          monthly: 12
          yearly: 5
        storage_locations:
          - remote_datacenter
          - cloud_storage
        encryption: aes_256
        compression: enabled
        
      tier_3_archival:
        description: "Historical logs and audit trails"
        rpo: 24h
        rto: 24h
        backup_frequency: "0 2 * * *"  # Daily at 2 AM
        retention_policy:
          weekly: 52
          monthly: 60
          yearly: 10
        storage_locations:
          - tape_archive
          - deep_storage
        encryption: aes_256
        compression: high
        
    backup_validation:
      test_frequency: weekly
      automated_recovery_tests: enabled
      integrity_checks: enabled
      compliance_verification: enabled

Let me walk you through implementing this backup strategy step by step, focusing on the reasoning behind each decision.

Automated Backup Implementation:

# backup_manager.py
import subprocess
import logging
import json
import yaml
from datetime import datetime, timedelta
from pathlib import Path
import boto3
import psycopg2
from kubernetes import client, config
import threading
import time

class SovereignBackupManager:
    """
    Comprehensive backup manager for sovereign AI cloud infrastructure.
    This class handles the complexities of backing up both structured data
    (databases) and unstructured data (files, models) while maintaining
    government compliance requirements.
    """
    
    def __init__(self, config_path: str):
        # Load backup configuration
        with open(config_path, 'r') as f:
            self.backup_config = yaml.safe_load(f)
        
        # Initialize logging
        logging.basicConfig(level=logging.INFO)
        self.logger = logging.getLogger(__name__)
        
        # Initialize Kubernetes client for container backups
        config.load_incluster_config()
        self.k8s_client = client.CoreV1Api()
        
        # Initialize storage clients
        self.s3_client = boto3.client('s3')
        
        # Backup status tracking
        self.backup_status = {}
        
    def execute_tier_1_backup(self):
        """
        Execute Tier 1 (critical) backups with 15-minute RPO.
        This includes active AI models, critical databases, and real-time government data.
        """
        try:
            self.logger.info("Starting Tier 1 critical backup")
            
            # Backup critical PostgreSQL databases
            self._backup_critical_databases()
            
            # Backup active AI models
            self._backup_active_ai_models()
            
            # Backup Kubernetes persistent volumes
            self._backup_kubernetes_volumes("tier-1")
            
            # Backup configuration and secrets
            self._backup_kubernetes_configs()
            
            # Replicate to secondary site
            self._replicate_to_secondary_site("tier-1")
            
            # Update backup status
            self.backup_status['tier_1'] = {
                'last_backup': datetime.now().isoformat(),
                'status': 'success',
                'next_backup': (datetime.now() + timedelta(minutes=15)).isoformat()
            }
            
            self.logger.info("Tier 1 backup completed successfully")
            
        except Exception as e:
            self.logger.error(f"Tier 1 backup failed: {str(e)}")
            self.backup_status['tier_1'] = {
                'last_backup': datetime.now().isoformat(),
                'status': 'failed',
                'error': str(e)
            }
            
            # Send alert for critical backup failure
            self._send_backup_alert("critical", f"Tier 1 backup failed: {str(e)}")
    
    def _backup_critical_databases(self):
        """
        Backup critical PostgreSQL databases with point-in-time recovery capability.
        This method demonstrates how to create consistent backups of databases
        that might be actively processing government transactions.
        """
        try:
            # Define critical databases that need immediate backup
            critical_databases = [
                'government_services',
                'citizen_data', 
                'ai_model_metadata',
                'audit_logs'
            ]
            
            for db_name in critical_databases:
                self.logger.info(f"Backing up critical database: {db_name}")
                
                # Create consistent snapshot using pg_dump
                backup_filename = f"{db_name}_{datetime.now().strftime('%Y%m%d_%H%M%S')}.sql"
                backup_path = f"/backups/tier1/databases/{backup_filename}"
                
                # Execute pg_dump with compression and consistency options
                pg_dump_cmd = [
                    'pg_dump',
                    '--host=postgres-primary',
                    '--port=5432',
                    '--username=backup_user',
                    '--verbose',
                    '--no-password',
                    '--format=custom',
                    '--compress=9',
                    '--no-owner',
                    '--no-privileges',
                    f'--file={backup_path}',
                    db_name
                ]
                
                result = subprocess.run(pg_dump_cmd, capture_output=True, text=True)
                
                if result.returncode == 0:
                    self.logger.info(f"Database {db_name} backed up successfully")
                    
                    # Encrypt the backup file
                    self._encrypt_backup_file(backup_path)
                    
                    # Copy to secondary locations
                    self._replicate_backup_file(backup_path, "tier-1")
                    
                else:
                    raise Exception(f"pg_dump failed for {db_name}: {result.stderr}")
                    
        except Exception as e:
            self.logger.error(f"Critical database backup failed: {str(e)}")
            raise
    
    def _backup_active_ai_models(self):
        """
        Backup active AI models including weights, configurations, and metadata.
        This is crucial for government AI services that need rapid recovery.
        """
        try:
            # Get list of active models from MLflow
            import mlflow
            
            mlflow.set_tracking_uri("http://mlflow-service:5000")
            client = mlflow.tracking.MlflowClient()
            
            # Get all registered models
            registered_models = client.list_registered_models()
            
            for model in registered_models:
                model_name = model.name
                self.logger.info(f"Backing up AI model: {model_name}")
                
                # Get latest version
                latest_version = client.get_latest_versions(model_name, stages=["Production"])[0]
                
                # Download model artifacts
                model_path = f"/backups/tier1/models/{model_name}_{latest_version.version}_{datetime.now().strftime('%Y%m%d_%H%M%S')}"
                
                # Create model backup directory
                Path(model_path).mkdir(parents=True, exist_ok=True)
                
                # Download model files
                mlflow.artifacts.download_artifacts(
                    artifact_uri=latest_version.source,
                    dst_path=model_path
                )
                
                # Create model metadata backup
                model_metadata = {
                    'name': model_name,
                    'version': latest_version.version,
                    'stage': latest_version.current_stage,
                    'description': latest_version.description,
                    'tags': latest_version.tags,
                    'creation_timestamp': latest_version.creation_timestamp,
                    'last_updated_timestamp': latest_version.last_updated_timestamp,
                    'backup_timestamp': datetime.now().isoformat()
                }
                
                with open(f"{model_path}/metadata.json", 'w') as f:
                    json.dump(model_metadata, f, indent=2)
                
                # Compress and encrypt model backup
                self._compress_and_encrypt_directory(model_path)
                
                self.logger.info(f"AI model {model_name} backed up successfully")
                
        except Exception as e:
            self.logger.error(f"AI model backup failed: {str(e)}")
            raise
    
    def _backup_kubernetes_volumes(self, tier: str):
        """
        Backup Kubernetes persistent volumes using volume snapshots.
        This ensures that container data is protected and can be rapidly restored.
        """
        try:
            # Get all PVCs in critical namespaces
            critical_namespaces = ['kubeflow', 'mlflow', 'default', 'security']
            
            for namespace in critical_namespaces:
                pvcs = self.k8s_client.list_namespaced_persistent_volume_claim(namespace)
                
                for pvc in pvcs.items:
                    pvc_name = pvc.metadata.name
                    self.logger.info(f"Creating snapshot for PVC: {pvc_name} in namespace: {namespace}")
                    
                    # Create volume snapshot
                    snapshot_name = f"{pvc_name}-{tier}-{datetime.now().strftime('%Y%m%d-%H%M%S')}"
                    
                    snapshot_manifest = {
                        'apiVersion': 'snapshot.storage.k8s.io/v1',
                        'kind': 'VolumeSnapshot',
                        'metadata': {
                            'name': snapshot_name,
                            'namespace': namespace
                        },
                        'spec': {
                            'source': {
                                'persistentVolumeClaimName': pvc_name
                            }
                        }
                    }
                    
                    # Apply snapshot using kubectl
                    import tempfile
                    with tempfile.NamedTemporaryFile(mode='w', suffix='.yaml', delete=False) as f:
                        yaml.dump(snapshot_manifest, f)
                        snapshot_file = f.name
                    
                    kubectl_cmd = ['kubectl', 'apply', '-f', snapshot_file]
                    result = subprocess.run(kubectl_cmd, capture_output=True, text=True)
                    
                    if result.returncode == 0:
                        self.logger.info(f"Volume snapshot {snapshot_name} created successfully")
                    else:
                        self.logger.error(f"Failed to create snapshot {snapshot_name}: {result.stderr}")
                    
                    # Clean up temp file
                    Path(snapshot_file).unlink()
                    
        except Exception as e:
            self.logger.error(f"Kubernetes volume backup failed: {str(e)}")
            raise
    
    def _encrypt_backup_file(self, file_path: str):
        """
        Encrypt backup files using AES-256 encryption.
        This ensures data protection even if backup media is compromised.
        """
        try:
            # Use gpg for encryption with government-approved algorithms
            encrypted_path = f"{file_path}.gpg"
            
            gpg_cmd = [
                'gpg',
                '--symmetric',
                '--cipher-algo', 'AES256',
                '--compress-algo', '2',
                '--s2k-mode', '3',
                '--s2k-digest-algo', 'SHA512',
                '--s2k-count', '65011712',
                '--force-mdc',
                '--quiet',
                '--batch',
                '--yes',
                '--passphrase-file', '/etc/backup/encryption-key',
                '--output', encrypted_path,
                file_path
            ]
            
            result = subprocess.run(gpg_cmd, capture_output=True, text=True)
            
            if result.returncode == 0:
                # Remove unencrypted file
                Path(file_path).unlink()
                self.logger.info(f"File encrypted successfully: {encrypted_path}")
            else:
                raise Exception(f"Encryption failed: {result.stderr}")
                
        except Exception as e:
            self.logger.error(f"File encryption failed: {str(e)}")
            raise

class DisasterRecoveryOrchestrator:
    """
    Orchestrates disaster recovery procedures including failover to secondary sites,
    data recovery, and service restoration. This class embodies the understanding
    that disaster recovery for government services requires coordinated, tested
    procedures that can be executed under pressure.
    """
    
    def __init__(self, dr_config_path: str):
        with open(dr_config_path, 'r') as f:
            self.dr_config = yaml.safe_load(f)
        
        logging.basicConfig(level=logging.INFO)
        self.logger = logging.getLogger(__name__)
        
        # Initialize recovery status tracking
        self.recovery_status = {
            'infrastructure': 'unknown',
            'data': 'unknown',
            'applications': 'unknown',
            'ai_models': 'unknown'
        }
    
    def execute_disaster_recovery(self, disaster_type: str, affected_components: list):
        """
        Execute comprehensive disaster recovery based on the type of disaster
        and affected components. This method demonstrates the decision tree
        approach needed for effective government disaster recovery.
        """
        try:
            self.logger.info(f"Initiating disaster recovery for: {disaster_type}")
            self.logger.info(f"Affected components: {affected_components}")
            
            # Step 1: Assess damage and determine recovery strategy
            recovery_plan = self._assess_and_plan_recovery(disaster_type, affected_components)
            
            # Step 2: Execute infrastructure recovery
            if 'infrastructure' in affected_components:
                self._recover_infrastructure(recovery_plan['infrastructure'])
            
            # Step 3: Execute data recovery
            if 'data' in affected_components:
                self._recover_data(recovery_plan['data'])
            
            # Step 4: Execute application recovery
            if 'applications' in affected_components:
                self._recover_applications(recovery_plan['applications'])
            
            # Step 5: Execute AI model recovery
            if 'ai_models' in affected_components:
                self._recover_ai_models(recovery_plan['ai_models'])
            
            # Step 6: Validate recovery and perform testing
            self._validate_recovery()
            
            # Step 7: Notify stakeholders of recovery completion
            self._notify_recovery_completion()
            
            self.logger.info("Disaster recovery completed successfully")
            
        except Exception as e:
            self.logger.error(f"Disaster recovery failed: {str(e)}")
            self._escalate_recovery_failure(str(e))
            raise
    
    def _assess_and_plan_recovery(self, disaster_type: str, affected_components: list) -> dict:
        """
        Assess the scope of disaster and create a recovery plan.
        This demonstrates the critical thinking required for government DR.
        """
        recovery_plan = {}
        
        # Determine recovery priorities based on government service criticality
        priority_matrix = {
            'citizen_services': 1,  # Highest priority
            'ai_models': 2,
            'data_processing': 3,
            'analytics': 4,
            'development': 5  # Lowest priority
        }
        
        # Infrastructure recovery planning
        if 'infrastructure' in affected_components:
            if disaster_type in ['datacenter_failure', 'hardware_failure']:
                recovery_plan['infrastructure'] = {
                    'strategy': 'failover_to_secondary',
                    'target_site': self.dr_config['secondary_sites']['primary'],
                    'estimated_time': '30 minutes',
                    'prerequisites': ['network_connectivity', 'secondary_site_available']
                }
            elif disaster_type == 'network_partition':
                recovery_plan['infrastructure'] = {
                    'strategy': 'restore_network_connectivity',
                    'target_site': 'current',
                    'estimated_time': '15 minutes',
                    'prerequisites': ['alternative_network_path']
                }
        
        # Data recovery planning
        if 'data' in affected_components:
            recovery_plan['data'] = {
                'strategy': 'restore_from_backup',
                'backup_tier': 'tier_1',  # Use most recent backups
                'estimated_time': '45 minutes',
                'data_loss_estimate': '15 minutes',  # Based on backup frequency
                'prerequisites': ['backup_integrity_verified', 'storage_available']
            }
        
        # Application recovery planning
        if 'applications' in affected_components:
            recovery_plan['applications'] = {
                'strategy': 'redeploy_from_registry',
                'deployment_order': self._determine_application_startup_order(),
                'estimated_time': '20 minutes',
                'prerequisites': ['infrastructure_recovered', 'container_registry_available']
            }
        
        # AI model recovery planning
        if 'ai_models' in affected_components:
            recovery_plan['ai_models'] = {
                'strategy': 'restore_from_model_backup',
                'model_priority': self._determine_model_recovery_priority(),
                'estimated_time': '30 minutes',
                'prerequisites': ['mlflow_available', 'model_artifacts_accessible']
            }
        
        return recovery_plan
    
    def _recover_infrastructure(self, infrastructure_plan: dict):
        """
        Execute infrastructure recovery procedures.
        This method shows how to orchestrate complex infrastructure failover.
        """
        try:
            self.logger.info("Starting infrastructure recovery")
            
            if infrastructure_plan['strategy'] == 'failover_to_secondary':
                # Verify secondary site readiness
                if not self._verify_secondary_site_readiness(infrastructure_plan['target_site']):
                    raise Exception("Secondary site not ready for failover")
                
                # Update DNS to point to secondary site
                self._update_dns_failover(infrastructure_plan['target_site'])
                
                # Start services on secondary site
                self._start_secondary_site_services(infrastructure_plan['target_site'])
                
                # Verify service availability
                if not self._verify_service_availability():
                    raise Exception("Service verification failed after failover")
                
                self.recovery_status['infrastructure'] = 'recovered'
                self.logger.info("Infrastructure failover completed successfully")
                
            elif infrastructure_plan['strategy'] == 'restore_network_connectivity':
                # Attempt to restore primary network paths
                self._restore_network_connectivity()
                
                # Verify connectivity
                if not self._verify_network_connectivity():
                    raise Exception("Network connectivity restoration failed")
                
                self.recovery_status['infrastructure'] = 'recovered'
                self.logger.info("Network connectivity restored successfully")
                
        except Exception as e:
            self.recovery_status['infrastructure'] = 'failed'
            self.logger.error(f"Infrastructure recovery failed: {str(e)}")
            raise
    
    def _recover_data(self, data_plan: dict):
        """
        Execute data recovery procedures with point-in-time recovery.
        This demonstrates handling of critical government data recovery.
        """
        try:
            self.logger.info("Starting data recovery")
            
            # Determine recovery point based on disaster timing
            recovery_point = self._determine_optimal_recovery_point(data_plan)
            
            # Stop any remaining database processes to ensure consistency
            self._stop_database_services()
            
            # Restore from backup
            if data_plan['backup_tier'] == 'tier_1':
                self._restore_tier_1_backups(recovery_point)
            elif data_plan['backup_tier'] == 'tier_2':
                self._restore_tier_2_backups(recovery_point)
            
            # Verify data integrity
            if not self._verify_data_integrity():
                raise Exception("Data integrity verification failed")
            
            # Restart database services
            self._start_database_services()
            
            # Perform data consistency checks
            if not self._verify_data_consistency():
                raise Exception("Data consistency verification failed")
            
            self.recovery_status['data'] = 'recovered'
            self.logger.info(f"Data recovery completed. Recovery point: {recovery_point}")
            
        except Exception as e:
            self.recovery_status['data'] = 'failed'
            self.logger.error(f"Data recovery failed: {str(e)}")
            raise
    
    def _restore_tier_1_backups(self, recovery_point: str):
        """
        Restore critical data from Tier 1 backups.
        This method handles the most critical government data recovery.
        """
        try:
            # Get list of databases to restore
            critical_databases = ['government_services', 'citizen_data', 'ai_model_metadata', 'audit_logs']
            
            for db_name in critical_databases:
                self.logger.info(f"Restoring database: {db_name}")
                
                # Find appropriate backup file
                backup_file = self._find_backup_file(db_name, recovery_point, 'tier_1')
                
                if not backup_file:
                    raise Exception(f"No suitable backup found for {db_name} at recovery point {recovery_point}")
                
                # Decrypt backup file
                decrypted_file = self._decrypt_backup_file(backup_file)
                
                # Drop existing database (if any)
                self._drop_database_if_exists(db_name)
                
                # Create new database
                self._create_database(db_name)
                
                # Restore from backup
                pg_restore_cmd = [
                    'pg_restore',
                    '--host=postgres-primary',
                    '--port=5432',
                    '--username=restore_user',
                    '--verbose',
                    '--no-password',
                    '--clean',
                    '--create',
                    f'--dbname={db_name}',
                    decrypted_file
                ]
                
                result = subprocess.run(pg_restore_cmd, capture_output=True, text=True)
                
                if result.returncode == 0:
                    self.logger.info(f"Database {db_name} restored successfully")
                else:
                    raise Exception(f"Database restore failed for {db_name}: {result.stderr}")
                
                # Clean up decrypted file
                Path(decrypted_file).unlink()
                
        except Exception as e:
            self.logger.error(f"Tier 1 backup restoration failed: {str(e)}")
            raise

# Automated DR testing service
class DisasterRecoveryTester:
    """
    Automated testing service for disaster recovery procedures.
    Regular DR testing is crucial for government environments to ensure
    recovery procedures work when actually needed.
    """
    
    def __init__(self, dr_orchestrator: DisasterRecoveryOrchestrator):
        self.dr_orchestrator = dr_orchestrator
        logging.basicConfig(level=logging.INFO)
        self.logger = logging.getLogger(__name__)
    
    def run_monthly_dr_test(self):
        """
        Execute comprehensive monthly disaster recovery test.
        This simulates various disaster scenarios without affecting production.
        """
        try:
            self.logger.info("Starting monthly disaster recovery test")
            
            # Test scenarios to execute
            test_scenarios = [
                {
                    'name': 'database_failure_simulation',
                    'description': 'Simulate critical database failure',
                    'affected_components': ['data'],
                    'expected_recovery_time': 45  # minutes
                },
                {
                    'name': 'infrastructure_failover_test',
                    'description': 'Test failover to secondary datacenter',
                    'affected_components': ['infrastructure'],
                    'expected_recovery_time': 30  # minutes
                },
                {
                    'name': 'ai_model_recovery_test',
                    'description': 'Test AI model restoration procedures',
                    'affected_components': ['ai_models'],
                    'expected_recovery_time': 30  # minutes
                }
            ]
            
            test_results = []
            
            for scenario in test_scenarios:
                self.logger.info(f"Executing test scenario: {scenario['name']}")
                
                # Record start time
                start_time = datetime.now()
                
                # Execute test in isolated environment
                test_result = self._execute_test_scenario(scenario)
                
                # Record end time and calculate duration
                end_time = datetime.now()
                duration_minutes = (end_time - start_time).total_seconds() / 60
                
                # Evaluate test results
                test_passed = (
                    test_result['success'] and 
                    duration_minutes <= scenario['expected_recovery_time']
                )
                
                test_results.append({
                    'scenario': scenario['name'],
                    'success': test_passed,
                    'duration_minutes': duration_minutes,
                    'expected_duration': scenario['expected_recovery_time'],
                    'details': test_result
                })
                
                self.logger.info(f"Test scenario {scenario['name']} completed: {'PASSED' if test_passed else 'FAILED'}")
            
            # Generate test report
            self._generate_dr_test_report(test_results)
            
            # Alert if any tests failed
            failed_tests = [t for t in test_results if not t['success']]
            if failed_tests:
                self._alert_dr_test_failures(failed_tests)
            
            self.logger.info("Monthly disaster recovery test completed")
            
        except Exception as e:
            self.logger.error(f"DR testing failed: {str(e)}")
            self._alert_dr_test_error(str(e))
    
    def _execute_test_scenario(self, scenario: dict) -> dict:
        """
        Execute individual DR test scenario in isolated environment.
        This method demonstrates safe DR testing without production impact.
        """
        try:
            # Create isolated test environment
            test_env = self._create_test_environment(scenario['name'])
            
            # Simulate disaster condition
            self._simulate_disaster(test_env, scenario['affected_components'])
            
            # Execute recovery procedures
            recovery_result = self.dr_orchestrator.execute_disaster_recovery(
                disaster_type='test_simulation',
                affected_components=scenario['affected_components']
            )
            
            # Validate recovery
            validation_result = self._validate_test_recovery(test_env, scenario)
            
            # Clean up test environment
            self._cleanup_test_environment(test_env)
            
            return {
                'success': validation_result['success'],
                'recovery_steps': recovery_result,
                'validation_details': validation_result
            }
            
        except Exception as e:
            self.logger.error(f"Test scenario execution failed: {str(e)}")
            return {
                'success': False,
                'error': str(e)
            }

10. Testing and Validation

10.1 Security Testing

Understanding security testing for a sovereign AI cloud requires recognising that government environments face unique threats.

Unlike commercial environments, government systems are high-value targets for nation-state actors, requiring comprehensive security validation that goes beyond standard penetration testing.

Step 40: Implement Comprehensive Security Testing Framework

The security testing framework I'm about to show you operates on the principle of "assumed breach" we assume that sophisticated attackers will eventually find a way into the system, so we need to test not just prevention, but detection and response capabilities as well.

# security_testing_framework.py
import subprocess
import json
import yaml
import requests
import threading
import time
from datetime import datetime, timedelta
import logging
from typing import Dict, List, Optional
import paramiko
import nmap
import sqlparse
from selenium import webdriver
from selenium.webdriver.chrome.options import Options

class GovernmentSecurityTester:
    """
    Comprehensive security testing framework specifically designed for
    government AI cloud environments. This class demonstrates how to
    perform security testing that addresses government-specific threats
    while maintaining operational security.
    """
    
    def __init__(self, test_config_path: str):
        with open(test_config_path, 'r') as f:
            self.test_config = yaml.safe_load(f)
        
        logging.basicConfig(level=logging.INFO)
        self.logger = logging.getLogger(__name__)
        
        # Initialize test results tracking
        self.test_results = {
            'infrastructure': [],
            'applications': [],
            'data_protection': [],
            'compliance': [],
            'ai_security': []
        }
    
    def execute_comprehensive_security_assessment(self):
        """
        Execute comprehensive security assessment covering all aspects
        of government AI cloud security requirements.
        """
        try:
            self.logger.info("Starting comprehensive security assessment")
            
            # Phase 1: Infrastructure Security Testing
            self._test_infrastructure_security()
            
            # Phase 2: Application Security Testing
            self._test_application_security()
            
            # Phase 3: Data Protection Testing
            self._test_data_protection()
            
            # Phase 4: Compliance Validation
            self._test_compliance_controls()
            
            # Phase 5: AI-Specific Security Testing
            self._test_ai_security()
            
            # Phase 6: Incident Response Testing
            self._test_incident_response()
            
            # Generate comprehensive report
            self._generate_security_assessment_report()
            
            self.logger.info("Comprehensive security assessment completed")
            
        except Exception as e:
            self.logger.error(f"Security assessment failed: {str(e)}")
            raise
    
    def _test_infrastructure_security(self):
        """
        Test infrastructure security including network segmentation,
        access controls, and system hardening.
        """
        try:
            self.logger.info("Testing infrastructure security")
            
            # Test network segmentation
            segmentation_results = self._test_network_segmentation()
            self.test_results['infrastructure'].extend(segmentation_results)
            
            # Test access controls
            access_control_results = self._test_access_controls()
            self.test_results['infrastructure'].extend(access_control_results)
            
            # Test system hardening
            hardening_results = self._test_system_hardening()
            self.test_results['infrastructure'].extend(hardening_results)
            
            # Test encryption implementation
            encryption_results = self._test_encryption_implementation()
            self.test_results['infrastructure'].extend(encryption_results)
            
        except Exception as e:
            self.logger.error(f"Infrastructure security testing failed: {str(e)}")
            raise
    
    def _test_network_segmentation(self) -> List[Dict]:
        """
        Test network segmentation to ensure proper isolation between
        different security zones (management, compute, data, external).
        """
        results = []
        
        try:
            # Define network zones and expected isolation
            network_zones = {
                'management': {'subnet': '10.1.0.0/16', 'allowed_outbound': ['dns', 'ntp']},
                'compute': {'subnet': '10.2.0.0/16', 'allowed_outbound': ['storage', 'api']},
                'storage': {'subnet': '10.3.0.0/16', 'allowed_outbound': ['backup']},
                'external': {'subnet': '10.4.0.0/16', 'allowed_outbound': ['internet']}
            }
            
            for zone_name, zone_config in network_zones.items():
                self.logger.info(f"Testing network segmentation for zone: {zone_name}")
                
                # Test unauthorized inter-zone communication
                unauthorized_access = self._test_unauthorized_network_access(zone_name, zone_config)
                
                results.append({
                    'test_name': f'network_segmentation_{zone_name}',
                    'category': 'infrastructure',
                    'severity': 'high',
                    'passed': not unauthorized_access['violations_found'],
                    'details': unauthorized_access,
                    'timestamp': datetime.now().isoformat()
                })
                
                # Test firewall rules effectiveness
                firewall_test = self._test_firewall_rules(zone_name, zone_config)
                
                results.append({
                    'test_name': f'firewall_rules_{zone_name}',
                    'category': 'infrastructure',
                    'severity': 'high',
                    'passed': firewall_test['rules_effective'],
                    'details': firewall_test,
                    'timestamp': datetime.now().isoformat()
                })
            
        except Exception as e:
            self.logger.error(f"Network segmentation testing failed: {str(e)}")
            results.append({
                'test_name': 'network_segmentation_test',
                'category': 'infrastructure',
                'severity': 'high',
                'passed': False,
                'error': str(e),
                'timestamp': datetime.now().isoformat()
            })
        
        return results
    
    def _test_unauthorized_network_access(self, zone_name: str, zone_config: Dict) -> Dict:
        """
        Test for unauthorized network access between security zones.
        This simulates lateral movement attempts by attackers.
        """
        try:
            violations = []
            
            # Use nmap to test connectivity from different zones
            nm = nmap.PortScanner()
            
            # Define prohibited connections based on security policy
            prohibited_connections = {
                'management': ['external', 'compute'],
                'compute': ['external'],
                'storage': ['external'],
                'external': ['management', 'storage']
            }
            
            if zone_name in prohibited_connections:
                for prohibited_zone in prohibited_connections[zone_name]:
                    # Attempt connection to prohibited zone
                    test_result = self._attempt_zone_connection(zone_name, prohibited_zone)
                    
                    if test_result['connection_successful']:
                        violations.append({
                            'source_zone': zone_name,
                            'target_zone': prohibited_zone,
                            'connection_type': test_result['connection_type'],
                            'risk_level': 'high'
                        })
            
            return {
                'violations_found': len(violations) > 0,
                'violation_count': len(violations),
                'violations': violations
            }
            
        except Exception as e:
            self.logger.error(f"Unauthorized network access test failed: {str(e)}")
            return {'violations_found': True, 'error': str(e)}
    
    def _test_ai_security(self):
        """
        Test AI-specific security concerns including model poisoning protection,
        adversarial input detection, and model extraction prevention.
        """
        try:
            self.logger.info("Testing AI-specific security measures")
            
            # Test model access controls
            model_access_results = self._test_model_access_controls()
            self.test_results['ai_security'].extend(model_access_results)
            
            # Test adversarial input detection
            adversarial_results = self._test_adversarial_input_detection()
            self.test_results['ai_security'].extend(adversarial_results)
            
            # Test model extraction prevention
            extraction_results = self._test_model_extraction_prevention()
            self.test_results['ai_security'].extend(extraction_results)
            
            # Test training data protection
            training_data_results = self._test_training_data_protection()
            self.test_results['ai_security'].extend(training_data_results)
            
        except Exception as e:
            self.logger.error(f"AI security testing failed: {str(e)}")
            raise
    
    def _test_adversarial_input_detection(self) -> List[Dict]:
        """
        Test the system's ability to detect and handle adversarial inputs
        designed to fool AI models. This is crucial for government AI systems
        that might be targeted by sophisticated attackers.
        """
        results = []
        
        try:
            # Define adversarial test cases
            adversarial_tests = [
                {
                    'name': 'text_adversarial_attack',
                    'description': 'Text inputs designed to bypass content filters',
                    'test_inputs': [
                        'Legitimate request with hidden malicious intent',
                        'Social engineering attempt disguised as help request',
                        'Prompt injection attempt to extract sensitive information'
                    ]
                },
                {
                    'name': 'image_adversarial_attack',
                    'description': 'Images with subtle perturbations to fool classifiers',
                    'test_inputs': [
                        'Modified government document images',
                        'Subtly altered identity documents',
                        'Adversarial patches on legitimate images'
                    ]
                }
            ]
            
            for test_case in adversarial_tests:
                self.logger.info(f"Testing adversarial detection: {test_case['name']}")
                
                detection_results = []
                
                for test_input in test_case['test_inputs']:
                    # Submit adversarial input to AI system
                    response = self._submit_ai_input(test_input, test_case['name'])
                    
                    # Check if adversarial input was detected
                    adversarial_detected = self._check_adversarial_detection(response)
                    
                    detection_results.append({
                        'input': test_input,
                        'detected': adversarial_detected,
                        'response': response
                    })
                
                # Calculate detection rate
                detection_rate = sum(1 for r in detection_results if r['detected']) / len(detection_results)
                
                results.append({
                    'test_name': f'adversarial_detection_{test_case["name"]}',
                    'category': 'ai_security',
                    'severity': 'high',
                    'passed': detection_rate >= 0.8,  # Require 80% detection rate
                    'detection_rate': detection_rate,
                    'details': detection_results,
                    'timestamp': datetime.now().isoformat()
                })
                
        except Exception as e:
            self.logger.error(f"Adversarial input detection testing failed: {str(e)}")
            results.append({
                'test_name': 'adversarial_input_detection',
                'category': 'ai_security',
                'severity': 'high',
                'passed': False,
                'error': str(e),
                'timestamp': datetime.now().isoformat()
            })
        
        return results

class ComplianceValidator:
    """
    Validates compliance with Australian government regulations including
    Privacy Act, Australian Government Information Security Manual (ISM),
    and other relevant standards.
    """
    
    def __init__(self, compliance_config_path: str):
        with open(compliance_config_path, 'r') as f:
            self.compliance_config = yaml.safe_load(f)
        
        logging.basicConfig(level=logging.INFO)
        self.logger = logging.getLogger(__name__)
    
    def validate_privacy_act_compliance(self) -> Dict:
        """
        Validate compliance with Australian Privacy Act requirements.
        This includes data handling, consent management, and access controls.
        """
        try:
            self.logger.info("Validating Privacy Act compliance")
            
            compliance_results = {
                'overall_compliant': True,
                'principle_results': {},
                'violations': [],
                'recommendations': []
            }
            
            # Australian Privacy Principles validation
            privacy_principles = [
                'open_and_transparent_management',
                'anonymity_and_pseudonymity',
                'collection_of_solicited_information',
                'dealing_with_unsolicited_information',
                'notification_of_collection',
                'use_or_disclosure',
                'direct_marketing',
                'cross_border_disclosure',
                'adoption_use_or_disclosure_of_government_identifiers',
                'quality_of_personal_information',
                'security_of_personal_information',
                'access_to_personal_information',
                'correction_of_personal_information'
            ]
            
            for principle in privacy_principles:
                principle_result = self._validate_privacy_principle(principle)
                compliance_results['principle_results'][principle] = principle_result
                
                if not principle_result['compliant']:
                    compliance_results['overall_compliant'] = False
                    compliance_results['violations'].extend(principle_result['violations'])
                
                compliance_results['recommendations'].extend(principle_result.get('recommendations', []))
            
            return compliance_results
            
        except Exception as e:
            self.logger.error(f"Privacy Act compliance validation failed: {str(e)}")
            return {
                'overall_compliant': False,
                'error': str(e)
            }
    
    def _validate_privacy_principle(self, principle: str) -> Dict:
        """
        Validate specific Australian Privacy Principle compliance.
        Each principle has specific technical requirements that must be verified.
        """
        try:
            if principle == 'security_of_personal_information':
                return self._validate_security_of_personal_information()
            elif principle == 'access_to_personal_information':
                return self._validate_access_to_personal_information()
            elif principle == 'cross_border_disclosure':
                return self._validate_cross_border_disclosure()
            elif principle == 'collection_of_solicited_information':
                return self._validate_collection_of_solicited_information()
            else:
                # Generic validation for other principles
                return self._validate_generic_principle(principle)
                
        except Exception as e:
            return {
                'compliant': False,
                'violations': [f"Validation error for {principle}: {str(e)}"],
                'recommendations': [f"Review implementation of {principle}"]
            }
    
    def _validate_security_of_personal_information(self) -> Dict:
        """
        Validate APP 11 - Security of personal information.
        This principle requires reasonable steps to protect personal information.
        """
        violations = []
        recommendations = []
        
        try:
            # Check encryption at rest
            encryption_check = self._check_data_encryption_at_rest()
            if not encryption_check['encrypted']:
                violations.append("Personal information not encrypted at rest")
                recommendations.append("Implement AES-256 encryption for all personal information storage")
            
            # Check encryption in transit
            transit_check = self._check_data_encryption_in_transit()
            if not transit_check['encrypted']:
                violations.append("Personal information not encrypted in transit")
                recommendations.append("Implement TLS 1.3 for all data transmission")
            
            # Check access controls
            access_check = self._check_personal_information_access_controls()
            if not access_check['adequate']:
                violations.append("Inadequate access controls for personal information")
                recommendations.append("Implement role-based access controls with principle of least privilege")
            
            # Check audit logging
            audit_check = self._check_personal_information_audit_logging()
            if not audit_check['comprehensive']:
                violations.append("Insufficient audit logging for personal information access")
                recommendations.append("Implement comprehensive audit logging for all personal information access")
            
            # Check data retention policies
            retention_check = self._check_data_retention_policies()
            if not retention_check['compliant']:
                violations.append("Data retention policies not properly implemented")
                recommendations.append("Implement automated data retention and deletion policies")
            
            return {
                'compliant': len(violations) == 0,
                'violations': violations,
                'recommendations': recommendations,
                'technical_details': {
                    'encryption_at_rest': encryption_check,
                    'encryption_in_transit': transit_check,
                    'access_controls': access_check,
                    'audit_logging': audit_check,
                    'data_retention': retention_check
                }
            }
            
        except Exception as e:
            return {
                'compliant': False,
                'violations': [f"Security validation error: {str(e)}"],
                'recommendations': ["Review security implementation"]
            }

## 11. Go-Live and Maintenance

### 11.1 Production Deployment

Understanding production deployment for a sovereign AI cloud requires recognizing that government services demand zero-downtime deployment strategies. Citizens and government agencies depend on these services, making traditional maintenance windows unacceptable for critical systems.

**Step 41: Implement Blue-Green Deployment Strategy**

The blue-green deployment strategy I'm about to demonstrate operates on the principle of maintaining two identical production environments. This approach is particularly valuable for government services because it allows for comprehensive testing in a production-like environment before switching traffic, ensuring service continuity.

```python
# production_deployment.py
import kubernetes
import boto3
import time
import logging
import yaml
import requests
from datetime import datetime, timedelta
from typing import Dict, List, Optional
import subprocess
import threading

class ProductionDeploymentManager:
    """
    Manages production deployments for sovereign AI cloud using blue-green
    deployment strategies. This class demonstrates how to achieve zero-downtime
    deployments while maintaining government service availability requirements.
    """
    
    def __init__(self, deployment_config_path: str):
        with open(deployment_config_path, 'r') as f:
            self.deployment_config = yaml.safe_load(f)
        
        # Initialize Kubernetes client
        kubernetes.config.load_incluster_config()
        self.k8s_apps_v1 = kubernetes.client.AppsV1Api()
        self.k8s_core_v1 = kubernetes.client.CoreV1Api()
        self.k8s_networking_v1 = kubernetes.client.NetworkingV1Api()
        
        # Initialize logging
        logging.basicConfig(level=logging.INFO)
        self.logger = logging.getLogger(__name__)
        
        # Track deployment state
        self.deployment_state = {
            'active_environment': 'blue',
            'deployment_in_progress': False,
            'last_deployment': None,
            'health_checks': {}
        }
    
    def execute_blue_green_deployment(self, new_version: str, service_manifest: Dict):
        """
        Execute blue-green deployment for government AI services.
        This method ensures zero-downtime deployment with comprehensive validation.
        """
        try:
            self.logger.info(f"Starting blue-green deployment for version: {new_version}")
            self.deployment_state['deployment_in_progress'] = True
            
            # Determine current and target environments
            current_env = self.deployment_state['active_environment']
            target_env = 'green' if current_env == 'blue' else 'blue'
            
            self.logger.info(f"Current environment: {current_env}, Target environment: {target_env}")
            
            # Phase 1: Deploy to inactive environment
            self._deploy_to_environment(target_env, new_version, service_manifest)
            
            # Phase 2: Comprehensive health checks
            if not self._perform_comprehensive_health_checks(target_env):
                raise Exception("Health checks failed for target environment")
            
            # Phase 3: Gradual traffic shifting
            self._execute_gradual_traffic_shift(current_env, target_env)
            
            # Phase 4: Final validation
            if not self._validate_deployment_success(target_env):
                self.logger.error("Deployment validation failed, initiating rollback")
                self._rollback_deployment(current_env, target_env)
                raise Exception("Deployment validation failed")
            
            # Phase 5: Update deployment state
            self.deployment_state['active_environment'] = target_env
            self.deployment_state['last_deployment'] = {
                'version': new_version,
                'timestamp': datetime.now().isoformat(),
                'previous_environment': current_env
            }
            
            # Phase 6: Clean up old environment (keep for rollback capability)
            self._prepare_rollback_environment(current_env)
            
            self.deployment_state['deployment_in_progress'] = False
            self.logger.info(f"Blue-green deployment completed successfully. Active environment: {target_env}")
            
        except Exception as e:
            self.deployment_state['deployment_in_progress'] = False
            self.logger.error(f"Blue-green deployment failed: {str(e)}")
            self._send_deployment_alert("failed", str(e))
            raise
    
    def _deploy_to_environment(self, environment: str, version: str, service_manifest: Dict):
        """
        Deploy services to specified environment (blue or green).
        This method handles the complexities of deploying AI workloads including
        GPU resources, model artifacts, and data dependencies.
        """
        try:
            self.logger.info(f"Deploying version {version} to {environment} environment")
            
            # Update manifest with environment-specific configurations
            env_manifest = self._prepare_environment_manifest(service_manifest, environment, version)
            
            # Deploy AI model services
            self._deploy_ai_model_services(env_manifest, environment)
            
            # Deploy data processing services
            self._deploy_data_processing_services(env_manifest, environment)
            
            # Deploy API gateway and ingress
            self._deploy_api_services(env_manifest, environment)
            
            # Deploy monitoring and logging services
            self._deploy_monitoring_services(env_manifest, environment)
            
            # Wait for all deployments to be ready
            self._wait_for_deployment_ready(environment)
            
            self.logger.info(f"Deployment to {environment} environment completed")
            
        except Exception as e:
            self.logger.error(f"Deployment to {environment} environment failed: {str(e)}")
            raise
    
    def _deploy_ai_model_services(self, manifest: Dict, environment: str):
        """
        Deploy AI model services including MLflow, model serving, and inference endpoints.
        This method demonstrates deploying government AI services with proper resource allocation.
        """
        try:
            ai_services = manifest.get('ai_services', {})
            
            for service_name, service_config in ai_services.items():
                self.logger.info(f"Deploying AI service: {service_name} to {environment}")
                
                # Create deployment manifest
                deployment_manifest = {
                    'apiVersion': 'apps/v1',
                    'kind': 'Deployment',
                    'metadata': {
                        'name': f"{service_name}-{environment}",
                        'namespace': 'ai-services',
                        'labels': {
                            'app': service_name,
                            'environment': environment,
                            'version': manifest['version']
                        }
                    },
                    'spec': {
                        'replicas': service_config.get('replicas', 3),
                        'selector': {
                            'matchLabels': {
                                'app': service_name,
                                'environment': environment
                            }
                        },
                        'template': {
                            'metadata': {
                                'labels': {
                                    'app': service_name,
                                    'environment': environment,
                                    'version': manifest['version']
                                }
                            },
                            'spec': {
                                'containers': [{
                                    'name': service_name,
                                    'image': f"{service_config['image']}:{manifest['version']}",
                                    'ports': service_config.get('ports', []),
                                    'env': self._build_environment_variables(service_config, environment),
                                    'resources': {
                                        'requests': {
                                            'memory': service_config.get('memory_request', '1Gi'),
                                            'cpu': service_config.get('cpu_request', '500m')
                                        },
                                        'limits': {
                                            'memory': service_config.get('memory_limit', '2Gi'),
                                            'cpu': service_config.get('cpu_limit', '1000m')
                                        }
                                    },
                                    'volumeMounts': service_config.get('volume_mounts', []),
                                    'livenessProbe': {
                                        'httpGet': {
                                            'path': service_config.get('health_check_path', '/health'),
                                            'port': service_config.get('health_check_port', 8080)
                                        },
                                        'initialDelaySeconds': 30,
                                        'periodSeconds': 10
                                    },
                                    'readinessProbe': {
                                        'httpGet': {
                                            'path': service_config.get('readiness_check_path', '/ready'),
                                            'port': service_config.get('health_check_port', 8080)
                                        },
                                        'initialDelaySeconds': 5,
                                        'periodSeconds': 5
                                    }
                                }],
                                'volumes': service_config.get('volumes', []),
                                'nodeSelector': service_config.get('node_selector', {}),
                                'tolerations': service_config.get('tolerations', [])
                            }
                        }
                    }
                }
                
                # Add GPU resources if required
                if service_config.get('gpu_required', False):
                    deployment_manifest['spec']['template']['spec']['containers'][0]['resources']['limits']['nvidia.com/gpu'] = service_config.get('gpu_count', 1)
                
                # Deploy to Kubernetes
                self.k8s_apps_v1.create_namespaced_deployment(
                    namespace='ai-services',
                    body=deployment_manifest
                )
                
                # Create service for the deployment
                service_manifest = {
                    'apiVersion': 'v1',
                    'kind': 'Service',
                    'metadata': {
                        'name': f"{service_name}-{environment}",
                        'namespace': 'ai-services',
                        'labels': {
                            'app': service_name,
                            'environment': environment
                        }
                    },
                    'spec': {
                        'selector': {
                            'app': service_name,
                            'environment': environment
                        },
                        'ports': [
                            {
                                'port': port['port'],
                                'targetPort': port['targetPort'],
                                'protocol': port.get('protocol', 'TCP')
                            } for port in service_config.get('ports', [])
                        ],
                        'type': service_config.get('service_type', 'ClusterIP')
                    }
                }
                
                self.k8s_core_v1.create_namespaced_service(
                    namespace='ai-services',
                    body=service_manifest
                )
                
                self.logger.info(f"AI service {service_name} deployed successfully to {environment}")
                
        except Exception as e:
            self.logger.error(f"AI model services deployment failed: {str(e)}")
            raise
    
    def _execute_gradual_traffic_shift(self, source_env: str, target_env: str):
        """
        Execute gradual traffic shifting from source to target environment.
        This method demonstrates safe traffic migration with monitoring and rollback capability.
        """
        try:
            self.logger.info(f"Starting gradual traffic shift from {source_env} to {target_env}")
            
            # Define traffic shift stages
            traffic_stages = [
                {'target_percentage': 10, 'duration_minutes': 5},
                {'target_percentage': 25, 'duration_minutes': 10},
                {'target_percentage': 50, 'duration_minutes': 15},
                {'target_percentage': 75, 'duration_minutes': 10},
                {'target_percentage': 100, 'duration_minutes': 5}
            ]
            
            for stage in traffic_stages:
                self.logger.info(f"Shifting {stage['target_percentage']}% traffic to {target_env}")
                
                # Update ingress controller weights
                self._update_traffic_weights(source_env, target_env, stage['target_percentage'])
                
                # Monitor for specified duration
                monitor_start = datetime.now()
                monitor_end = monitor_start + timedelta(minutes=stage['duration_minutes'])
                
                while datetime.now() < monitor_end:
                    # Check health metrics during traffic shift
                    health_status = self._monitor_traffic_shift_health(target_env)
                    
                    if not health_status['healthy']:
                        self.logger.error(f"Health issues detected during traffic shift: {health_status['issues']}")
                        # Rollback traffic shift
                        self._update_traffic_weights(source_env, target_env, 0)
                        raise Exception(f"Traffic shift failed due to health issues: {health_status['issues']}")
                    
                    # Wait before next health check
                    time.sleep(30)
                
                self.logger.info(f"Traffic shift stage completed: {stage['target_percentage']}% to {target_env}")
            
            self.logger.info("Gradual traffic shift completed successfully")
            
        except Exception as e:
            self.logger.error(f"Gradual traffic shift failed: {str(e)}")
            # Attempt to rollback traffic
            self._update_traffic_weights(source_env, target_env, 0)
            raise
    
    def _monitor_traffic_shift_health(self, environment: str) -> Dict:
        """
        Monitor system health during traffic shifting.
        This includes response times, error rates, and AI model performance.
        """
        try:
            health_status = {
                'healthy': True,
                'issues': [],
                'metrics': {}
            }
            
            # Check response times
            response_times = self._check_response_times(environment)
            health_status['metrics']['response_times'] = response_times
            
            if response_times['p95'] > 2000:  # 2 second threshold
                health_status['healthy'] = False
                health_status['issues'].append(f"High response times: {response_times['p95']}ms")
            
            # Check error rates
            error_rates = self._check_error_rates(environment)
            health_status['metrics']['error_rates'] = error_rates
            
            if error_rates['error_percentage'] > 1.0:  # 1% error threshold
                health_status['healthy'] = False
                health_status['issues'].append(f"High error rate: {error_rates['error_percentage']}%")
            
            # Check AI model performance
            model_performance = self._check_ai_model_performance(environment)
            health_status['metrics']['model_performance'] = model_performance
            
            if model_performance['accuracy_degradation'] > 0.05:  # 5% degradation threshold
                health_status['healthy'] = False
                health_status['issues'].append(f"AI model accuracy degradation: {model_performance['accuracy_degradation']}")
            
            # Check resource utilization
            resource_usage = self._check_resource_utilization(environment)
            health_status['metrics']['resource_usage'] = resource_usage
            
            if resource_usage['cpu_usage'] > 80 or resource_usage['memory_usage'] > 80:
                health_status['healthy'] = False
                health_status['issues'].append(f"High resource usage: CPU {resource_usage['cpu_usage']}%, Memory {resource_usage['memory_usage']}%")
            
            return health_status
            
        except Exception as e:
            return {
                'healthy': False,
                'issues': [f"Health monitoring error: {str(e)}"],
                'metrics': {}
            }

class MaintenanceScheduler:
    """
    Manages scheduled maintenance for sovereign AI cloud infrastructure.
    This class demonstrates how to perform maintenance activities while
    minimizing impact on government services.
    """
    
    def __init__(self, maintenance_config_path: str):
        with open(maintenance_config_path, 'r') as f:
            self.maintenance_config = yaml.safe_load(f)
        
        logging.basicConfig(level=logging.INFO)
        self.logger = logging.getLogger(__name__)
        
        # Track maintenance windows and activities
        self.maintenance_state = {
            'current_maintenance': None,
            'scheduled_maintenance': [],
            'maintenance_history': []
        }
    
    def schedule_routine_maintenance(self):
        """
        Schedule routine maintenance activities including security updates,
        performance optimization, and compliance validation.
        """
        try:
            self.logger.info("Scheduling routine maintenance activities")
            
            # Define maintenance activities
            maintenance_activities = [
                {
                    'name': 'security_updates',
                    'description': 'Apply security updates to all systems',
                    'frequency': 'weekly',
                    'duration_hours': 2,
                    'impact_level': 'low',
                    'requires_downtime': False
                },
                {
                    'name': 'ai_model_retraining',
                    'description': 'Retrain AI models with latest data',
                    'frequency': 'monthly',
                    'duration_hours': 8,
                    'impact_level': 'medium',
                    'requires_downtime': False
                },
                {
                    'name': 'database_optimization',
                    'description': 'Optimize database performance',
                    'frequency': 'monthly',
                    'duration_hours': 3,
                    'impact_level': 'medium',
                    'requires_downtime': False
                },
                {
                    'name': 'compliance_validation',
                    'description': 'Validate compliance with government regulations',
                    'frequency': 'quarterly',
                    'duration_hours': 4,
                    'impact_level': 'low',
                    'requires_downtime': False
                },
                {
                    'name': 'disaster_recovery_test',
                    'description': 'Test disaster recovery procedures',
                    'frequency': 'quarterly',
                    'duration_hours': 6,
                    'impact_level': 'high',
                    'requires_downtime': True
                }
            ]
            
            # Schedule each activity based on frequency
            for activity in maintenance_activities:
                next_execution = self._calculate_next_execution_time(activity)
                
                scheduled_maintenance = {
                    'activity': activity,
                    'scheduled_time': next_execution,
                    'status': 'scheduled',
                    'scheduled_by': 'automated_scheduler',
                    'scheduled_at': datetime.now().isoformat()
                }
                
                self.maintenance_state['scheduled_maintenance'].append(scheduled_maintenance)
                
                self.logger.info(f"Scheduled maintenance activity: {activity['name']} for {next_execution}")
            
            # Sort scheduled maintenance by execution time
            self.maintenance_state['scheduled_maintenance'].sort(
                key=lambda x: x['scheduled_time']
            )
            
        except Exception as e:
            self.logger.error(f"Maintenance scheduling failed: {str(e)}")
            raise
    
    def execute_maintenance_activity(self, activity: Dict):
        """
        Execute specific maintenance activity with proper coordination and monitoring.
        This method demonstrates safe maintenance execution for government systems.
        """
        try:
            self.logger.info(f"Starting maintenance activity: {activity['name']}")
            
            # Update maintenance state
            self.maintenance_state['current_maintenance'] = {
                'activity': activity,
                'start_time': datetime.now().isoformat(),
                'status': 'in_progress'
            }
            
            # Send maintenance start notification
            self._send_maintenance_notification('started', activity)
            
            # Execute pre-maintenance checks
            if not self._perform_pre_maintenance_checks(activity):
                raise Exception("Pre-maintenance checks failed")
            
            # Execute maintenance based on activity type
            if activity['name'] == 'security_updates':
                self._execute_security_updates()
            elif activity['name'] == 'ai_model_retraining':
                self._execute_ai_model_retraining()
            elif activity['name'] == 'database_optimization':
                self._execute_database_optimization()
            elif activity['name'] == 'compliance_validation':
                self._execute_compliance_validation()
            elif activity['name'] == 'disaster_recovery_test':
                self._execute_disaster_recovery_test()
            else:
                raise Exception(f"Unknown maintenance activity: {activity['name']}")
            
            # Execute post-maintenance validation
            if not self._perform_post_maintenance_validation(activity):
                raise Exception("Post-maintenance validation failed")
            
            # Update maintenance state
            self.maintenance_state['current_maintenance']['status'] = 'completed'
            self.maintenance_state['current_maintenance']['end_time'] = datetime.now().isoformat()
            
            # Move to maintenance history
            self.maintenance_state['maintenance_history'].append(
                self.maintenance_state['current_maintenance']
            )
            self.maintenance_state['current_maintenance'] = None
            
            # Send maintenance completion notification
            self._send_maintenance_notification('completed', activity)
            
            self.logger.info(f"Maintenance activity completed successfully: {activity['name']}")
            
        except Exception as e:
            # Update maintenance state with error
            if self.maintenance_state['current_maintenance']:
                self.maintenance_state['current_maintenance']['status'] = 'failed'
                self.maintenance_state['current_maintenance']['error'] = str(e)
                self.maintenance_state['current_maintenance']['end_time'] = datetime.now().isoformat()
            
            # Send maintenance failure notification
            self._send_maintenance_notification('failed', activity, str(e))
            
            self.logger.error(f"Maintenance activity failed: {activity['name']}: {str(e)}")
            raise
    
    def _execute_security_updates(self):
        """
        Execute security updates across all system components.
        This includes OS updates, container image updates, and security patches.
        """
        try:
            self.logger.info("Executing security updates")
            
            # Update base OS packages on all nodes
            self._update_node_packages()
            
            # Update container images with latest security patches
            self._update_container_images()
            
            # Update Kubernetes cluster components
            self._update_kubernetes_components()
            
            # Update security tools and signatures
            self._update_security_tools()
            
            # Validate security posture after updates
            self._validate_security_posture()
            
            self.logger.info("Security updates completed successfully")
            
        except Exception as e:
            self.logger.error(f"Security updates failed: {str(e)}")
            raise
    
    def _execute_ai_model_retraining(self):
        """
        Execute AI model retraining with latest government data.
        This ensures models remain accurate and relevant for government services.
        """
        try:
            self.logger.info("Executing AI model retraining")
            
            # Get list of models that need retraining
            models_to_retrain = self._identify_models_for_retraining()
            
            for model in models_to_retrain:
                self.logger.info(f"Retraining model: {model['name']}")
                
                # Prepare training data
                training_data = self._prepare_training_data(model)
                
                # Execute model training
                training_results = self._train_model(model, training_data)
                
                # Validate model performance
                if not self._validate_model_performance(model, training_results):
                    self.logger.warning(f"Model performance validation failed for {model['name']}")
                    continue
                
                # Deploy updated model (using blue-green deployment)
                self._deploy_updated_model(model, training_results)
                
                self.logger.info(f"Model retraining completed: {model['name']}")
            
            self.logger.info("AI model retraining completed successfully")
            
        except Exception as e:
            self.logger.error(f"AI model retraining failed: {str(e)}")
            raise

# Automated maintenance orchestration
def start_maintenance_orchestration():
    """
    Start automated maintenance orchestration service.
    This service continuously monitors and executes scheduled maintenance.
    """
    try:
        # Initialize maintenance scheduler
        scheduler = MaintenanceScheduler('/config/maintenance-config.yaml')
        
        # Schedule routine maintenance
        scheduler.schedule_routine_maintenance()
        
        # Start maintenance execution loop
        while True:
            # Check for scheduled maintenance
            scheduled_activities = scheduler.maintenance_state['scheduled_maintenance']
            current_time = datetime.now()
            
            for scheduled_activity in scheduled_activities.copy():
                scheduled_time = datetime.fromisoformat(scheduled_activity['scheduled_time'])
                
                if current_time >= scheduled_time and scheduled_activity['status'] == 'scheduled':
                    # Execute maintenance activity
                    try:
                        scheduler.execute_maintenance_activity(scheduled_activity['activity'])
                        scheduled_activities.remove(scheduled_activity)
                    except Exception as e:
                        logging.error(f"Failed to execute maintenance activity: {str(e)}")
                        scheduled_activity['status'] = 'failed'
            
            # Wait before checking again
            time.sleep(300)  # Check every 5 minutes
            
    except Exception as e:
        logging.error(f"Maintenance orchestration failed: {str(e)}")
        raise

if __name__ == "__main__":
    start_maintenance_orchestration()

Conclusion

This comprehensive guide has walked you through every aspect of establishing a sovereign AI cloud solution for the Australian government. From initial planning and infrastructure setup to ongoing maintenance and compliance validation, each step has been designed to meet the unique requirements of government operations while maintaining the highest standards of security and sovereignty.

Key Takeaways

Data Sovereignty: Every component of this solution ensures that government data remains within Australian borders and under Australian control. This includes not just storage, but processing, backup, and disaster recovery operations.

Security First: The security implementation goes beyond standard commercial practices to address government-specific threats, including nation-state actors and sophisticated attack vectors targeting government infrastructure.

Compliance by Design: Rather than treating compliance as an afterthought, this solution builds regulatory compliance into every layer, from data classification and access controls to audit logging and retention policies.

Operational Excellence: The monitoring, maintenance, and disaster recovery procedures ensure that government services remain available and performant, meeting the service level expectations of citizens and government agencies.

Future-Ready Architecture: The containerized, cloud-native approach ensures that the solution can evolve with changing technology and government requirements while maintaining security and compliance standards.

Next Steps

1. Assessment Phase: Begin with a thorough assessment of your current infrastructure and requirements

2. Pilot Implementation: Start with a small pilot deployment to validate the approach

3. Phased Rollout: Gradually expand the implementation across government agencies

4. Continuous Improvement: Establish ongoing processes for security updates, compliance validation, and performance optimization

Resources and References

• Australian Government Information Security Manual (ISM)

• Australian Privacy Act 1988

• Australian Cyber Security Centre (ACSC) Guidelines

• Kubernetes Documentation: https://kubernetes.io/docs/

• MLflow Documentation: https://mlflow.org/docs/

• Prometheus Monitoring: https://prometheus.io/docs/

• Australian Government Cloud Computing Policy

This guide provides the foundation for a world-class sovereign AI cloud solution that meets the unique needs of Australian government operations while maintaining the highest standards of security, compliance, and operational excellence.

12. Advanced Configuration and Optimization

12.1 Performance Tuning for Government Workloads

Step 42: Optimize AI Workload Performance

Government AI workloads often have unique performance requirements, particularly around response times for citizen services and processing large datasets for policy analysis.

# GPU Performance Optimization Script
#!/bin/bash

# optimize_gpu_performance.sh
# This script optimizes GPU performance for government AI workloads

echo "Starting GPU performance optimization for government AI workloads..."

# Set GPU performance mode to maximum
nvidia-smi -pm 1

# Set GPU power limit to maximum (adjust based on your hardware)
nvidia-smi -pl 300

# Set GPU memory and graphics clocks to maximum stable values
nvidia-smi -ac 5001,1590

# Configure GPU persistence mode for faster job startup
nvidia-smi -pm 1

# Set up GPU monitoring
cat > /etc/systemd/system/gpu-monitor.service << EOF
[Unit]
Description=GPU Performance Monitor
After=network.target

[Service]
Type=simple
User=root
ExecStart=/usr/local/bin/gpu-monitor.sh
Restart=always
RestartSec=10

[Install]
WantedBy=multi-user.target
EOF

# Create GPU monitoring script
cat > /usr/local/bin/gpu-monitor.sh << 'EOF'
#!/bin/bash
while true; do
    # Log GPU utilization and temperature
    nvidia-smi --query-gpu=timestamp,gpu_uuid,utilization.gpu,utilization.memory,temperature.gpu,power.draw --format=csv,noheader,nounits >> /var/log/gpu-performance.log
    
    # Check for thermal throttling
    TEMP=$(nvidia-smi --query-gpu=temperature.gpu --format=csv,noheader,nounits)
    if [ "$TEMP" -gt 83 ]; then
        echo "$(date): GPU temperature high: ${TEMP}°C" >> /var/log/gpu-alerts.log
        # Send alert to monitoring system
        curl -X POST http://alertmanager:9093/api/v1/alerts \
             -H "Content-Type: application/json" \
             -d '[{"labels":{"alertname":"GPUTemperatureHigh","severity":"warning","gpu_temp":"'$TEMP'"}}]'
    fi
    
    sleep 60
done
EOF

chmod +x /usr/local/bin/gpu-monitor.sh
systemctl enable gpu-monitor
systemctl start gpu-monitor

echo "GPU performance optimization completed"

Database Performance Tuning:

-- postgresql_government_tuning.sql
-- Optimize PostgreSQL for government data processing workloads

-- Adjust memory settings for large government datasets
ALTER SYSTEM SET shared_buffers = '8GB';
ALTER SYSTEM SET effective_cache_size = '24GB';
ALTER SYSTEM SET work_mem = '256MB';
ALTER SYSTEM SET maintenance_work_mem = '2GB';

-- Optimize for government reporting workloads
ALTER SYSTEM SET random_page_cost = 1.1;
ALTER SYSTEM SET seq_page_cost = 1.0;
ALTER SYSTEM SET effective_io_concurrency = 200;

-- Checkpoint and WAL optimization for high-write government systems
ALTER SYSTEM SET wal_buffers = '16MB';
ALTER SYSTEM SET checkpoint_completion_target = 0.9;
ALTER SYSTEM SET checkpoint_timeout = '15min';
ALTER SYSTEM SET max_wal_size = '4GB';
ALTER SYSTEM SET min_wal_size = '1GB';

-- Connection and worker process optimization
ALTER SYSTEM SET max_connections = 200;
ALTER SYSTEM SET max_worker_processes = 16;
ALTER SYSTEM SET max_parallel_workers = 8;
ALTER SYSTEM SET max_parallel_workers_per_gather = 4;

-- Logging for government audit requirements
ALTER SYSTEM SET log_statement = 'all';
ALTER SYSTEM SET log_min_duration_statement = 1000;
ALTER SYSTEM SET log_checkpoints = on;
ALTER SYSTEM SET log_connections = on;
ALTER SYSTEM SET log_disconnections = on;
ALTER SYSTEM SET log_lock_waits = on;

-- Apply configuration
SELECT pg_reload_conf();

14. Troubleshooting and Support

14.1 Common Issues and Solutions

Step 46: Comprehensive Troubleshooting Guide

#!/bin/bash
# government_ai_cloud_diagnostics.sh
# Comprehensive diagnostics script for troubleshooting government AI cloud issues

echo "=== Government AI Cloud Diagnostics Tool ==="
echo "Starting comprehensive system diagnostics..."

# Function to check system health
check_system_health() {
    echo "Checking system health..."
    
    # Check disk space
    echo "=== Disk Space Check ==="
    df -h | grep -E "(Filesystem|/dev/)" | head -20
    
    # Check for full disks
    DISK_USAGE=$(df -h | awk 'NF==6 && $5+0 >= 85 {print $5, $6}')
    if [ ! -z "$DISK_USAGE" ]; then
        echo "WARNING: High disk usage detected:"
        echo "$DISK_USAGE"
    fi
    
    # Check memory usage
    echo "=== Memory Usage Check ==="
    free -h
    
    # Check for memory pressure
    MEM_USAGE=$(free | awk 'NR==2{printf "%.1f%%", $3/$2*100}')
    echo "Memory usage: $MEM_USAGE"
    
    # Check CPU usage
    echo "=== CPU Usage Check ==="
    top -bn1 | grep "Cpu(s)" | awk '{print $2}' | awk -F'%' '{print "CPU Usage: " $1"%"}'
    
    # Check system load
    echo "=== System Load Check ==="
    uptime
    
    # Check for high load
    LOAD_AVG=$(uptime | awk -F'load average:' '{ print $2 }' | cut -d, -f1)
    CPU_CORES=$(nproc)
    if (( $(echo "$LOAD_AVG > $CPU_CORES" | bc -l) )); then
        echo "WARNING: High system load detected: $LOAD_AVG on $CPU_CORES cores"
    fi
}

# Function to check Kubernetes cluster health
check_kubernetes_health() {
    echo "=== Kubernetes Cluster Health Check ==="
    
    # Check node status
    echo "Node Status:"
    kubectl get nodes -o wide
    
    # Check for unhealthy nodes
    UNHEALTHY_NODES=$(kubectl get nodes --no-headers | grep -v Ready | wc -l)
    if [ $UNHEALTHY_NODES -gt 0 ]; then
        echo "WARNING: $UNHEALTHY_NODES unhealthy nodes detected"
        kubectl get nodes --no-headers | grep -v Ready
    fi
    
    # Check pod status across critical namespaces
    echo "=== Critical Pod Status ==="
    CRITICAL_NAMESPACES=("kube-system" "kubeflow" "mlflow" "security" "monitoring")
    
    for namespace in "${CRITICAL_NAMESPACES[@]}"; do
        echo "Checking namespace: $namespace"
        kubectl get pods -n $namespace --no-headers | grep -v Running | grep -v Completed
        
        # Count failing pods
        FAILING_PODS=$(kubectl get pods -n $namespace --no-headers | grep -v Running | grep -v Completed | wc -l)
        if [ $FAILING_PODS -gt 0 ]; then
            echo "WARNING: $FAILING_PODS failing pods in namespace $namespace"
        fi
    done
    
    # Check persistent volume claims
    echo "=== PVC Status ==="
    kubectl get pvc --all-namespaces | grep -E "(Pending|Lost)"
    
    # Check cluster events for errors
    echo "=== Recent Cluster Events ==="
    kubectl get events --all-namespaces --sort-by='.lastTimestamp' | tail -20
}

# Function to check AI/ML services
check_ai_services() {
    echo "=== AI/ML Services Health Check ==="
    
    # Check MLflow service
    echo "Checking MLflow service..."
    MLFLOW_STATUS=$(curl -s -o /dev/null -w "%{http_code}" http://mlflow-service:5000/health || echo "000")
    if [ "$MLFLOW_STATUS" != "200" ]; then
        echo "WARNING: MLflow service unhealthy (HTTP $MLFLOW_STATUS)"
    else
        echo "MLflow service: OK"
    fi
    
    # Check JupyterHub service
    echo "Checking JupyterHub service..."
    JUPYTER_STATUS=$(curl -s -o /dev/null -w "%{http_code}" http://jupyterhub:8000/hub/api || echo "000")
    if [ "$JUPYTER_STATUS" != "200" ]; then
        echo "WARNING: JupyterHub service unhealthy (HTTP $JUPYTER_STATUS)"
    else
        echo "JupyterHub service: OK"
    fi
    
    # Check GPU availability
    echo "=== GPU Status ==="
    if command -v nvidia-smi &> /dev/null; then
        nvidia-smi --query-gpu=name,utilization.gpu,memory.used,memory.total,temperature.gpu --format=csv,noheader,nounits
        
        # Check for GPU errors
        GPU_ERRORS=$(nvidia-smi --query-gpu=gpu_name --format=csv,noheader 2>&1 | grep -i error | wc -l)
        if [ $GPU_ERRORS -gt 0 ]; then
            echo "WARNING: GPU errors detected"
            nvidia-smi
        fi
    else
        echo "NVIDIA drivers not installed or GPUs not available"
    fi
    
    # Check model serving endpoints
    echo "=== AI Model Endpoints ==="
    ENDPOINTS=("citizen-service-classifier" "document-processor" "fraud-detector")
    
    for endpoint in "${ENDPOINTS[@]}"; do
        ENDPOINT_STATUS=$(curl -s -o /dev/null -w "%{http_code}" "http://ai-gateway/api/v1/$endpoint/health" || echo "000")
        if [ "$ENDPOINT_STATUS" != "200" ]; then
            echo "WARNING: $endpoint endpoint unhealthy (HTTP $ENDPOINT_STATUS)"
        else
            echo "$endpoint endpoint: OK"
        fi
    done
}

# Function to check security services
check_security_services() {
    echo "=== Security Services Health Check ==="
    
    # Check Wazuh manager
    echo "Checking Wazuh SIEM..."
    WAZUH_STATUS=$(systemctl is-active wazuh-manager 2>/dev/null || echo "inactive")
    if [ "$WAZUH_STATUS" != "active" ]; then
        echo "WARNING: Wazuh manager not running"
    else
        echo "Wazuh manager: OK"
    fi
    
    # Check certificate validity
    echo "=== Certificate Status ==="
    CERT_FILE="/etc/ssl/certs/government-ai-cloud.crt"
    if [ -f "$CERT_FILE" ]; then
        EXPIRY_DATE=$(openssl x509 -enddate -noout -in "$CERT_FILE" | cut -d= -f2)
        EXPIRY_EPOCH=$(date -d "$EXPIRY_DATE" +%s)
        CURRENT_EPOCH=$(date +%s)
        DAYS_UNTIL_EXPIRY=$(( (EXPIRY_EPOCH - CURRENT_EPOCH) / 86400 ))
        
        echo "Certificate expires in $DAYS_UNTIL_EXPIRY days"
        if [ $DAYS_UNTIL_EXPIRY -lt 30 ]; then
            echo "WARNING: Certificate expires in less than 30 days"
        fi
    else
        echo "WARNING: Certificate file not found"
    fi
    
    # Check firewall status
    echo "=== Firewall Status ==="
    UFW_STATUS=$(ufw status | head -1)
    echo "UFW: $UFW_STATUS"
    
    # Check fail2ban
    if command -v fail2ban-client &> /dev/null; then
        echo "Fail2ban status:"
        fail2ban-client status
    fi
}

# Function to check data services
check_data_services() {
    echo "=== Data Services Health Check ==="
    
    # Check PostgreSQL
    echo "Checking PostgreSQL..."
    PG_STATUS=$(systemctl is-active postgresql 2>/dev/null || echo "inactive")
    if [ "$PG_STATUS" != "active" ]; then
        echo "WARNING: PostgreSQL not running"
    else
        echo "PostgreSQL: OK"
        
        # Check database connections
        DB_CONNECTIONS=$(sudo -u postgres psql -c "SELECT count(*) FROM pg_stat_activity;" -t | xargs)
        echo "Active database connections: $DB_CONNECTIONS"
    fi
    
    # Check MinIO object storage
    echo "Checking MinIO..."
    MINIO_STATUS=$(curl -s -o /dev/null -w "%{http_code}" http://minio:9000/minio/health/live || echo "000")
    if [ "$MINIO_STATUS" != "200" ]; then
        echo "WARNING: MinIO unhealthy (HTTP $MINIO_STATUS)"
    else
        echo "MinIO: OK"
    fi
    
    # Check Redis (if used for caching)
    echo "Checking Redis..."
    if command -v redis-cli &> /dev/null; then
        REDIS_PING=$(redis-cli ping 2>/dev/null || echo "FAILED")
        if [ "$REDIS_PING" != "PONG" ]; then
            echo "WARNING: Redis not responding"
        else
            echo "Redis: OK"
        fi
    fi
}

# Function to check monitoring services
check_monitoring_services() {
    echo "=== Monitoring Services Health Check ==="
    
    # Check Prometheus
    echo "Checking Prometheus..."
    PROMETHEUS_STATUS=$(curl -s -o /dev/null -w "%{http_code}" http://prometheus:9090/-/healthy || echo "000")
    if [ "$PROMETHEUS_STATUS" != "200" ]; then
        echo "WARNING: Prometheus unhealthy (HTTP $PROMETHEUS_STATUS)"
    else
        echo "Prometheus: OK"
    fi
    
    # Check Grafana
    echo "Checking Grafana..."
    GRAFANA_STATUS=$(curl -s -o /dev/null -w "%{http_code}" http://grafana:3000/api/health || echo "000")
    if [ "$GRAFANA_STATUS" != "200" ]; then
        echo "WARNING: Grafana unhealthy (HTTP $GRAFANA_STATUS)"
    else
        echo "Grafana: OK"
    fi
    
    # Check Elasticsearch (for logging)
    echo "Checking Elasticsearch..."
    ES_STATUS=$(curl -s -o /dev/null -w "%{http_code}" http://elasticsearch:9200/_cluster/health || echo "000")
    if [ "$ES_STATUS" != "200" ]; then
        echo "WARNING: Elasticsearch unhealthy (HTTP $ES_STATUS)"
    else
        echo "Elasticsearch: OK"
    fi
}

# Function to generate diagnostic report
generate_diagnostic_report() {
    echo "=== Generating Diagnostic Report ==="
    
    REPORT_FILE="/tmp/government_ai_cloud_diagnostic_$(date +%Y%m%d_%H%M%S).txt"
    
    {
        echo "Government AI Cloud Diagnostic Report"
        echo "Generated: $(date)"
        echo "Hostname: $(hostname)"
        echo "Kernel: $(uname -r)"
        echo "Uptime: $(uptime)"
        echo "=========================================="
        echo ""
        
        check_system_health
        echo ""
        check_kubernetes_health
        echo ""
        check_ai_services
        echo ""
        check_security_services
        echo ""
        check_data_services
        echo ""
        check_monitoring_services
        
    } | tee "$REPORT_FILE"
    
    echo "Diagnostic report saved to: $REPORT_FILE"
    
    # Compress the report
    gzip "$REPORT_FILE"
    echo "Compressed report: ${REPORT_FILE}.gz"
}

# Main execution
main() {
    check_system_health
    echo ""
    check_kubernetes_health
    echo ""
    check_ai_services
    echo ""
    check_security_services
    echo ""
    check_data_services
    echo ""
    check_monitoring_services
    echo ""
    generate_diagnostic_report
}

# Run main function if script is executed directly
if [[ "${BASH_SOURCE[0]}" == "${0}" ]]; then
    main "$@"
fi

14.2 Performance Optimization Scripts

# performance_optimizer.py
import psutil
import subprocess
import logging
import yaml
import time
from datetime import datetime, timedelta
from typing import Dict, List, Optional
import nvidia_ml_py3 as nvml

class GovernmentCloudOptimizer:
    """
    Performance optimization tool specifically designed for government AI cloud workloads.
    This tool monitors system performance and automatically applies optimizations
    to maintain optimal performance for government services.
    """
    
    def __init__(self, optimization_config_path: str):
        with open(optimization_config_path, 'r') as f:
            self.config = yaml.safe_load(f)
        
        self.logger = logging.getLogger(__name__)
        
        # Initialize NVIDIA ML for GPU monitoring
        try:
            nvml.nvmlInit()
            self.gpu_available = True
        except:
            self.gpu_available = False
            self.logger.warning("NVIDIA GPUs not available for monitoring")
    
    def optimize_system_performance(self) -> Dict:
        """
        Perform comprehensive system performance optimization.
        This includes CPU, memory, disk, network, and GPU optimizations.
        """
        try:
            self.logger.info("Starting system performance optimization")
            
            optimization_results = {
                'timestamp': datetime.now().isoformat(),
                'optimizations_applied': [],
                'performance_metrics': {},
                'recommendations': []
            }
            
            # CPU optimizations
            cpu_optimizations = self._optimize_cpu_performance()
            optimization_results['optimizations_applied'].extend(cpu_optimizations)
            
            # Memory optimizations
            memory_optimizations = self._optimize_memory_performance()
            optimization_results['optimizations_applied'].extend(memory_optimizations)
            
            # Disk I/O optimizations
            disk_optimizations = self._optimize_disk_performance()
            optimization_results['optimizations_applied'].extend(disk_optimizations)
            
            # Network optimizations
            network_optimizations = self._optimize_network_performance()
            optimization_results['optimizations_applied'].extend(network_optimizations)
            
            # GPU optimizations (if available)
            if self.gpu_available:
                gpu_optimizations = self._optimize_gpu_performance()
                optimization_results['optimizations_applied'].extend(gpu_optimizations)
            
            # Kubernetes optimizations
            k8s_optimizations = self._optimize_kubernetes_performance()
            optimization_results['optimizations_applied'].extend(k8s_optimizations)
            
            # Collect post-optimization metrics
            optimization_results['performance_metrics'] = self._collect_performance_metrics()
            
            # Generate recommendations
            optimization_results['recommendations'] = self._generate_performance_recommendations()
            
            self.logger.info("System performance optimization completed")
            return optimization_results
            
        except Exception as e:
            self.logger.error(f"System performance optimization failed: {str(e)}")
            return {'status': 'failed', 'error': str(e)}
    
    def _optimize_cpu_performance(self) -> List[str]:
        """
        Optimize CPU performance for government AI workloads.
        This includes CPU governor settings, process priorities, and affinity optimization.
        """
        optimizations = []
        
        try:
            # Set CPU governor to performance mode for critical AI workloads
            current_governor = subprocess.check_output(['cat', '/sys/devices/system/cpu/cpu0/cpufreq/scaling_governor']).decode().strip()
            
            if current_governor != 'performance':
                subprocess.run(['cpupower', 'frequency-set', '-g', 'performance'], check=True)
                optimizations.append('Set CPU governor to performance mode')
                self.logger.info("CPU governor set to performance mode")
            
            # Optimize CPU affinity for AI processes
            ai_processes = ['python', 'jupyter', 'mlflow', 'kubeflow']
            for proc in psutil.process_iter(['pid', 'name', 'cpu_percent']):
                try:
                    if any(ai_proc in proc.info['name'].lower() for ai_proc in ai_processes):
                        if proc.info['cpu_percent'] > 50:  # High CPU usage process
                            # Set process to high priority
                            psutil.Process(proc.info['pid']).nice(-5)
                            optimizations.append(f"Increased priority for {proc.info['name']} (PID: {proc.info['pid']})")
                except (psutil.NoSuchProcess, psutil.AccessDenied):
                    continue
            
            # Disable CPU power saving features for consistent performance
            with open('/sys/devices/system/cpu/cpuidle/current_driver', 'r') as f:
                idle_driver = f.read().strip()
            
            if idle_driver != 'none':
                # Disable C-states for consistent latency
                subprocess.run(['cpupower', 'idle-set', '-d', '2'], check=False)
                optimizations.append('Disabled deep CPU idle states for consistent performance')
            
        except Exception as e:
            self.logger.error(f"CPU optimization failed: {str(e)}")
        
        return optimizations
    
    def _optimize_memory_performance(self) -> List[str]:
        """
        Optimize memory performance for large government datasets and AI models.
        """
        optimizations = []
        
        try:
            # Adjust swappiness for AI workloads (reduce swapping)
            current_swappiness = int(open('/proc/sys/vm/swappiness').read().strip())
            optimal_swappiness = 10  # Low swappiness for AI workloads
            
            if current_swappiness > optimal_swappiness:
                with open('/proc/sys/vm/swappiness', 'w') as f:
                    f.write(str(optimal_swappiness))
                optimizations.append(f'Reduced swappiness from {current_swappiness} to {optimal_swappiness}')
            
            # Optimize transparent huge pages for AI workloads
            thp_enabled = open('/sys/kernel/mm/transparent_hugepage/enabled').read().strip()
            if 'madvise' not in thp_enabled:
                with open('/sys/kernel/mm/transparent_hugepage/enabled', 'w') as f:
                    f.write('madvise')
                optimizations.append('Enabled transparent huge pages with madvise')
            
            # Adjust dirty page parameters for better I/O performance
            current_dirty_ratio = int(open('/proc/sys/vm/dirty_ratio').read().strip())
            optimal_dirty_ratio = 15  # Lower ratio for consistent performance
            
            if current_dirty_ratio > optimal_dirty_ratio:
                with open('/proc/sys/vm/dirty_ratio', 'w') as f:
                    f.write(str(optimal_dirty_ratio))
                optimizations.append(f'Adjusted dirty page ratio from {current_dirty_ratio}% to {optimal_dirty_ratio}%')
            
            # Clear page cache if memory usage is high
            memory = psutil.virtual_memory()
            if memory.percent > 80:
                subprocess.run(['sync'], check=True)
                with open('/proc/sys/vm/drop_caches', 'w') as f:
                    f.write('1')  # Drop page cache only
                optimizations.append('Cleared page cache to free memory')
            
        except Exception as e:
            self.logger.error(f"Memory optimization failed: {str(e)}")
        
        return optimizations
    
    def _optimize_gpu_performance(self) -> List[str]:
        """
        Optimize GPU performance for AI model training and inference.
        """
        optimizations = []
        
        if not self.gpu_available:
            return optimizations
        
        try:
            device_count = nvml.nvmlDeviceGetCount()
            
            for i in range(device_count):
                handle = nvml.nvmlDeviceGetHandleByIndex(i)
                
                # Set GPU to persistence mode
                try:
                    nvml.nvmlDeviceSetPersistenceMode(handle, nvml.NVML_FEATURE_ENABLED)
                    optimizations.append(f'Enabled persistence mode for GPU {i}')
                except nvml.NVMLError:
                    pass  # May already be enabled or not supported
                
                # Set power limit to maximum
                try:
                    max_power = nvml.nvmlDeviceGetPowerManagementLimitConstraints(handle)[1]
                    current_power = nvml.nvmlDeviceGetPowerManagementLimitDefault(handle)
                    
                    if current_power < max_power:
                        nvml.nvmlDeviceSetPowerManagementLimit(handle, max_power)
                        optimizations.append(f'Set GPU {i} power limit to maximum ({max_power}W)')
                except nvml.NVMLError:
                    pass  # Not supported on all GPUs
                
                # Set memory and graphics clocks to maximum
                try:
                    # Get supported memory clocks
                    mem_clocks = nvml.nvmlDeviceGetSupportedMemoryClocks(handle)
                    if mem_clocks:
                        max_mem_clock = max(mem_clocks)
                        
                        # Get supported graphics clocks for max memory clock
                        graphics_clocks = nvml.nvmlDeviceGetSupportedGraphicsClocks(handle, max_mem_clock)
                        if graphics_clocks:
                            max_graphics_clock = max(graphics_clocks)
                            
                            # Set application clocks
                            nvml.nvmlDeviceSetApplicationsClocks(handle, max_mem_clock, max_graphics_clock)
                            optimizations.append(f'Set GPU {i} to maximum clocks (Memory: {max_mem_clock}MHz, Graphics: {max_graphics_clock}MHz)')
                
                except nvml.NVMLError:
                    pass  # Not supported on all GPUs
            
        except Exception as e:
            self.logger.error(f"GPU optimization failed: {str(e)}")
        
        return optimizations

## 15. Training and Documentation

### 15.1 Administrator Training Guide

```markdown
# Government AI Cloud Administrator Training Guide

## Module 1: System Overview and Architecture

### Learning Objectives
By the end of this module, administrators will be able to:
- Understand the overall architecture of the sovereign AI cloud
- Identify key components and their interactions
- Navigate the management interfaces
- Understand data flow and security boundaries

### Architecture Components

#### 1. Infrastructure Layer
- **Physical Infrastructure**: Servers, storage, networking hardware located in Australian data centers
- **Virtualization**: VMware vSphere or KVM providing hardware abstraction
- **Container Platform**: Kubernetes orchestrating containerized applications
- **Storage Systems**: Distributed storage providing persistent volumes

#### 2. Platform Services Layer
- **Container Registry**: Secure storage for government-approved container images
- **Service Mesh**: Istio providing secure service-to-service communication
- **API Gateway**: Kong managing external API access with authentication and rate limiting
- **Load Balancers**: HAProxy distributing traffic across service instances

#### 3. AI/ML Services Layer
- **MLflow**: Model lifecycle management and experiment tracking
- **JupyterHub**: Multi-user data science environment
- **Kubeflow**: Machine learning workflow orchestration
- **Model Serving**: TensorFlow Serving and custom inference services

#### 4. Data Services Layer
- **PostgreSQL**: Primary relational database for structured data
- **MinIO**: S3-compatible object storage for unstructured data
- **Apache Kafka**: Real-time data streaming and event processing
- **Elasticsearch**: Search and analytics for logs and documents

#### 5. Security and Compliance Layer
- **Identity Management**: LDAP/Active Directory integration
- **Certificate Management**: Internal PKI for secure communications
- **Secrets Management**: Kubernetes secrets and external secret stores
- **Audit Logging**: Comprehensive audit trails for compliance

### Hands-On Lab 1: System Navigation

**Exercise 1.1: Accessing Management Interfaces**
```bash
# Connect to Kubernetes dashboard
kubectl proxy --port=8080
# Access via browser: http://localhost:8080/api/v1/namespaces/kubernetes-dashboard/services/https:kubernetes-dashboard:/proxy/

# Connect to Grafana monitoring
kubectl port-forward -n monitoring svc/grafana 3000:80
# Access via browser: http://localhost:3000

# Connect to MLflow UI
kubectl port-forward -n mlflow svc/mlflow-service 5000:5000
# Access via browser: http://localhost:5000

Exercise 1.2: Reviewing System Status

# Check overall cluster health
kubectl get nodes
kubectl get pods --all-namespaces | grep -v Running

# Check resource utilization
kubectl top nodes
kubectl top pods --all-namespaces

# Review system logs
kubectl logs -n kube-system deployment/coredns
kubectl logs -n monitoring deployment/prometheus-server

Daily Operations and Monitoring

Learning Objectives

• Monitor system health and performance

• Identify and respond to alerts

• Perform routine maintenance tasks

• Manage user access and permissions

Daily Monitoring Checklist

Morning Health Check (15 minutes)

1. Infrastructure Status

# Check node status
kubectl get nodes

# Check critical namespace pods
kubectl get pods -n kube-system
kubectl get pods -n monitoring
kubectl get pods -n security

1. Service Health Verification

# Test API endpoints
curl -k https://api.government-ai-cloud.local/health
curl -k https://mlflow.government-ai-cloud.local/health
curl -k https://jupyter.government-ai-cloud.local/hub/api

1. Resource Utilisation Review

   • Access Grafana dashboard: Infrastructure Overview

   • Review CPU, memory, and storage utilization

   • Check for any resource alerts

2. Security Status Check

   bash

# Check security service status
kubectl get pods -n security

# Review recent security alerts
kubectl logs -n security deployment/wazuh-manager --tail=100

Throughout the Day (Ongoing)

• Monitor Grafana dashboards for performance trends

• Respond to alerts from Prometheus AlertManager

• Review audit logs for unusual activity

• Check backup job status

End of Day Review (10 minutes)

1. Performance Summary

   • Review daily performance metrics

   • Document any issues encountered

   • Plan maintenance activities

2. Security Review

   • Check security incident logs

   • Review user access patterns

   • Verify backup completions

Alert Response Procedures

High Priority Alerts

Alert: Node Down

# Investigation steps
kubectl describe node <node-name>
kubectl get events --field-selector involvedObject.name=<node-name>

# If node is unresponsive
ssh <node-ip>
sudo systemctl status kubelet
sudo journalctl -u kubelet --since "1 hour ago"

# Recovery actions
sudo systemctl restart kubelet
kubectl cordon <node-name>  # If issues persist
kubectl drain <node-name> --ignore-daemonsets --delete-emptydir-data

Alert: Pod CrashLooping

bash

# Investigation
kubectl describe pod <pod-name> -n <namespace>
kubectl logs <pod-name> -n <namespace> --previous

# Common fixes
kubectl rollout restart deployment/<deployment-name> -n <namespace>
kubectl scale deployment/<deployment-name> --replicas=0 -n <namespace>
kubectl scale deployment/<deployment-name> --replicas=3 -n <namespace>

Alert: High Resource Usage

bash

# Identify resource-heavy pods
kubectl top pods --all-namespaces --sort-by=cpu
kubectl top pods --all-namespaces --sort-by=memory

# Scale down non-critical services if needed
kubectl scale deployment/<deployment-name> --replicas=1 -n <namespace>

# Check for resource leaks
kubectl describe node <node-name>

Module 3: User Management and Access Control

Learning Objectives

• Manage user accounts and permissions

• Configure role-based access control (RBAC)

• Handle access requests and approvals

• Monitor user activity

User Account Management

Creating New User Accounts

bash

# Create user in LDAP (example)
ldapadd -x -D "cn=admin,dc=government,dc=local" -W << EOF
dn: cn=john.smith,ou=users,dc=government,dc=local
objectClass: person
objectClass: organizationalPerson
objectClass: inetOrgPerson
cn: john.smith
sn: Smith
givenName: John# Sovereign AI Cloud Implementation Guide for Australian Government

## Executive Summary

This comprehensive guide provides detailed, step-by-step instructions for establishing a sovereign AI cloud solution specifically designed for Australian government use. The solution prioritizes data sovereignty, security, compliance with Australian regulations, and operational independence while maintaining scalability and performance.

## Table of Contents

1. [Understanding Sovereign AI Cloud](#understanding-sovereign-ai-cloud)
2. [Pre-Implementation Planning](#pre-implementation-planning)
3. [Infrastructure Setup](#infrastructure-setup)
4. [Security Implementation](#security-implementation)
5. [AI Platform Configuration](#ai-platform-configuration)
6. [Data Management and Storage](#data-management-and-storage)
7. [Compliance and Governance](#compliance-and-governance)
8. [Monitoring and Operations](#monitoring-and-operations)
9. [Disaster Recovery and Business Continuity](#disaster-recovery-and-business-continuity)
10. [Testing and Validation](#testing-and-validation)
11. [Go-Live and Maintenance](#go-live-and-maintenance)

## 1. Understanding Sovereign AI Cloud

### 1.1 Definition and Importance

A sovereign AI cloud is a cloud computing infrastructure that ensures complete control over data, applications, and AI models within national boundaries. For Australian government agencies, this means:

- **Data Sovereignty**: All data remains within Australian borders
- **Legal Compliance**: Adherence to Australian Privacy Act, GDPR, and government regulations
- **Security Control**: Full oversight of security protocols and access controls
- **Operational Independence**: Reduced dependency on foreign cloud providers
- **Audit Capability**: Complete transparency and auditability of all operations

### 1.2 Key Components

The sovereign AI cloud solution consists of:

- **Infrastructure Layer**: Physical servers, networking, and storage hosted in Australia
- **Platform Layer**: Kubernetes orchestration, container management, and service mesh
- **AI/ML Layer**: Machine learning frameworks, model serving, and training platforms
- **Data Layer**: Databases, data lakes, and analytics platforms
- **Security Layer**: Identity management, encryption, and compliance tools
- **Governance Layer**: Policy enforcement, audit trails, and compliance monitoring

## 2. Pre-Implementation Planning

### 2.1 Stakeholder Identification and Engagement

**Step 1: Identify Key Stakeholders**

Create a comprehensive stakeholder list including:
- Chief Information Officer (CIO)
- Chief Technology Officer (CTO)
- Chief Security Officer (CSO)
- Data Protection Officer (DPO)
- IT Operations Manager
- Compliance Manager
- Budget/Finance Manager
- End-user representatives from each department

**Step 2: Establish Governance Structure**

Set up a steering committee with:
- Executive sponsor (typically CIO or CTO)
- Project manager
- Technical lead
- Security lead
- Compliance lead
- Change management lead

**Step 3: Define Roles and Responsibilities**

Document specific responsibilities for each role:
- **Project Manager**: Timeline, budget, resource coordination
- **Technical Lead**: Architecture decisions, implementation oversight
- **Security Lead**: Security architecture, compliance validation
- **Operations Lead**: Day-to-day operations, monitoring, maintenance

### 2.2 Requirements Gathering

**Step 4: Conduct Requirements Analysis**

Create detailed requirements documentation covering:

**Functional Requirements:**
- AI/ML workload types (training, inference, data processing)
- Expected user base and concurrent users
- Performance requirements (latency, throughput)
- Integration requirements with existing systems
- Data processing and storage requirements

**Non-Functional Requirements:**
- Security requirements (encryption, access controls)
- Compliance requirements (specific regulations)
- Availability requirements (uptime, disaster recovery)
- Scalability requirements (growth projections)
- Performance requirements (response times, throughput)

**Step 5: Create Technical Specifications**

Document technical specifications including:
- Compute requirements (CPU, GPU, memory)
- Storage requirements (capacity, performance, redundancy)
- Network requirements (bandwidth, latency, security)
- Security requirements (encryption standards, access controls)
- Compliance requirements (audit trails, data retention)

### 2.3 Budget Planning

**Step 6: Develop Comprehensive Budget**

Create detailed budget covering:

**Initial Setup Costs:**
- Hardware procurement: $500,000 - $2,000,000
- Software licenses: $100,000 - $500,000
- Implementation services: $200,000 - $800,000
- Training and certification: $50,000 - $150,000

**Ongoing Operational Costs:**
- Staff salaries: $300,000 - $800,000 annually
- Maintenance and support: $100,000 - $300,000 annually
- Utility and facility costs: $50,000 - $200,000 annually
- Software renewals: $50,000 - $200,000 annually

**Step 7: Secure Funding Approval**

Prepare business case including:
- Cost-benefit analysis
- Risk assessment
- Implementation timeline
- Expected return on investment
- Comparison with alternative solutions

### 2.4 Vendor Selection

**Step 8: Evaluate Australian Data Center Providers**

Research and evaluate providers such as:
- **NextDC**: Primary Australian data center provider
- **Digital Realty**: International provider with Australian presence
- **Equinix**: Global provider with Australian facilities
- **NEXTDC**: Tier III/IV data centers in major Australian cities

**Evaluation Criteria:**
- Australian ownership and control
- Security certifications (ISO 27001, SOC 2)
- Compliance with Australian regulations
- Physical security measures
- Redundancy and disaster recovery capabilities
- Proximity to your primary location

**Step 9: Select Infrastructure Partners**

Choose partners for:
- **Hardware**: Dell, HPE, Cisco, Lenovo
- **Software**: Red Hat, VMware, Microsoft, Canonical
- **Security**: Fortinet, Palo Alto Networks, Check Point
- **Monitoring**: Splunk, Datadog, New Relic

## 3. Infrastructure Setup

### 3.1 Physical Infrastructure Preparation

**Step 10: Data Center Site Selection**

Select appropriate data center facilities based on:
- Location within Australia (preferably multiple sites)
- Tier III or IV certification
- Power redundancy (N+1 or 2N)
- Cooling systems (redundant HVAC)
- Physical security (biometric access, 24/7 monitoring)
- Connectivity options (multiple ISPs, dark fiber)

**Step 11: Hardware Procurement**

Procure hardware components:

**Compute Nodes:**
- Quantity: 20-50 servers (depending on scale)
- Specification: 2x Intel Xeon or AMD EPYC processors
- Memory: 256GB-1TB DDR4 ECC RAM
- Storage: 2x 480GB SSD (OS) + 4x 1.92TB NVMe SSD (data)
- Network: 2x 25GbE or 100GbE interfaces
- Recommended models: Dell PowerEdge R750, HPE ProLiant DL380

**GPU Nodes (for AI workloads):**
- Quantity: 5-20 servers
- GPUs: 4-8x NVIDIA A100, H100, or V100 per server
- CPU: 2x Intel Xeon or AMD EPYC processors
- Memory: 512GB-2TB DDR4 ECC RAM
- Storage: NVMe SSD for high-performance data access
- Recommended models: Dell PowerEdge R750xa, HPE ProLiant DL380a

**Storage Systems:**
- Primary storage: All-flash array (NetApp, Dell EMC, HPE)
- Capacity: 500TB-2PB usable
- Performance: 100,000+ IOPS, <1ms latency
- Backup storage: High-capacity disk arrays or tape libraries

**Network Infrastructure:**
- Core switches: 100GbE spine switches
- Top-of-rack switches: 25GbE/100GbE leaf switches
- Firewalls: Next-generation firewalls (Fortinet, Palo Alto)
- Load balancers: Hardware or software-based (F5, HAProxy)

### 3.2 Network Configuration

**Step 12: Design Network Architecture**

Implement a secure, high-performance network:

**Network Segmentation:**
- Management network (isolated for administrative access)
- Compute network (inter-node communication)
- Storage network (high-performance storage traffic)
- External network (internet and external connections)

**IP Address Planning:**
- Management: 10.1.0.0/16
- Compute: 10.2.0.0/16
- Storage: 10.3.0.0/16
- External: Public IP ranges as assigned

**Step 13: Configure Network Security**

Implement network security measures:

**Firewall Configuration:**
```bash
# Example firewall rules (adapt to your firewall platform)
# Allow management access from authorized networks
allow tcp from 10.0.0.0/8 to any port 22  # SSH
allow tcp from 10.0.0.0/8 to any port 443 # HTTPS

# Allow compute node communication
allow tcp from 10.2.0.0/16 to 10.2.0.0/16 port 6443  # Kubernetes API
allow tcp from 10.2.0.0/16 to 10.2.0.0/16 port 2379:2380  # etcd
allow tcp from 10.2.0.0/16 to 10.2.0.0/16 port 10250  # kubelet

# Block all other traffic by default
deny all

VPN Configuration:

• Deploy site-to-site VPN for multi-site connectivity

• Configure client VPN for remote administrative access

• Use IPsec with AES-256 encryption

• Implement certificate-based authentication

3.3 Operating System Installation

Step 14: Install Base Operating System

Install Ubuntu 20.04 LTS or Red Hat Enterprise Linux 8 on all nodes:

Automated Installation Process:

bash

# Create automated installation script
#!/bin/bash

# Set hostname
hostnamectl set-hostname $NODE_NAME

# Update system
apt update && apt upgrade -y

# Install essential packages
apt install -y curl wget vim git htop iotop nmap

# Configure SSH
sed -i 's/#PasswordAuthentication yes/PasswordAuthentication no/' /etc/ssh/sshd_config
systemctl restart ssh

# Configure firewall
ufw enable
ufw allow 22/tcp
ufw allow 443/tcp
ufw allow 80/tcp

# Install Docker
curl -fsSL https://get.docker.com -o get-docker.sh
sh get-docker.sh
usermod -aG docker $USER

# Install Kubernetes tools
curl -s https://packages.cloud.google.com/apt/doc/apt-key.gpg | apt-key add -
echo "deb https://apt.kubernetes.io/ kubernetes-xenial main" | tee -a /etc/apt/sources.list.d/kubernetes.list
apt update
apt install -y kubectl kubeadm kubelet

Step 15: Configure System Security

Implement security hardening:

System Hardening Script:

bash

#!/bin/bash

# Disable unnecessary services
systemctl disable bluetooth
systemctl disable cups
systemctl disable avahi-daemon

# Configure audit logging
apt install -y auditd
systemctl enable auditd
systemctl start auditd

# Configure log rotation
cat > /etc/logrotate.d/system-logs << EOF
/var/log/*.log {
    daily
    rotate 30
    compress
    delaycompress
    missingok
    notifempty
    create 0644 root root
}
EOF

# Set up automatic security updates
apt install -y unattended-upgrades
dpkg-reconfigure -plow unattended-upgrades

# Configure fail2ban
apt install -y fail2ban
systemctl enable fail2ban
systemctl start fail2ban

4. Security Implementation

4.1 Identity and Access Management

Step 16: Deploy Identity Management System

Install and configure OpenLDAP or Active Directory:

OpenLDAP Installation:

bash

# Install OpenLDAP
apt install -y slapd ldap-utils

# Configure basic LDAP structure
cat > base.ldif << EOF
dn: ou=People,dc=example,dc=com
objectClass: organizationalUnit
ou: People

dn: ou=Groups,dc=example,dc=com
objectClass: organizationalUnit
ou: Groups

dn: cn=admins,ou=Groups,dc=example,dc=com
objectClass: groupOfNames
cn: admins
member: cn=admin,ou=People,dc=example,dc=com
EOF

ldapadd -x -D "cn=admin,dc=example,dc=com" -W -f base.ldif

Step 17: Configure Multi-Factor Authentication

Deploy MFA solution using tools like:

• FreeOTP: Open-source OTP solution

• privacyIDEA: Enterprise MFA platform

• Duo Security: Cloud-based MFA service

FreeOTP Configuration:

bash

# Install FreeOTP server
apt install -y privacyidea privacyidea-apache2

# Configure Apache for privacyIDEA
a2enmod wsgi
a2enmod headers
a2enmod ssl
a2ensite privacyidea

# Start services
systemctl restart apache2
systemctl enable privacyidea

4.2 Encryption Implementation

Step 18: Deploy Certificate Authority

Set up internal PKI infrastructure:

Create Root CA:

bash

# Generate root CA private key
openssl genrsa -out ca-key.pem 4096

# Create root CA certificate
openssl req -new -x509 -days 3650 -key ca-key.pem -out ca.pem \
  -subj "/C=AU/ST=NSW/L=Sydney/O=Australian Government/CN=Root CA"

# Generate server certificate
openssl genrsa -out server-key.pem 4096
openssl req -new -key server-key.pem -out server.csr \
  -subj "/C=AU/ST=NSW/L=Sydney/O=Australian Government/CN=*.example.com"

# Sign server certificate with CA
openssl x509 -req -days 365 -in server.csr -CA ca.pem -CAkey ca-key.pem \
  -CAcreateserial -out server.pem

Step 19: Configure Encryption at Rest

Implement full disk encryption:

LUKS Encryption Setup:

bash

# Install cryptsetup
apt install -y cryptsetup

# Create encrypted partition
cryptsetup luksFormat /dev/sdb
cryptsetup luksOpen /dev/sdb encrypted_disk

# Create filesystem
mkfs.ext4 /dev/mapper/encrypted_disk

# Mount encrypted partition
mkdir /encrypted
mount /dev/mapper/encrypted_disk /encrypted

# Add to fstab for automatic mounting
echo "encrypted_disk /encrypted ext4 defaults 0 0" >> /etc/fstab
echo "encrypted_disk /dev/sdb none luks" >> /etc/crypttab

4.3 Network Security

Step 20: Configure Network Intrusion Detection

Deploy Suricata for network monitoring:

Suricata Installation:

bash

# Install Suricata
apt install -y suricata

# Configure Suricata
cat > /etc/suricata/suricata.yaml << EOF
vars:
  address-groups:
    HOME_NET: "[10.0.0.0/8]"
    EXTERNAL_NET: "!$HOME_NET"
    
af-packet:
  - interface: eth0
    cluster-id: 99
    cluster-type: cluster_flow
    defrag: yes
    
outputs:
  - eve-log:
      enabled: yes
      filetype: regular
      filename: eve.json
      
rule-files:
  - /var/lib/suricata/rules/suricata.rules
  - /var/lib/suricata/rules/emerging-threats.rules
EOF

# Start Suricata
systemctl enable suricata
systemctl start suricata

Step 21: Deploy Web Application Firewall

Install and configure ModSecurity:

ModSecurity Configuration:

bash

# Install ModSecurity
apt install -y libapache2-mod-security2

# Enable ModSecurity
a2enmod security2

# Configure ModSecurity
cat > /etc/modsecurity/modsecurity.conf << EOF
SecRuleEngine On
SecRequestBodyAccess On
SecResponseBodyAccess On
SecResponseBodyMimeType text/plain text/html text/xml application/json
SecDefaultAction "phase:1,log,auditlog,pass"
SecDefaultAction "phase:2,log,auditlog,pass"
EOF

# Install OWASP Core Rule Set
cd /etc/modsecurity
wget https://github.com/coreruleset/coreruleset/archive/v3.3.0.tar.gz
tar -xzf v3.3.0.tar.gz
mv coreruleset-3.3.0 crs
cp crs/crs-setup.conf.example crs/crs-setup.conf

# Enable CRS
echo "Include /etc/modsecurity/crs/crs-setup.conf" >> /etc/modsecurity/modsecurity.conf
echo "Include /etc/modsecurity/crs/rules/*.conf" >> /etc/modsecurity/modsecurity.conf

systemctl restart apache2

5. AI Platform Configuration

5.1 Container Orchestration Setup

Step 22: Install Kubernetes

Deploy Kubernetes cluster for container orchestration:

Master Node Setup:

bash

# Initialize Kubernetes cluster
kubeadm init --pod-network-cidr=10.244.0.0/16 --apiserver-advertise-address=<MASTER_IP>

# Configure kubectl for admin user
mkdir -p $HOME/.kube
cp -i /etc/kubernetes/admin.conf $HOME/.kube/config
chown $(id -u):$(id -g) $HOME/.kube/config

# Install Flannel network plugin
kubectl apply -f https://raw.githubusercontent.com/coreos/flannel/master/Documentation/kube-flannel.yml

# Remove master node taint (if running workloads on master)
kubectl taint nodes --all node-role.kubernetes.io/master-

Worker Node Setup:

bash

# Join worker nodes to cluster (get token from master)
kubeadm join <MASTER_IP>:6443 --token <TOKEN> --discovery-token-ca-cert-hash <HASH>

Step 23: Configure GPU Support

Install NVIDIA GPU support for AI workloads:

GPU Driver Installation:

bash

# Install NVIDIA drivers
apt install -y nvidia-driver-470
reboot

# Install NVIDIA Container Toolkit
distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | apt-key add -
curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | tee /etc/apt/sources.list.d/nvidia-docker.list

apt update
apt install -y nvidia-container-toolkit
systemctl restart docker

# Install NVIDIA Device Plugin for Kubernetes
kubectl apply -f https://raw.githubusercontent.com/NVIDIA/k8s-device-plugin/v0.12.0/nvidia-device-plugin.yml

5.2 AI/ML Framework Deployment

Step 24: Deploy MLflow

Set up MLflow for experiment tracking and model management:

MLflow Deployment:

yaml

# mlflow-deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: mlflow-server
spec:
  replicas: 1
  selector:
    matchLabels:
      app: mlflow-server
  template:
    metadata:
      labels:
        app: mlflow-server
    spec:
      containers:
      - name: mlflow-server
        image: mlflow/mlflow:latest
        ports:
        - containerPort: 5000
        env:
        - name: MLFLOW_BACKEND_STORE_URI
          value: "postgresql://mlflow:password@postgres:5432/mlflow"
        - name: MLFLOW_DEFAULT_ARTIFACT_ROOT
          value: "s3://mlflow-artifacts"
        command:
        - mlflow
        - server
        - --host
        - 0.0.0.0
        - --port
        - "5000"
        - --backend-store-uri
        - $(MLFLOW_BACKEND_STORE_URI)
        - --default-artifact-root
        - $(MLFLOW_DEFAULT_ARTIFACT_ROOT)
---
apiVersion: v1
kind: Service
metadata:
  name: mlflow-service
spec:
  selector:
    app: mlflow-server
  ports:
  - port: 5000
    targetPort: 5000
  type: LoadBalancer

Step 25: Deploy JupyterHub

Set up JupyterHub for data science workflows:

JupyterHub Installation:

bash

# Install JupyterHub
pip install jupyterhub
pip install jupyterlab
pip install dockerspawner

# Configure JupyterHub
cat > /etc/jupyterhub/jupyterhub_config.py << EOF
c.JupyterHub.spawner_class = 'dockerspawner.DockerSpawner'
c.DockerSpawner.image = 'jupyter/datascience-notebook:latest'
c.DockerSpawner.network_name = 'jupyterhub-network'
c.Authenticator.admin_users = {'admin'}
c.JupyterHub.hub_ip = '0.0.0.0'
c.JupyterHub.port = 8000
EOF

# Create systemd service
cat > /etc/systemd/system/jupyterhub.service << EOF
[Unit]
Description=JupyterHub
After=syslog.target network.target

[Service]
User=jupyterhub
ExecStart=/usr/local/bin/jupyterhub -f /etc/jupyterhub/jupyterhub_config.py
Restart=always

[Install]
WantedBy=multi-user.target
EOF

systemctl enable jupyterhub
systemctl start jupyterhub

Step 26: Deploy Kubeflow

Install Kubeflow for ML workflow management:

Kubeflow Installation:

bash

# Install kfctl
wget https://github.com/kubeflow/kfctl/releases/download/v1.2.0/kfctl_v1.2.0-0-gbc038f9_linux.tar.gz
tar -xvf kfctl_v1.2.0-0-gbc038f9_linux.tar.gz
mv kfctl /usr/local/bin/

# Create Kubeflow deployment
export KF_NAME=kubeflow
export BASE_DIR=/opt/kubeflow
export KF_DIR=${BASE_DIR}/${KF_NAME}
export CONFIG_URI="https://raw.githubusercontent.com/kubeflow/manifests/v1.2-branch/kfdef/kfctl_k8s_istio.v1.2.0.yaml"

mkdir -p ${KF_DIR}
cd ${KF_DIR}
kfctl apply -V -f ${CONFIG_URI}

# Wait for deployment to complete
kubectl get pods -n kubeflow

6. Data Management and Storage

6.1 Database Setup

Step 27: Deploy PostgreSQL Cluster

Set up high-availability PostgreSQL for metadata storage:

PostgreSQL HA Configuration:

yaml

# postgresql-ha.yaml
apiVersion: v1
kind: ConfigMap
metadata:
  name: postgres-config
data:
  postgresql.conf: |
    listen_addresses = '*'
    max_connections = 100
    shared_buffers = 128MB
    effective_cache_size = 4GB
    maintenance_work_mem = 64MB
    checkpoint_completion_target = 0.9
    wal_buffers = 16MB
    default_statistics_target = 100
    random_page_cost = 1.1
    effective_io_concurrency = 200
    work_mem = 4MB
    min_wal_size = 80MB
    max_wal_size = 1GB
    max_worker_processes = 8
    max_parallel_workers_per_gather = 4
    max_parallel_workers = 8
    max_parallel_maintenance_workers = 4
    
---
apiVersion: apps/v1
kind: StatefulSet
metadata:
  name: postgres-primary
spec:
  serviceName: postgres-primary
  replicas: 1
  selector:
    matchLabels:
      app: postgres-primary
  template:
    metadata:
      labels:
        app: postgres-primary
    spec:
      containers:
      - name: postgres
        image: postgres:13
        env:
        - name: POSTGRES_DB
          value: "postgres"
        - name: POSTGRES_USER
          value: "postgres"
        - name: POSTGRES_PASSWORD
          value: "SecurePassword123!"
        - name: PGDATA
          value: "/var/lib/postgresql/data/pgdata"
        ports:
        - containerPort: 5432
        volumeMounts:
        - name: postgres-storage
          mountPath: /var/lib/postgresql/data
        - name: config-volume
          mountPath: /etc/postgresql/postgresql.conf
          subPath: postgresql.conf
      volumes:
      - name: config-volume
        configMap:
          name: postgres-config
  volumeClaimTemplates:
  - metadata:
      name: postgres-storage
    spec:
      accessModes: ["ReadWriteOnce"]
      resources:
        requests:
          storage: 100Gi

Step 28: Configure Data Lake Storage

Deploy MinIO for object storage:

MinIO Deployment:

yaml

# minio-deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: minio
spec:
  replicas: 1
  selector:
    matchLabels:
      app: minio
  template:
    metadata:
      labels:
        app: minio
    spec:
      containers:
      - name: minio
        image: minio/minio:latest
        args:
        - server
        - /data
        - --console-address
        - :9090
        env:
        - name: MINIO_ROOT_USER
          value: "admin"
        - name: MINIO_ROOT_PASSWORD
          value: "SecureMinioPassword123!"
        ports:
        - containerPort: 9000
        - containerPort: 9090
        volumeMounts:
        - name: minio-storage
          mountPath: /data
      volumes:
      - name: minio-storage
        persistentVolumeClaim:
          claimName: minio-pvc
---
apiVersion: v1
kind: PersistentVolumeClaim
metadata:
  name: minio-pvc
spec:
  accessModes:
    - ReadWriteOnce
  resources:
    requests:
      storage: 1Ti
---
apiVersion: v1
kind: Service
metadata:
  name: minio-service
spec:
  selector:
    app: minio
  ports:
  - name: api
    port: 9000
    targetPort: 9000
  - name: console
    port: 9090
    targetPort: 9090
  type: LoadBalancer

6.2 Data Pipeline Configuration

Step 29: Deploy Apache Airflow

Set up Airflow for data pipeline orchestration:

Airflow Installation:

bash

# Install Airflow
pip install apache-airflow[celery,postgres,redis,s3]==2.3.0

# Initialize Airflow database
airflow db init

# Create admin user
airflow users create \
  --username admin \
  --firstname Admin \
  --lastname User \
  --role Admin \
  --email admin@example.com \
  --password admin123

# Configure Airflow
cat > /opt/airflow/airflow.cfg << EOF
[core]
dags_folder = /opt/airflow/dags
base_log_folder = /opt/airflow/logs
remote_logging = False
remote_base_log_folder = 
remote_log_conn_id = 
encrypt_s3_logs = False
logging_level = INFO
fab_logging_level = WARN
logging_config_class = 
colored_console_log = True
colored_log_format = [%%(blue)s%%(asctime)s%%(reset)s] {%%(blue)s%%(filename)s:%%(reset)s%%(lineno)d} %%(log_color)s%%(levelname)s%%(reset)s - %%(log_color)s%%(message)s%%(reset)s
colored_formatter_class = airflow.utils.log.colored_log.CustomTTYColoredFormatter
log_format = [%%(asctime)s] {%%(filename)s:%%(lineno)d} %%(levelname)s - %%(message)s
simple_log_format = %%(asctime)s %%(levelname)s - %%(message)s
executor = CeleryExecutor
sql_alchemy_conn = postgresql+psycopg2://airflow:airflow@postgres:5432/airflow
sql_engine_encoding = utf-8
sql_alchemy_pool_enabled = True
sql_alchemy_pool_size = 5
sql_alchemy_max_overflow = 10
sql_alchemy_pool_recycle = 1800
sql_alchemy_pool_pre_ping = True
sql_alchemy_schema = 
parallelism = 32
dag_concurrency = 16
dags_are_paused_at_creation = True
non_pooled_task_slot_count = 128
max_active_runs_per_dag = 16
load_examples = False
plugins_folder = /opt/airflow/plugins
fernet_key = 
donot_pickle = True
dagbag_import_timeout = 30
dagbag_import_error_tracebacks = True
dagbag_import_error_traceback_depth = 2
dag_file_processor_timeout = 50
task_runner = StandardTaskRunner
default_impersonation = 
security = 
unit_test_mode = False
enable_xcom_pickling = True
killed_task_cleanup_time = 60
dag_run_conf_overrides_params = True
dag_discovery_safe_mode = True
default_task_retries = 0
default_task_retry_delay = 300
default_task_weight_rule = downstream
min_serialized_dag_update_interval = 30
min_serialized_dag_fetch_interval = 10
max_serialized_dag_fetch_tries = 5
allowed_deserialization_classes = airflow\..*
store_serialized_dags = False
store_dag_code = False
max_num_rendered_ti_fields_per_task = 30
check_slas = True
xcom_backend = airflow.models.xcom.BaseXCom
dag_ignore_file_syntax = regexp
dag_orientation = LR
dag_default_view = grid
dag_dependencies_view = grid
render_template_as_native_obj = False
default_ui_timezone = UTC
hide_sensitive_var_conn_fields = True
sensitive_var_conn_names = 
default_wrap_method_names = 
EOF

# Create systemd services
cat > /etc/systemd/system/airflow-webserver.service << EOF
[Unit]
Description=Airflow webserver daemon
After=network.target postgresql.service mysql.service redis.service rabbitmq-server.service
Wants=postgresql.service mysql.service redis.service rabbitmq-server.service

[Service]
EnvironmentFile=/etc/sysconfig/airflow
User=airflow
Group=airflow
Type=notify
ExecStart=/usr/local/bin/airflow webserver
Restart=on-failure
RestartSec=5s
PrivateTmp=true

[Install]
WantedBy=multi-user.target
EOF

systemctl enable airflow-webserver
systemctl start airflow-webserver

Step 30: Configure Data Ingestion

Set up data ingestion pipelines using Apache Kafka:

Kafka Cluster Deployment:

yaml

# kafka-cluster.yaml
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
  name: data-cluster
spec:
  kafka:
    version: 3.2.0
    replicas: 3
    listeners:
      - name: plain
        port: 9092
        type: internal
        tls: false
      - name: tls
        port: 9093
        type: internal
        tls: true
      - name: external
        port: 9094
        type: nodeport
        tls: false
    config:
      offsets.topic.replication.factor: 3
      transaction.state.log.replication.factor: 3
      transaction.state.log.min.isr: 2
      default.replication.factor: 3
      min.insync.replicas: 2
      inter.broker.protocol.version: "3.2"
    storage:
      type: jbod
      volumes:
      - id: 0
        type: persistent-claim
        size: 100Gi
        deleteClaim: false
  zookeeper:
    replicas: 3
    storage:
      type: persistent-claim
      size: 10Gi
      deleteClaim: false
  entityOperator:
    topicOperator: {}
    userOperator: {}

Data Ingestion Script:

python

# data_ingestion.py
from kafka import KafkaProducer, KafkaConsumer
import json
import pandas as pd
from datetime import datetime
import logging

class DataIngestor:
    def __init__(self, kafka_servers, topic_name):
        self.kafka_servers = kafka_servers
        self.topic_name = topic_name
        self.producer = KafkaProducer(
            bootstrap_servers=kafka_servers,
            value_serializer=lambda x: json.dumps(x).encode('utf-8')
        )
        
    def ingest_csv_data(self, csv_file_path):
        """Ingest data from CSV file"""
        try:
            df = pd.read_csv(csv_file_path)
            for index, row in df.iterrows():
                message = {
                    'timestamp': datetime.now().isoformat(),
                    'data': row.to_dict(),
                    'source': csv_file_path
                }
                self.producer.send(self.topic_name, value=message)
            self.producer.flush()
            logging.info(f"Successfully ingested {len(df)} records from {csv_file_path}")
        except Exception as e:
            logging.error(f"Error ingesting data: {str(e)}")
            
    def ingest_api_data(self, api_endpoint):
        """Ingest data from API endpoint"""
        try:
            response = requests.get(api_endpoint)
            if response.status_code == 200:
                data = response.json()
                message = {
                    'timestamp': datetime.now().isoformat(),
                    'data': data,
                    'source': api_endpoint
                }
                self.producer.send(self.topic_name, value=message)
                self.producer.flush()
                logging.info(f"Successfully ingested API data from {api_endpoint}")
        except Exception as e:
            logging.error(f"Error ingesting API data: {str(e)}")

# Usage example
if __name__ == "__main__":
    ingestor = DataIngestor(['kafka-broker:9092'], 'government-data')
    ingestor.ingest_csv_data('/data/census_data.csv')

7. Compliance and Governance

7.1 Data Governance Framework

Step 31: Implement Data Classification

Create data classification policies and automated tagging:

Data Classification Policy:

yaml

# data-classification-policy.yaml
apiVersion: v1
kind: ConfigMap
metadata:
  name: data-classification-policy
data:
  policy.yaml: |
    classification_levels:
      - name: "OFFICIAL"
        description: "Standard business information"
        retention_days: 2555  # 7 years
        encryption_required: false
        access_controls:
          - "authenticated_users"
      - name: "OFFICIAL:Sensitive"
        description: "Personal or sensitive business information"
        retention_days: 2555  # 7 years
        encryption_required: true
        access_controls:
          - "authorized_personnel"
      - name: "PROTECTED"
        description: "Information that could cause damage if disclosed"
        retention_days: 3650  # 10 years
        encryption_required: true
        access_controls:
          - "cleared_personnel"
      - name: "SECRET"
        description: "Information that could cause serious damage if disclosed"
        retention_days: 7300  # 20 years
        encryption_required: true
        access_controls:
          - "security_cleared"
    
    auto_classification_rules:
      - pattern: ".*ssn.*|.*tax.*|.*medicare.*"
        classification: "OFFICIAL:Sensitive"
      - pattern: ".*classified.*|.*confidential.*"
        classification: "PROTECTED"
      - pattern: ".*secret.*|.*national.*security.*"
        classification: "SECRET"

Data Classification Service:

python

# data_classifier.py
import re
import yaml
from typing import Dict, List, Optional

class DataClassifier:
    def __init__(self, policy_config_path: str):
        with open(policy_config_path, 'r') as f:
            self.policy = yaml.safe_load(f)
        
    def classify_data(self, data: Dict, metadata: Dict = None) -> str:
        """Classify data based on content and metadata"""
        content_str = str(data).lower()
        
        # Check auto-classification rules
        for rule in self.policy['auto_classification_rules']:
            if re.search(rule['pattern'], content_str):
                return rule['classification']
        
        # Default classification
        return "OFFICIAL"
    
    def get_retention_policy(self, classification: str) -> Dict:
        """Get retention policy for classification level"""
        for level in self.policy['classification_levels']:
            if level['name'] == classification:
                return {
                    'retention_days': level['retention_days'],
                    'encryption_required': level['encryption_required'],
                    'access_controls': level['access_controls']
                }
        return None
    
    def apply_data_controls(self, data_id: str, classification: str):
        """Apply data controls based on classification"""
        policy = self.get_retention_policy(classification)
        if policy:
            # Apply encryption if required
            if policy['encryption_required']:
                self.encrypt_data(data_id)
            
            # Set retention schedule
            self.set_retention_schedule(data_id, policy['retention_days'])
            
            # Configure access controls
            self.configure_access_controls(data_id, policy['access_controls'])
    
    def encrypt_data(self, data_id: str):
        """Encrypt data using AES-256"""
        # Implementation for data encryption
        pass
    
    def set_retention_schedule(self, data_id: str, retention_days: int):
        """Set automatic deletion schedule"""
        # Implementation for retention scheduling
        pass
    
    def configure_access_controls(self, data_id: str, access_controls: List[str]):
        """Configure RBAC for data access"""
        # Implementation for access control configuration
        pass

Step 32: Deploy Data Loss Prevention

Implement DLP using OpenDLP or similar tools:

DLP Configuration:

bash

# Install OpenDLP
git clone https://github.com/opendlp/opendlp.git
cd opendlp
./configure
make install

# Configure DLP policies
cat > /etc/opendlp/dlp-policies.conf << EOF
# Australian Privacy Act compliance
policy "australian_privacy" {
    name = "Australian Privacy Act Compliance"
    description = "Detect personal information under Australian Privacy Act"
    
    rules = [
        {
            name = "medicare_number"
            pattern = "[0-9]{10}\\s[0-9]"
            severity = "high"
            action = "block"
        },
        {
            name = "tax_file_number"
            pattern = "[0-9]{3}\\s[0-9]{3}\\s[0-9]{3}"
            severity = "high" 
            action = "block"
        },
        {
            name = "drivers_license"
            pattern = "[A-Z]{2}[0-9]{6,8}"
            severity = "medium"
            action = "alert"
        },
        {
            name = "email_address"
            pattern = "[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\\.[a-zA-Z]{2,}"
            severity = "low"
            action = "log"
        }
    ]
}

# Government classification markings
policy "classification_markings" {
    name = "Government Classification Markings"
    description = "Detect government classification markings"
    
    rules = [
        {
            name = "secret_marking"
            pattern = "SECRET|CONFIDENTIAL|TOP\\sSECRET"
            severity = "critical"
            action = "block"
        },
        {
            name = "protected_marking" 
            pattern = "PROTECTED|OFFICIAL:Sensitive"
            severity = "high"
            action = "encrypt"
        }
    ]
}
EOF

# Start DLP service
systemctl enable opendlp
systemctl start opendlp

7.2 Audit and Compliance Monitoring

Step 33: Deploy Audit Logging System

Set up comprehensive audit logging using ELK stack:

Elasticsearch Deployment:

yaml

# elasticsearch.yaml
apiVersion: apps/v1
kind: StatefulSet
metadata:
  name: elasticsearch
spec:
  serviceName: elasticsearch
  replicas: 3
  selector:
    matchLabels:
      app: elasticsearch
  template:
    metadata:
      labels:
        app: elasticsearch
    spec:
      containers:
      - name: elasticsearch
        image: docker.elastic.co/elasticsearch/elasticsearch:8.5.0
        ports:
        - containerPort: 9200
        - containerPort: 9300
        env:
        - name: cluster.name
          value: "audit-cluster"
        - name: node.name
          valueFrom:
            fieldRef:
              fieldPath: metadata.name
        - name: discovery.seed_hosts
          value: "elasticsearch-0.elasticsearch,elasticsearch-1.elasticsearch,elasticsearch-2.elasticsearch"
        - name: cluster.initial_master_nodes
          value: "elasticsearch-0,elasticsearch-1,elasticsearch-2"
        - name: ES_JAVA_OPTS
          value: "-Xms1g -Xmx1g"
        - name: xpack.security.enabled
          value: "true"
        - name: xpack.security.transport.ssl.enabled
          value: "true"
        - name: xpack.security.http.ssl.enabled
          value: "true"
        volumeMounts:
        - name: elasticsearch-storage
          mountPath: /usr/share/elasticsearch/data
        resources:
          limits:
            memory: 2Gi
            cpu: 1000m
          requests:
            memory: 2Gi
            cpu: 1000m
  volumeClaimTemplates:
  - metadata:
      name: elasticsearch-storage
    spec:
      accessModes: ["ReadWriteOnce"]
      resources:
        requests:
          storage: 100Gi

Logstash Configuration:

yaml

# logstash-config.yaml
apiVersion: v1
kind: ConfigMap
metadata:
  name: logstash-config
data:
  logstash.conf: |
    input {
      beats {
        port => 5044
      }
      syslog {
        port => 514
      }
      http {
        port => 8080
        codec => json
      }
    }
    
    filter {
      if [fields][log_type] == "audit" {
        mutate {
          add_tag => ["audit"]
        }
        
        # Parse audit logs
        grok {
          match => { "message" => "%{TIMESTAMP_ISO8601:timestamp} %{LOGLEVEL:level} %{GREEDYDATA:audit_message}" }
        }
        
        # Extract user information
        if [audit_message] =~ /user=/ {
          grok {
            match => { "audit_message" => "user=%{USERNAME:audit_user}" }
          }
        }
        
        # Extract action information
        if [audit_message] =~ /action=/ {
          grok {
            match => { "audit_message" => "action=%{WORD:audit_action}" }
          }
        }
        
        # Add compliance tags
        if [audit_action] in ["login", "logout", "access", "modify", "delete"] {
          mutate {
            add_tag => ["privacy_act"]
          }
        }
      }
      
      # Government data classification
      if [message] =~ /PROTECTED|SECRET|CONFIDENTIAL/ {
        mutate {
          add_tag => ["classified"]
        }
      }
      
      # Add geolocation for IP addresses
      if [client_ip] {
        geoip {
          source => "client_ip"
          target => "geoip"
        }
      }
      
      # Enrich with threat intelligence
      if [client_ip] {
        translate {
          source => "client_ip"
          target => "threat_intel"
          dictionary_path => "/etc/logstash/threat_intel.yml"
          fallback => "clean"
        }
      }
    }
    
    output {
      elasticsearch {
        hosts => ["elasticsearch:9200"]
        index => "audit-logs-%{+YYYY.MM.dd}"
        template_name => "audit-template"
        template => "/etc/logstash/audit-template.json"
        template_overwrite => true
      }
      
      # Send critical alerts to SIEM
      if "critical" in [tags] or "classified" in [tags] {
        http {
          url => "https://siem.gov.au/api/alerts"
          http_method => "post"
          format => "json"
          headers => {
            "Authorization" => "Bearer ${SIEM_API_TOKEN}"
          }
        }
      }
      
      # Backup to long-term storage
      s3 {
        access_key_id => "${AWS_ACCESS_KEY}"
        secret_access_key => "${AWS_SECRET_KEY}"
        region => "ap-southeast-2"
        bucket => "audit-logs-backup"
        prefix => "logs/%{+YYYY/MM/dd}/"
        time_file => 60
      }
    }

Step 34: Implement Compliance Reporting

Create automated compliance reports:

Compliance Reporting Service:

python

# compliance_reporter.py
import pandas as pd
from elasticsearch import Elasticsearch
from datetime import datetime, timedelta
import jinja2
import smtplib
from email.mime.multipart import MIMEMultipart
from email.mime.text import MIMEText
from email.mime.application import MIMEApplication

class ComplianceReporter:
    def __init__(self, es_host, es_port=9200):
        self.es = Elasticsearch([{'host': es_host, 'port': es_port}])
        self.template_env = jinja2.Environment(
            loader=jinja2.FileSystemLoader('templates/')
        )
    
    def generate_privacy_act_report(self, start_date, end_date):
        """Generate Privacy Act compliance report"""
        query = {
            "query": {
                "bool": {
                    "must": [
                        {"range": {"@timestamp": {"gte": start_date, "lte": end_date}}},
                        {"terms": {"tags": ["privacy_act"]}}
                    ]
                }
            },
            "aggs": {
                "by_action": {
                    "terms": {"field": "audit_action.keyword"}
                },
                "by_user": {
                    "terms": {"field": "audit_user.keyword", "size": 100}
                },
                "by_classification": {
                    "terms": {"field": "data_classification.keyword"}
                }
            }
        }
        
        result = self.es.search(index="audit-logs-*", body=query)
        
        report_data = {
            'report_period': f"{start_date} to {end_date}",
            'total_events': result['hits']['total']['value'],
            'actions': result['aggregations']['by_action']['buckets'],
            'users': result['aggregations']['by_user']['buckets'],
            'classifications': result['aggregations']['by_classification']['buckets'],
            'generated_at': datetime.now().isoformat()
        }
        
        return report_data
    
    def generate_security_report(self, start_date, end_date):
        """Generate security incident report"""
        query = {
            "query": {
                "bool": {
                    "must": [
                        {"range": {"@timestamp": {"gte": start_date, "lte": end_date}}},
                        {"terms": {"level": ["ERROR", "CRITICAL", "ALERT"]}}
                    ]
                }
            },
            "aggs": {
                "by_severity": {
                    "terms": {"field": "level.keyword"}
                },
                "by_source": {
                    "terms": {"field": "source.keyword"}
                },
                "security_events": {
                    "filter": {
                        "terms": {"tags": ["security", "authentication", "authorization"]}
                    },
                    "aggs": {
                        "by_event_type": {
                            "terms": {"field": "event_type.keyword"}
                        }
                    }
                }
            }
        }
        
        result = self.es.search(index="audit-logs-*", body=query)
        
        report_data = {
            'report_period': f"{start_date} to {end_date}",
            'total_incidents': result['hits']['total']['value'],
            'by_severity': result['aggregations']['by_severity']['buckets'],
            'by_source': result['aggregations']['by_source']['buckets'],
            'security_events': result['aggregations']['security_events']['by_event_type']['buckets'],
            'generated_at': datetime.now().isoformat()
        }
        
        return report_data
    
    def generate_html_report(self, report_data, template_name):
        """Generate HTML report from template"""
        template = self.template_env.get_template(template_name)
        return template.render(report_data)
    
    def send_report(self, report_html, recipients, subject):
        """Send report via email"""
        msg = MIMEMultipart()
        msg['From'] = 'compliance@gov.au'
        msg['To'] = ', '.join(recipients)
        msg['Subject'] = subject
        
        msg.attach(MIMEText(report_html, 'html'))
        
        # Add CSV attachment
        csv_data = self.generate_csv_report(report_data)
        csv_attachment = MIMEApplication(csv_data)
        csv_attachment.add_header('Content-Disposition', 'attachment', filename='compliance_report.csv')
        msg.attach(csv_attachment)
        
        # Send email
        with smtplib.SMTP('smtp.gov.au', 587) as server:
            server.starttls()
            server.login('compliance@gov.au', 'password')
            server.send_message(msg)

# Automated report generation
if __name__ == "__main__":
    reporter = ComplianceReporter('elasticsearch.gov.au')
    
    # Generate weekly reports
    end_date = datetime.now()
    start_date = end_date - timedelta(days=7)
    
    # Privacy Act compliance report
    privacy_report = reporter.generate_privacy_act_report(
        start_date.isoformat(),
        end_date.isoformat()
    )
    
    privacy_html = reporter.generate_html_report(privacy_report, 'privacy_report.html')
    reporter.send_report(
        privacy_html,
        ['privacy.officer@gov.au', 'cio@gov.au'],
        'Weekly Privacy Act Compliance Report'
    )
    
    # Security incident report
    security_report = reporter.generate_security_report(
        start_date.isoformat(),
        end_date.isoformat()
    )
    
    security_html = reporter.generate_html_report(security_report, 'security_report.html')
    reporter.send_report(
        security_html,
        ['security.officer@gov.au', 'ciso@gov.au'],
        'Weekly Security Incident Report'
    )

8. Monitoring and Operations

8.1 Infrastructure Monitoring

Step 35: Deploy Prometheus and Grafana

Set up comprehensive monitoring stack:

Prometheus Configuration:

yaml

# prometheus-config.yaml
apiVersion: v1
kind: ConfigMap
metadata:
  name: prometheus-config
data:
  prometheus.yml: |
    global:
      scrape_interval: 15s
      evaluation_interval: 15s
    
    rule_files:
      - "alert_rules.yml"
    
    alerting:
      alertmanagers:
        - static_configs:
            - targets:
              - alertmanager:9093
    
    scrape_configs:
      - job_name: 'prometheus'
        static_configs:
          - targets: ['localhost:9090']
      
      - job_name: 'kubernetes-pods'
        kubernetes_sd_configs:
          - role: pod
        relabel_configs:
          - source_labels: [__meta_kubernetes_pod_annotation_prometheus_io_scrape]
            action: keep
            regex: true
          - source_labels: [__meta_kubernetes_pod_annotation_prometheus_io_path]
            action: replace
            target_label: __metrics_path__
            regex: (.+)
          - source_labels: [__address__, __meta_kubernetes_pod_annotation_prometheus_io_port]
            action: replace
            regex: ([^:]+)(?::\d+)?;(\d+)
            replacement: $1:$2
            target_label: __address__
      
      - job_name: 'kubernetes-nodes'
        kubernetes_sd_configs:
          - role: node
        relabel_configs:
          - action: labelmap
            regex: __meta_kubernetes_node_label_(.+)
      
      - job_name: 'gpu-metrics'
        static_configs:
          - targets: ['nvidia-dcgm-exporter:9400']
      
      - job_name: 'minio-metrics'
        static_configs:
          - targets: ['minio:9000']
        metrics_path: /minio/v2/metrics/cluster
      
      - job_name: 'postgres-metrics'
        static_configs:
          - targets: ['postgres-exporter:9187']
  
  alert_rules.yml: |
    groups:
    - name: infrastructure
      rules:
      - alert: HighCPUUsage
        expr: 100 - (avg by(instance) (irate(node_cpu_seconds_total{mode="idle"}[5m])) * 100) > 80
        for: 5m
        labels:
          severity: warning
        annotations:
          summary: "High CPU usage detected"
          description: "CPU usage is above 80% for instance {{ $labels.instance }}"
      
      - alert: HighMemoryUsage
        expr: (node_memory_MemTotal_bytes - node_memory_MemAvailable_bytes) / node_memory_MemTotal_bytes * 100 > 90
        for: 5m
        labels:
          severity: critical
        annotations:
          summary: "High memory usage detected"
          description: "Memory usage is above 90% for instance {{ $labels.instance }}"
      
      - alert: DiskSpaceLow
        expr: (node_filesystem_avail_bytes{mountpoint="/"} / node_filesystem_size_bytes{mountpoint="/"}) * 100 < 10
        for: 5m
        labels:
          severity: critical
        annotations:
          summary: "Disk space is running low"
          description: "Disk space is below 10% for instance {{ $labels.instance }}"
      
      - alert: PodCrashLooping
        expr: rate(kube_pod_container_status_restarts_total[15m]) > 0
        for: 5m
        labels:
          severity: warning
        annotations:
          summary: "Pod is crash looping"
          description: "Pod {{ $labels.pod }} in namespace {{ $labels.namespace }} is crash looping"
      
      - alert: GPUTemperatureHigh
        expr: DCGM_FI_DEV_GPU_TEMP > 80
        for: 5m
        labels:
          severity: warning
        annotations:
          summary: "GPU temperature is high"
          description: "GPU {{ $labels.gpu }} temperature is above 80°C"

Grafana Dashboard Configuration:

json

{
  "dashboard": {
    "id": null,
    "title": "Sovereign AI Cloud Overview",
    "tags": ["kubernetes", "ai", "government"],
    "timezone": "Australia/Sydney",
    "panels": [
      {
        "id": 1,
        "title": "Cluster Resource Usage",
        "type": "stat",
        "targets": [
          {
            "expr": "sum(kube_node_status_capacity{resource=\"cpu\"})",
            "legendFormat": "Total CPU Cores"
          },
          {
            "expr": "sum(kube_node_status_capacity{resource=\"memory\"}) / 1024 / 1024 / 1024",
            "legendFormat": "Total Memory (GB)"
          }
        ],
        "gridPos": {"h": 8, "w": 12, "x": 0, "y": 0}
      },
      {
        "id": 2,
        "title": "AI Workload Performance",
        "type": "graph",
        "targets": [
          {
            "expr": "rate(container_cpu_usage_seconds_total{namespace=\"kubeflow\"}[5m])",
            "legendFormat": "CPU Usage - {{pod}}"
          },
          {
            "expr": "container_memory_usage_bytes{namespace=\"kubeflow\"} / 1024 / 1024",
            "legendFormat": "Memory Usage (MB) - {{pod}}"
          }
        ],
        "gridPos": {"h": 8, "w": 12, "x": 12, "y": 0}
      },
      {
        "id": 3,
        "title": "GPU Utilization",
        "type": "graph",
        "targets": [
          {
            "expr": "DCGM_FI_DEV_GPU_UTIL",
            "legendFormat": "GPU {{gpu}} Utilization %"
          }
        ],
        "gridPos": {"h": 8, "w": 24, "x": 0, "y": 8}
      },
      {
        "id": 4,
        "title": "Data Storage Usage",
        "type": "graph",
        "targets": [
          {
            "expr": "minio_cluster_usage_total_bytes / 1024 / 1024 / 1024",
            "legendFormat": "MinIO Storage Used (GB)"
          },
          {
            "expr": "pg_stat_database_size{datname=\"postgres\"} / 1024 / 1024",
            "legendFormat": "PostgreSQL Database Size (MB)"
          }
        ],
        "gridPos": {"h": 8, "w": 12, "x": 0, "y": 16}
      },
      {
        "id": 5,
        "title": "Network Traffic",
        "type": "graph",
        "targets": [
          {
            "expr": "rate(container_network_receive_bytes_total[5m]) / 1024 / 1024",
            "legendFormat": "Network In (MB/s) - {{pod}}"
          },
          {
            "expr": "rate(container_network_transmit_bytes_total[5m]) / 1024 / 1024",
            "legendFormat": "Network Out (MB/s) - {{pod}}"
          }
        ],
        "gridPos": {"h": 8, "w": 12, "x": 12, "y": 16}
      }
    ],
    "time": {
      "from": "now-1h",
      "to": "now"
    },
    "refresh": "5s"
  }
}

Step 36: Configure Application Performance Monitoring

Deploy APM for AI application monitoring:

APM Configuration with Elastic APM:

yaml

# apm-server.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: apm-server
spec:
  replicas: 2
  selector:
    matchLabels:
      app: apm-server
  template:
    metadata:
      labels:
        app: apm-server
    spec:
      containers:
      - name: apm-server
        image: docker.elastic.co/apm/apm-server:8.5.0
        ports:
        - containerPort: 8200
        env:
        - name: output.elasticsearch.hosts
          value: "elasticsearch:9200"
        - name: apm-server.host
          value: "0.0.0.0:8200"
        - name: apm-server.secret_token
          value: "your-secret-token"
        volumeMounts:
        - name: config-volume
          mountPath: /usr/share/apm-server/apm-server.yml
          subPath: apm-server.yml
      volumes:
      - name: config-volume
        configMap:
          name: apm-server-config
---
apiVersion: v1
kind: ConfigMap
metadata:
  name: apm-server-config
data:
  apm-server.yml: |
    apm-server:
      host: "0.0.0.0:8200"
      secret_token: "your-secret-token"
      
    output.elasticsearch:
      hosts: ["elasticsearch:9200"]
      
    setup.kibana:
      host: "kibana:5601"
      
    logging.level: info
    logging.to_files: true
    logging.files:
      path: /var/log/apm-server
      name: apm-server
      keepfiles: 7
      permissions: 0644

AI Application Instrumentation:

python

# ml_model_monitoring.py
from elasticapm import Client
import time
import logging
import numpy as np
from sklearn.metrics import accuracy_score, precision_score, recall_score

class MLModelMonitor:
    def __init__(self, service_name, apm_server_url, secret_token):
        self.apm_client = Client({
            'SERVICE_NAME': service_name,
            'SERVER_URL': apm_server_url,
            'SECRET_TOKEN': secret_token,
            'ENVIRONMENT': 'production'
        })
        
    def monitor_prediction(self, model_name, input_data, prediction, actual=None):
        """Monitor model prediction performance"""
        with self.amp_client.capture_span(
            name=f"ml_prediction_{model_name}",
            span_type="ml.prediction"
        ) as span:
            start_time = time.time()
            
            # Add custom labels for monitoring
            span.label('model_name', model_name)
            span.label('input_size', len(str(input_data)))
            span.label('prediction_value', str(prediction))
            
            # Calculate inference time
            inference_time = time.time() - start_time
            span.label('inference_time_ms', inference_time * 1000)
            
            # Monitor prediction quality if actual value is available
            if actual is not None:
                accuracy = 1 if prediction == actual else 0
                span.label('prediction_accuracy', accuracy)
                
                # Log prediction quality metrics
                self.apm_client.capture_message(
                    message=f"Model {model_name} prediction accuracy: {accuracy}",
                    level="info",
                    custom={
                        'model_name': model_name,
                        'prediction': prediction,
                        'actual': actual,
                        'inference_time': inference_time
                    }
                )
            
            return prediction
    
    def monitor_batch_predictions(self, model_name, predictions, actuals):
        """Monitor batch prediction performance metrics"""
        try:
            # Calculate comprehensive metrics
            accuracy = accuracy_score(actuals, predictions)
            precision = precision_score(actuals, predictions, average='weighted')
            recall = recall_score(actuals, predictions, average='weighted')
            
            # Send metrics to APM
            self.apm_client.capture_message(
                message=f"Batch prediction metrics for {model_name}",
                level="info",
                custom={
                    'model_name': model_name,
                    'batch_size': len(predictions),
                    'accuracy': accuracy,
                    'precision': precision,
                    'recall': recall,
                    'timestamp': time.time()
                }
            )
            
            # Alert if performance degrades
            if accuracy < 0.8:  # Threshold for acceptable accuracy
                self.apm_client.capture_message(
                    message=f"Model {model_name} accuracy below threshold: {accuracy}",
                    level="warning",
                    custom={
                        'model_name': model_name,
                        'accuracy': accuracy,
                        'threshold': 0.8
                    }
                )
                
        except Exception as e:
            self.apm_client.capture_exception()
            logging.error(f"Error monitoring batch predictions: {str(e)}")

# Example usage in ML application
class GovernmentAIService:
    def __init__(self):
        self.monitor = MLModelMonitor(
            service_name="government-ai-service",
            apm_server_url="http://apm-server:8200",
            secret_token="your-secret-token"
        )
        
    def predict_citizen_service_category(self, inquiry_text):
        """Predict the appropriate government service category for citizen inquiry"""
        # This would be your actual ML model prediction logic
        prediction = self.ml_model.predict(inquiry_text)
        
        # Monitor the prediction
        monitored_prediction = self.monitor.monitor_prediction(
            model_name="service_categorization_model",
            input_data=inquiry_text,
            prediction=prediction
        )
        
        return monitored_prediction

8.2 Security Monitoring

Step 37: Deploy Security Information and Event Management (SIEM)

Understanding SIEM implementation is crucial for maintaining security oversight in your sovereign AI cloud. Think of SIEM as the central nervous system of your security infrastructure - it collects, correlates, and analyzes security events from across your entire environment to detect threats and ensure compliance.

The key concept here is that modern cyber threats are sophisticated and often involve multiple attack vectors across different systems. A SIEM solution provides the comprehensive visibility needed to connect these dots and identify patterns that might indicate a security incident.

SIEM Configuration with Wazuh:

yaml

# wazuh-manager.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: wazuh-manager
  namespace: security
spec:
  replicas: 1
  selector:
    matchLabels:
      app: wazuh-manager
  template:
    metadata:
      labels:
        app: wazuh-manager
    spec:
      containers:
      - name: wazuh-manager
        image: wazuh/wazuh-manager:4.4.0
        ports:
        - containerPort: 1514  # Agent communication
        - containerPort: 1515  # Agent enrollment
        - containerPort: 514   # Syslog
        - containerPort: 55000 # API
        env:
        - name: WAZUH_MANAGER_IP
          valueFrom:
            fieldRef:
              fieldPath: status.podIP
        volumeMounts:
        - name: wazuh-config
          mountPath: /wazuh-config-mount/etc/ossec.conf
          subPath: ossec.conf
        - name: wazuh-rules
          mountPath: /wazuh-config-mount/etc/rules/
        - name: wazuh-data
          mountPath: /var/ossec/data
        resources:
          requests:
            memory: "1Gi"
            cpu: "500m"
          limits:
            memory: "2Gi"
            cpu: "1000m"
      volumes:
      - name: wazuh-config
        configMap:
          name: wazuh-config
      - name: wazuh-rules
        configMap:
          name: wazuh-rules
      - name: wazuh-data
        persistentVolumeClaim:
          claimName: wazuh-data-pvc

Let me explain the security monitoring approach step by step. First, we need to understand that security monitoring in a government environment requires multiple layers of detection. The Wazuh SIEM acts as our central collection point, but it needs specific rules tailored to government security requirements.

Custom Security Rules for Government Environment:

xml

<!-- government_security_rules.xml -->
<group name="government_compliance,">
  <!-- Privacy Act Violations -->
  <rule id="100001" level="12">
    <if_group>web,</if_group>
    <match>personal_information|medicare|tax_file_number|drivers_license</match>
    <description>Potential Privacy Act violation - Personal information accessed</description>
    <group>privacy_act,compliance,</group>
  </rule>

  <!-- Classified Information Access -->
  <rule id="100002" level="15">
    <if_group>authentication,</if_group>
    <match>PROTECTED|SECRET|CONFIDENTIAL</match>
    <description>Access to classified information detected</description>
    <group>classification,security_clearance,</group>
  </rule>

  <!-- Unusual Data Access Patterns -->
  <rule id="100003" level="10" frequency="10" timeframe="300">
    <if_matched_sid>100001</if_matched_sid>
    <description>Multiple privacy-sensitive data access attempts in short timeframe</description>
    <group>privacy_act,suspicious_activity,</group>
  </rule>

  <!-- Failed Security Clearance Authentication -->
  <rule id="100004" level="8" frequency="3" timeframe="180">
    <if_group>authentication_failed,</if_group>
    <match>security_clearance_required</match>
    <description>Multiple failed attempts to access security clearance required resources</description>
    <group>authentication,security_clearance,</group>
  </rule>

  <!-- Data Exfiltration Indicators -->
  <rule id="100005" level="12">
    <if_group>network,</if_group>
    <match>large_data_transfer|bulk_download|export</match>
    <field name="data_size">^[5-9][0-9]{7,}|[1-9][0-9]{8,}</field> <!-- >50MB -->
    <description>Large data transfer detected - potential data exfiltration</description>
    <group>data_exfiltration,dLP,</group>
  </rule>

  <!-- AI Model Access Monitoring -->
  <rule id="100006" level="8">
    <if_group>ai_model,</if_group>
    <match>model_download|weights_access|training_data_access</match>
    <description>AI model or training data access detected</description>
    <group>ai_security,intellectual_property,</group>
  </rule>
</group>

The beauty of this rule configuration lies in its ability to detect patterns specific to government operations. Notice how we're not just looking for generic security events, but for activities that could indicate violations of Australian privacy laws or unauthorized access to classified information.

Step 38: Implement Threat Intelligence Integration

Threat intelligence integration transforms your security monitoring from reactive to proactive. Instead of only detecting known attacks, you're now equipped to identify emerging threats and attack patterns that might target government infrastructure specifically.

Threat Intelligence Feed Integration:

python

# threat_intelligence.py
import requests
import json
import time
from datetime import datetime, timedelta
import hashlib
import logging
from typing import Dict, List, Optional

class ThreatIntelligenceManager:
    """
    Manages threat intelligence feeds and integrates them with security monitoring.
    This class demonstrates how to consume threat intelligence and apply it to
    your security monitoring pipeline.
    """
    
    def __init__(self, feeds_config: Dict, wazuh_api_url: str, api_key: str):
        self.feeds_config = feeds_config
        self.wazuh_api_url = wazuh_api_url
        self.api_key = api_key
        self.threat_indicators = {}
        
        # Initialize logging for threat intelligence activities
        logging.basicConfig(level=logging.INFO)
        self.logger = logging.getLogger(__name__)
    
    def fetch_government_threat_feeds(self):
        """
        Fetch threat intelligence specifically relevant to government infrastructure.
        This includes APT groups known to target government, nation-state indicators,
        and government-specific vulnerabilities.
        """
        try:
            # Australian Cyber Security Centre (ACSC) threat feed
            acsc_feed = self._fetch_acsc_indicators()
            
            # US-CERT government indicators
            uscert_feed = self._fetch_uscert_indicators()
            
            # Commercial government-focused threat intel
            commercial_feed = self._fetch_commercial_indicators()
            
            # Combine and deduplicate indicators
            all_indicators = {
                **acsc_feed,
                **uscert_feed, 
                **commercial_feed
            }
            
            self.threat_indicators = all_indicators
            self.logger.info(f"Updated threat intelligence with {len(all_indicators)} indicators")
            
            return all_indicators
            
        except Exception as e:
            self.logger.error(f"Error fetching threat intelligence: {str(e)}")
            return {}
    
    def _fetch_acsc_indicators(self) -> Dict:
        """Fetch indicators from Australian Cyber Security Centre"""
        # Note: This would integrate with actual ACSC feeds when available
        # For now, we simulate the structure
        indicators = {}
        
        try:
            # Government-specific APT indicators
            apt_indicators = {
                "apt1_government": {
                    "ips": ["192.168.100.1", "10.0.50.25"],
                    "domains": ["govt-fake-portal.com", "tax-office-fake.org"],
                    "hashes": ["d41d8cd98f00b204e9800998ecf8427e"],
                    "tactics": ["credential_harvesting", "data_exfiltration"],
                    "severity": "high",
                    "description": "APT group targeting Australian government agencies"
                }
            }
            
            indicators.update(apt_indicators)
            
            # Government service impersonation indicators
            impersonation_indicators = {
                "govt_impersonation": {
                    "domains": ["fake-centrelink.com", "fraudulent-ato.org"],
                    "keywords": ["urgent tax notice", "government benefit suspended"],
                    "severity": "medium",
                    "description": "Domains impersonating Australian government services"
                }
            }
            
            indicators.update(impersonation_indicators)
            
        except Exception as e:
            self.logger.error(f"Error fetching ACSC indicators: {str(e)}")
        
        return indicators
    
    def _fetch_uscert_indicators(self) -> Dict:
        """Fetch US-CERT indicators relevant to government infrastructure"""
        indicators = {}
        
        try:
            # Example structure for US-CERT integration
            # In production, this would connect to actual US-CERT STIX/TAXII feeds
            uscert_indicators = {
                "nation_state_apt": {
                    "ips": ["203.0.113.5", "198.51.100.10"],
                    "user_agents": ["GovBot/1.0", "OfficialCrawler/2.1"],
                    "techniques": ["T1566.001", "T1078.004"],  # MITRE ATT&CK techniques
                    "severity": "critical",
                    "description": "Nation state actors targeting government infrastructure"
                }
            }
            
            indicators.update(uscert_indicators)
            
        except Exception as e:
            self.logger.error(f"Error fetching US-CERT indicators: {str(e)}")
        
        return indicators
    
    def _fetch_commercial_indicators(self) -> Dict:
        """Fetch commercial threat intelligence focused on government targets"""
        indicators = {}
        
        try:
            # This would integrate with commercial threat intel providers
            # like Recorded Future, ThreatConnect, etc.
            commercial_indicators = {
                "government_targeted_malware": {
                    "file_hashes": ["e3b0c44298fc1c149afbf4c8996fb924"],
                    "registry_keys": ["HKLM\\Software\\GovMalware"],
                    "network_signatures": ["POST /api/exfiltrate"],
                    "severity": "high",
                    "description": "Malware specifically designed to target government networks"
                }
            }
            
            indicators.update(commercial_indicators)
            
        except Exception as e:
            self.logger.error(f"Error fetching commercial indicators: {str(e)}")
        
        return indicators
    
    def update_wazuh_rules(self, indicators: Dict):
        """
        Update Wazuh rules with new threat intelligence indicators.
        This creates dynamic rules based on current threat intelligence.
        """
        try:
            for threat_name, threat_data in indicators.items():
                # Create IP-based rules
                if 'ips' in threat_data:
                    self._create_ip_rules(threat_name, threat_data['ips'], threat_data.get('severity', 'medium'))
                
                # Create domain-based rules  
                if 'domains' in threat_data:
                    self._create_domain_rules(threat_name, threat_data['domains'], threat_data.get('severity', 'medium'))
                
                # Create hash-based rules
                if 'hashes' in threat_data or 'file_hashes' in threat_data:
                    hashes = threat_data.get('hashes', threat_data.get('file_hashes', []))
                    self._create_hash_rules(threat_name, hashes, threat_data.get('severity', 'medium'))
                
                self.logger.info(f"Updated Wazuh rules for threat: {threat_name}")
                
        except Exception as e:
            self.logger.error(f"Error updating Wazuh rules: {str(e)}")
    
    def _create_ip_rules(self, threat_name: str, ips: List[str], severity: str):
        """Create Wazuh rules for malicious IP addresses"""
        severity_level = {'low': 5, 'medium': 8, 'high': 12, 'critical': 15}.get(severity, 8)
        
        rule_xml = f"""
        <rule id="{self._generate_rule_id()}" level="{severity_level}">
            <if_group>network,</if_group>
            <srcip>{"|".join(ips)}</srcip>
            <description>Connection from known threat IP - {threat_name}</description>
            <group>threat_intelligence,{threat_name},</group>
        </rule>
        """
        
        self._deploy_rule_to_wazuh(rule_xml)
    
    def _create_domain_rules(self, threat_name: str, domains: List[str], severity: str):
        """Create Wazuh rules for malicious domains"""
        severity_level = {'low': 5, 'medium': 8, 'high': 12, 'critical': 15}.get(severity, 8)
        
        rule_xml = f"""
        <rule id="{self._generate_rule_id()}" level="{severity_level}">
            <if_group>web,dns,</if_group>
            <match>{"|".join(domains)}</match>
            <description>Access to known malicious domain - {threat_name}</description>
            <group>threat_intelligence,{threat_name},dns,</group>
        </rule>
        """
        
        self._deploy_rule_to_wazuh(rule_xml)
    
    def _create_hash_rules(self, threat_name: str, hashes: List[str], severity: str):
        """Create Wazuh rules for malicious file hashes"""
        severity_level = {'low': 5, 'medium': 8, 'high': 12, 'critical': 15}.get(severity, 8)
        
        rule_xml = f"""
        <rule id="{self._generate_rule_id()}" level="{severity_level}">
            <if_group>syscheck,</if_group>
            <match>{"|".join(hashes)}</match>
            <description>Known malicious file detected - {threat_name}</description>
            <group>threat_intelligence,{threat_name},malware,</group>
        </rule>
        """
        
        self._deploy_rule_to_wazuh(rule_xml)
    
    def _generate_rule_id(self) -> str:
        """Generate unique rule ID for dynamic rules"""
        timestamp = str(int(time.time()))
        return f"200{timestamp[-6:]}"  # Use last 6 digits of timestamp
    
    def _deploy_rule_to_wazuh(self, rule_xml: str):
        """Deploy rule to Wazuh manager via API"""
        try:
            headers = {
                'Authorization': f'Bearer {self.api_key}',
                'Content-Type': 'application/xml'
            }
            
            response = requests.post(
                f"{self.wazuh_api_url}/rules",
                data=rule_xml,
                headers=headers
            )
            
            if response.status_code == 200:
                self.logger.info("Successfully deployed rule to Wazuh")
            else:
                self.logger.error(f"Failed to deploy rule: {response.status_code}")
                
        except Exception as e:
            self.logger.error(f"Error deploying rule to Wazuh: {str(e)}")

# Automated threat intelligence update service
class ThreatIntelligenceUpdater:
    """
    Service that automatically updates threat intelligence on a scheduled basis.
    This ensures your security monitoring stays current with emerging threats.
    """
    
    def __init__(self, ti_manager: ThreatIntelligenceManager):
        self.ti_manager = ti_manager
        self.update_interval = 3600  # Update every hour
        
    def start_automated_updates(self):
        """Start the automated threat intelligence update process"""
        self.logger.info("Starting automated threat intelligence updates")
        
        while True:
            try:
                # Fetch latest indicators
                indicators = self.ti_manager.fetch_government_threat_feeds()
                
                # Update security monitoring rules
                if indicators:
                    self.ti_manager.update_wazuh_rules(indicators)
                    
                # Wait for next update cycle
                time.sleep(self.update_interval)
                
            except Exception as e:
                self.logger.error(f"Error in automated update cycle: {str(e)}")
                time.sleep(300)  # Wait 5 minutes before retrying

This threat intelligence implementation is particularly powerful because it creates a feedback loop. As new threats emerge targeting government infrastructure, your security monitoring automatically adapts to detect these threats. The key insight here is that government environments face unique threat landscapes, and generic security monitoring isn't sufficient.

9. Disaster Recovery and Business Continuity

9.1 Backup Strategy Implementation

Understanding disaster recovery for a sovereign AI cloud requires thinking about multiple failure scenarios simultaneously. Unlike commercial cloud environments where you might accept some data loss, government operations demand comprehensive protection with minimal tolerance for data loss or extended downtime.

Step 39: Implement Multi-Tier Backup Strategy

The concept of multi-tier backup strategy revolves around the understanding that different types of data have different recovery requirements. Critical AI models and government data require immediate recovery capabilities, while historical training data might tolerate longer recovery times but needs long-term retention for compliance.

Comprehensive Backup Configuration:

yaml

# backup-strategy.yaml
apiVersion: v1
kind: ConfigMap
metadata:
  name: backup-policy
data:
  backup-config.yaml: |
    backup_tiers:
      tier_1_critical:
        description: "Critical AI models and active government data"
        rpo: 15m  # Recovery Point Objective - max 15 minutes data loss
        rto: 30m  # Recovery Time Objective - max 30 minutes downtime
        backup_frequency: "*/15 * * * *"  # Every 15 minutes
        retention_policy:
          daily: 30
          weekly: 12
          monthly: 24
          yearly: 7
        storage_locations:
          - local_ssd
          - remote_datacenter
          - offline_tape
        encryption: aes_256
        compression: enabled
        
      tier_2_important:
        description: "Training data and model artifacts"
        rpo: 4h
        rto: 2h
        backup_frequency: "0 */4 * * *"  # Every 4 hours
        retention_policy:
          daily: 14
          weekly: 8
          monthly: 12
          yearly: 5
        storage_locations:
          - remote_datacenter
          - cloud_storage
        encryption: aes_256
        compression: enabled
        
      tier_3_archival:
        description: "Historical logs and audit trails"
        rpo: 24h
        rto: 24h
        backup_frequency: "0 2 * * *"  # Daily at 2 AM
        retention_policy:
          weekly: 52
          monthly: 60
          yearly: 10
        storage_locations:
          - tape_archive
          - deep_storage
        encryption: aes_256
        compression: high
        
    backup_validation:
      test_frequency: weekly
      automated_recovery_tests: enabled
      integrity_checks: enabled
      compliance_verification: enabled

Let me walk you through implementing this backup strategy step by step, focusing on the reasoning behind each decision.

Automated Backup Implementation:

python

# backup_manager.py
import subprocess
import logging
import json
import yaml
from datetime import datetime, timedelta
from pathlib import Path
import boto3
import psycopg2
from kubernetes import client, config
import threading
import time

class SovereignBackupManager:
    """
    Comprehensive backup manager for sovereign AI cloud infrastructure.
    This class handles the complexities of backing up both structured data
    (databases) and unstructured data (files, models) while maintaining
    government compliance requirements.
    """
    
    def __init__(self, config_path: str):
        # Load backup configuration
        with open(config_path, 'r') as f:
            self.backup_config = yaml.safe_load(f)
        
        # Initialize logging
        logging.basicConfig(level=logging.INFO)
        self.logger = logging.getLogger(__name__)
        
        # Initialize Kubernetes client for container backups
        config.load_incluster_config()
        self.k8s_client = client.CoreV1Api()
        
        # Initialize storage clients
        self.s3_client = boto3.client('s3')
        
        # Backup status tracking
        self.backup_status = {}
        
    def execute_tier_1_backup(self):
        """
        Execute Tier 1 (critical) backups with 15-minute RPO.
        This includes active AI models, critical databases, and real-time government data.
        """
        try:
            self.logger.info("Starting Tier 1 critical backup")
            
            # Backup critical PostgreSQL databases
            self._backup_critical_databases()
            
            # Backup active AI models
            self._backup_active_ai_models()
            
            # Backup Kubernetes persistent volumes
            self._backup_kubernetes_volumes("tier-1")
            
            # Backup configuration and secrets
            self._backup_kubernetes_configs()
            
            # Replicate to secondary site
            self._replicate_to_secondary_site("tier-1")
            
            # Update backup status
            self.backup_status['tier_1'] = {
                'last_backup': datetime.now().isoformat(),
                'status': 'success',
                'next_backup': (datetime.now() + timedelta(minutes=15)).isoformat()
            }
            
            self.logger.info("Tier 1 backup completed successfully")
            
        except Exception as e:
            self.logger.error(f"Tier 1 backup failed: {str(e)}")
            self.backup_status['tier_1'] = {
                'last_backup': datetime.now().isoformat(),
                'status': 'failed',
                'error': str(e)
            }
            
            # Send alert for critical backup failure
            self._send_backup_alert("critical", f"Tier 1 backup failed: {str(e)}")
    
    def _backup_critical_databases(self):
        """
        Backup critical PostgreSQL databases with point-in-time recovery capability.
        This method demonstrates how to create consistent backups of databases
        that might be actively processing government transactions.
        """
        try:
            # Define critical databases that need immediate backup
            critical_databases = [
                'government_services',
                'citizen_data', 
                'ai_model_metadata',
                'audit_logs'
            ]
            
            for db_name in critical_databases:
                self.logger.info(f"Backing up critical database: {db_name}")
                
                # Create consistent snapshot using pg_dump
                backup_filename = f"{db_name}_{datetime.now().strftime('%Y%m%d_%H%M%S')}.sql"
                backup_path = f"/backups/tier1/databases/{backup_filename}"
                
                # Execute pg_dump with compression and consistency options
                pg_dump_cmd = [
                    'pg_dump',
                    '--host=postgres-primary',
                    '--port=5432',
                    '--username=backup_user',
                    '--verbose',
                    '--no-password',
                    '--format=custom',
                    '--compress=9',
                    '--no-owner',
                    '--no-privileges',
                    f'--file={backup_path}',
                    db_name
                ]
                
                result = subprocess.run(pg_dump_cmd, capture_output=True, text=True)
                
                if result.returncode == 0:
                    self.logger.info(f"Database {db_name} backed up successfully")
                    
                    # Encrypt the backup file
                    self._encrypt_backup_file(backup_path)
                    
                    # Copy to secondary locations
                    self._replicate_backup_file(backup_path, "tier-1")
                    
                else:
                    raise Exception(f"pg_dump failed for {db_name}: {result.stderr}")
                    
        except Exception as e:
            self.logger.error(f"Critical database backup failed: {str(e)}")
            raise
    
    def _backup_active_ai_models(self):
        """
        Backup active AI models including weights, configurations, and metadata.
        This is crucial for government AI services that need rapid recovery.
        """
        try:
            # Get list of active models from MLflow
            import mlflow
            
            mlflow.set_tracking_uri("http://mlflow-service:5000")
            client = mlflow.tracking.MlflowClient()
            
            # Get all registered models
            registered_models = client.list_registered_models()
            
            for model in registered_models:
                model_name = model.name
                self.logger.info(f"Backing up AI model: {model_name}")
                
                # Get latest version
                latest_version = client.get_latest_versions(model_name, stages=["Production"])[0]
                
                # Download model artifacts
                model_path = f"/backups/tier1/models/{model_name}_{latest_version.version}_{datetime.now().strftime('%Y%m%d_%H%M%S')}"
                
                # Create model backup directory
                Path(model_path).mkdir(parents=True, exist_ok=True)
                
                # Download model files
                mlflow.artifacts.download_artifacts(
                    artifact_uri=latest_version.source,
                    dst_path=model_path
                )
                
                # Create model metadata backup
                model_metadata = {
                    'name': model_name,
                    'version': latest_version.version,
                    'stage': latest_version.current_stage,
                    'description': latest_version.description,
                    'tags': latest_version.tags,
                    'creation_timestamp': latest_version.creation_timestamp,
                    'last_updated_timestamp': latest_version.last_updated_timestamp,
                    'backup_timestamp': datetime.now().isoformat()
                }
                
                with open(f"{model_path}/metadata.json", 'w') as f:
                    json.dump(model_metadata, f, indent=2)
                
                # Compress and encrypt model backup
                self._compress_and_encrypt_directory(model_path)
                
                self.logger.info(f"AI model {model_name} backed up successfully")
                
        except Exception as e:
            self.logger.error(f"AI model backup failed: {str(e)}")
            raise
    
    def _backup_kubernetes_volumes(self, tier: str):
        """
        Backup Kubernetes persistent volumes using volume snapshots.
        This ensures that container data is protected and can be rapidly restored.
        """
        try:
            # Get all PVCs in critical namespaces
            critical_namespaces = ['kubeflow', 'mlflow', 'default', 'security']
            
            for namespace in critical_namespaces:
                pvcs = self.k8s_client.list_namespaced_persistent_volume_claim(namespace)
                
                for pvc in pvcs.items:
                    pvc_name = pvc.metadata.name
                    self.logger.info(f"Creating snapshot for PVC: {pvc_name} in namespace: {namespace}")
                    
                    # Create volume snapshot
                    snapshot_name = f"{pvc_name}-{tier}-{datetime.now().strftime('%Y%m%d-%H%M%S')}"
                    
                    snapshot_manifest = {
                        'apiVersion': 'snapshot.storage.k8s.io/v1',
                        'kind': 'VolumeSnapshot',
                        'metadata': {
                            'name': snapshot_name,
                            'namespace': namespace
                        },
                        'spec': {
                            'source': {
                                'persistentVolumeClaimName': pvc_name
                            }
                        }
                    }
                    
                    # Apply snapshot using kubectl
                    import tempfile
                    with tempfile.NamedTemporaryFile(mode='w', suffix='.yaml', delete=False) as f:
                        yaml.dump(snapshot_manifest, f)
                        snapshot_file = f.name
                    
                    kubectl_cmd = ['kubectl', 'apply', '-f', snapshot_file]
                    result = subprocess.run(kubectl_cmd, capture_output=True, text=True)
                    
                    if result.returncode == 0:
                        self.logger.info(f"Volume snapshot {snapshot_name} created successfully")
                    else:
                        self.logger.error(f"Failed to create snapshot {snapshot_name}: {result.stderr}")
                    
                    # Clean up temp file
                    Path(snapshot_file).unlink()
                    
        except Exception as e:
            self.logger.error(f"Kubernetes volume backup failed: {str(e)}")
            raise
    
    def _encrypt_backup_file(self, file_path: str):
        """
        Encrypt backup files using AES-256 encryption.
        This ensures data protection even if backup media is compromised.
        """
        try:
            # Use gpg for encryption with government-approved algorithms
            encrypted_path = f"{file_path}.gpg"
            
            gpg_cmd = [
                'gpg',
                '--symmetric',
                '--cipher-algo', 'AES256',
                '--compress-algo', '2',
                '--s2k-mode', '3',
                '--s2k-digest-algo', 'SHA512',
                '--s2k-count', '65011712',
                '--force-mdc',
                '--quiet',
                '--batch',
                '--yes',
                '--passphrase-file', '/etc/backup/encryption-key',
                '--output', encrypted_path,
                file_path
            ]
            
            result = subprocess.run(gpg_cmd, capture_output=True, text=True)
            
            if result.returncode == 0:
                # Remove unencrypted file
                Path(file_path).unlink()
                self.logger.info(f"File encrypted successfully: {encrypted_path}")
            else:
                raise Exception(f"Encryption failed: {result.stderr}")
                
        except Exception as e:
            self.logger.error(f"File encryption failed: {str(e)}")
            raise

class DisasterRecoveryOrchestrator:
    """
    Orchestrates disaster recovery procedures including failover to secondary sites,
    data recovery, and service restoration. This class embodies the understanding
    that disaster recovery for government services requires coordinated, tested
    procedures that can be executed under pressure.
    """
    
    def __init__(self, dr_config_path: str):
        with open(dr_config_path, 'r') as f:
            self.dr_config = yaml.safe_load(f)
        
        logging.basicConfig(level=logging.INFO)
        self.logger = logging.getLogger(__name__)
        
        # Initialize recovery status tracking
        self.recovery_status = {
            'infrastructure': 'unknown',
            'data': 'unknown',
            'applications': 'unknown',
            'ai_models': 'unknown'
        }
    
    def execute_disaster_recovery(self, disaster_type: str, affected_components: list):
        """
        Execute comprehensive disaster recovery based on the type of disaster
        and affected components. This method demonstrates the decision tree
        approach needed for effective government disaster recovery.
        """
        try:
            self.logger.info(f"Initiating disaster recovery for: {disaster_type}")
            self.logger.info(f"Affected components: {affected_components}")
            
            # Step 1: Assess damage and determine recovery strategy
            recovery_plan = self._assess_and_plan_recovery(disaster_type, affected_components)
            
            # Step 2: Execute infrastructure recovery
            if 'infrastructure' in affected_components:
                self._recover_infrastructure(recovery_plan['infrastructure'])
            
            # Step 3: Execute data recovery
            if 'data' in affected_components:
                self._recover_data(recovery_plan['data'])
            
            # Step 4: Execute application recovery
            if 'applications' in affected_components:
                self._recover_applications(recovery_plan['applications'])
            
            # Step 5: Execute AI model recovery
            if 'ai_models' in affected_components:
                self._recover_ai_models(recovery_plan['ai_models'])
            
            # Step 6: Validate recovery and perform testing
            self._validate_recovery()
            
            # Step 7: Notify stakeholders of recovery completion
            self._notify_recovery_completion()
            
            self.logger.info("Disaster recovery completed successfully")
            
        except Exception as e:
            self.logger.error(f"Disaster recovery failed: {str(e)}")
            self._escalate_recovery_failure(str(e))
            raise
    
    def _assess_and_plan_recovery(self, disaster_type: str, affected_components: list) -> dict:
        """
        Assess the scope of disaster and create a recovery plan.
        This demonstrates the critical thinking required for government DR.
        """
        recovery_plan = {}
        
        # Determine recovery priorities based on government service criticality
        priority_matrix = {
            'citizen_services': 1,  # Highest priority
            'ai_models': 2,
            'data_processing': 3,
            'analytics': 4,
            'development': 5  # Lowest priority
        }
        
        # Infrastructure recovery planning
        if 'infrastructure' in affected_components:
            if disaster_type in ['datacenter_failure', 'hardware_failure']:
                recovery_plan['infrastructure'] = {
                    'strategy': 'failover_to_secondary',
                    'target_site': self.dr_config['secondary_sites']['primary'],
                    'estimated_time': '30 minutes',
                    'prerequisites': ['network_connectivity', 'secondary_site_available']
                }
            elif disaster_type == 'network_partition':
                recovery_plan['infrastructure'] = {
                    'strategy': 'restore_network_connectivity',
                    'target_site': 'current',
                    'estimated_time': '15 minutes',
                    'prerequisites': ['alternative_network_path']
                }
        
        # Data recovery planning
        if 'data' in affected_components:
            recovery_plan['data'] = {
                'strategy': 'restore_from_backup',
                'backup_tier': 'tier_1',  # Use most recent backups
                'estimated_time': '45 minutes',
                'data_loss_estimate': '15 minutes',  # Based on backup frequency
                'prerequisites': ['backup_integrity_verified', 'storage_available']
            }
        
        # Application recovery planning
        if 'applications' in affected_components:
            recovery_plan['applications'] = {
                'strategy': 'redeploy_from_registry',
                'deployment_order': self._determine_application_startup_order(),
                'estimated_time': '20 minutes',
                'prerequisites': ['infrastructure_recovered', 'container_registry_available']
            }
        
        # AI model recovery planning
        if 'ai_models' in affected_components:
            recovery_plan['ai_models'] = {
                'strategy': 'restore_from_model_backup',
                'model_priority': self._determine_model_recovery_priority(),
                'estimated_time': '30 minutes',
                'prerequisites': ['mlflow_available', 'model_artifacts_accessible']
            }
        
        return recovery_plan
    
    def _recover_infrastructure(self, infrastructure_plan: dict):
        """
        Execute infrastructure recovery procedures.
        This method shows how to orchestrate complex infrastructure failover.
        """
        try:
            self.logger.info("Starting infrastructure recovery")
            
            if infrastructure_plan['strategy'] == 'failover_to_secondary':
                # Verify secondary site readiness
                if not self._verify_secondary_site_readiness(infrastructure_plan['target_site']):
                    raise Exception("Secondary site not ready for failover")
                
                # Update DNS to point to secondary site
                self._update_dns_failover(infrastructure_plan['target_site'])
                
                # Start services on secondary site
                self._start_secondary_site_services(infrastructure_plan['target_site'])
                
                # Verify service availability
                if not self._verify_service_availability():
                    raise Exception("Service verification failed after failover")
                
                self.recovery_status['infrastructure'] = 'recovered'
                self.logger.info("Infrastructure failover completed successfully")
                
            elif infrastructure_plan['strategy'] == 'restore_network_connectivity':
                # Attempt to restore primary network paths
                self._restore_network_connectivity()
                
                # Verify connectivity
                if not self._verify_network_connectivity():
                    raise Exception("Network connectivity restoration failed")
                
                self.recovery_status['infrastructure'] = 'recovered'
                self.logger.info("Network connectivity restored successfully")
                
        except Exception as e:
            self.recovery_status['infrastructure'] = 'failed'
            self.logger.error(f"Infrastructure recovery failed: {str(e)}")
            raise
    
    def _recover_data(self, data_plan: dict):
        """
        Execute data recovery procedures with point-in-time recovery.
        This demonstrates handling of critical government data recovery.
        """
        try:
            self.logger.info("Starting data recovery")
            
            # Determine recovery point based on disaster timing
            recovery_point = self._determine_optimal_recovery_point(data_plan)
            
            # Stop any remaining database processes to ensure consistency
            self._stop_database_services()
            
            # Restore from backup
            if data_plan['backup_tier'] == 'tier_1':
                self._restore_tier_1_backups(recovery_point)
            elif data_plan['backup_tier'] == 'tier_2':
                self._restore_tier_2_backups(recovery_point)
            
            # Verify data integrity
            if not self._verify_data_integrity():
                raise Exception("Data integrity verification failed")
            
            # Restart database services
            self._start_database_services()
            
            # Perform data consistency checks
            if not self._verify_data_consistency():
                raise Exception("Data consistency verification failed")
            
            self.recovery_status['data'] = 'recovered'
            self.logger.info(f"Data recovery completed. Recovery point: {recovery_point}")
            
        except Exception as e:
            self.recovery_status['data'] = 'failed'
            self.logger.error(f"Data recovery failed: {str(e)}")
            raise
    
    def _restore_tier_1_backups(self, recovery_point: str):
        """
        Restore critical data from Tier 1 backups.
        This method handles the most critical government data recovery.
        """
        try:
            # Get list of databases to restore
            critical_databases = ['government_services', 'citizen_data', 'ai_model_metadata', 'audit_logs']
            
            for db_name in critical_databases:
                self.logger.info(f"Restoring database: {db_name}")
                
                # Find appropriate backup file
                backup_file = self._find_backup_file(db_name, recovery_point, 'tier_1')
                
                if not backup_file:
                    raise Exception(f"No suitable backup found for {db_name} at recovery point {recovery_point}")
                
                # Decrypt backup file
                decrypted_file = self._decrypt_backup_file(backup_file)
                
                # Drop existing database (if any)
                self._drop_database_if_exists(db_name)
                
                # Create new database
                self._create_database(db_name)
                
                # Restore from backup
                pg_restore_cmd = [
                    'pg_restore',
                    '--host=postgres-primary',
                    '--port=5432',
                    '--username=restore_user',
                    '--verbose',
                    '--no-password',
                    '--clean',
                    '--create',
                    f'--dbname={db_name}',
                    decrypted_file
                ]
                
                result = subprocess.run(pg_restore_cmd, capture_output=True, text=True)
                
                if result.returncode == 0:
                    self.logger.info(f"Database {db_name} restored successfully")
                else:
                    raise Exception(f"Database restore failed for {db_name}: {result.stderr}")
                
                # Clean up decrypted file
                Path(decrypted_file).unlink()
                
        except Exception as e:
            self.logger.error(f"Tier 1 backup restoration failed: {str(e)}")
            raise

# Automated DR testing service
class DisasterRecoveryTester:
    """
    Automated testing service for disaster recovery procedures.
    Regular DR testing is crucial for government environments to ensure
    recovery procedures work when actually needed.
    """
    
    def __init__(self, dr_orchestrator: DisasterRecoveryOrchestrator):
        self.dr_orchestrator = dr_orchestrator
        logging.basicConfig(level=logging.INFO)
        self.logger = logging.getLogger(__name__)
    
    def run_monthly_dr_test(self):
        """
        Execute comprehensive monthly disaster recovery test.
        This simulates various disaster scenarios without affecting production.
        """
        try:
            self.logger.info("Starting monthly disaster recovery test")
            
            # Test scenarios to execute
            test_scenarios = [
                {
                    'name': 'database_failure_simulation',
                    'description': 'Simulate critical database failure',
                    'affected_components': ['data'],
                    'expected_recovery_time': 45  # minutes
                },
                {
                    'name': 'infrastructure_failover_test',
                    'description': 'Test failover to secondary datacenter',
                    'affected_components': ['infrastructure'],
                    'expected_recovery_time': 30  # minutes
                },
                {
                    'name': 'ai_model_recovery_test',
                    'description': 'Test AI model restoration procedures',
                    'affected_components': ['ai_models'],
                    'expected_recovery_time': 30  # minutes
                }
            ]
            
            test_results = []
            
            for scenario in test_scenarios:
                self.logger.info(f"Executing test scenario: {scenario['name']}")
                
                # Record start time
                start_time = datetime.now()
                
                # Execute test in isolated environment
                test_result = self._execute_test_scenario(scenario)
                
                # Record end time and calculate duration
                end_time = datetime.now()
                duration_minutes = (end_time - start_time).total_seconds() / 60
                
                # Evaluate test results
                test_passed = (
                    test_result['success'] and 
                    duration_minutes <= scenario['expected_recovery_time']
                )
                
                test_results.append({
                    'scenario': scenario['name'],
                    'success': test_passed,
                    'duration_minutes': duration_minutes,
                    'expected_duration': scenario['expected_recovery_time'],
                    'details': test_result
                })
                
                self.logger.info(f"Test scenario {scenario['name']} completed: {'PASSED' if test_passed else 'FAILED'}")
            
            # Generate test report
            self._generate_dr_test_report(test_results)
            
            # Alert if any tests failed
            failed_tests = [t for t in test_results if not t['success']]
            if failed_tests:
                self._alert_dr_test_failures(failed_tests)
            
            self.logger.info("Monthly disaster recovery test completed")
            
        except Exception as e:
            self.logger.error(f"DR testing failed: {str(e)}")
            self._alert_dr_test_error(str(e))
    
    def _execute_test_scenario(self, scenario: dict) -> dict:
        """
        Execute individual DR test scenario in isolated environment.
        This method demonstrates safe DR testing without production impact.
        """
        try:
            # Create isolated test environment
            test_env = self._create_test_environment(scenario['name'])
            
            # Simulate disaster condition
            self._simulate_disaster(test_env, scenario['affected_components'])
            
            # Execute recovery procedures
            recovery_result = self.dr_orchestrator.execute_disaster_recovery(
                disaster_type='test_simulation',
                affected_components=scenario['affected_components']
            )
            
            # Validate recovery
            validation_result = self._validate_test_recovery(test_env, scenario)
            
            # Clean up test environment
            self._cleanup_test_environment(test_env)
            
            return {
                'success': validation_result['success'],
                'recovery_steps': recovery_result,
                'validation_details': validation_result
            }
            
        except Exception as e:
            self.logger.error(f"Test scenario execution failed: {str(e)}")
            return {
                'success': False,
                'error': str(e)
            }

10. Testing and Validation

10.1 Security Testing

Understanding security testing for a sovereign AI cloud requires recognizing that government environments face unique threats. Unlike commercial environments, government systems are high-value targets for nation-state actors, requiring comprehensive security validation that goes beyond standard penetration testing.

Step 40: Implement Comprehensive Security Testing Framework

The security testing framework I'm about to show you operates on the principle of "assumed breach" - we assume that sophisticated attackers will eventually find a way into the system, so we need to test not just prevention, but detection and response capabilities as well.

python

# security_testing_framework.py
import subprocess
import json
import yaml
import requests
import threading
import time
from datetime import datetime, timedelta
import logging
from typing import Dict, List, Optional
import paramiko
import nmap
import sqlparse
from selenium import webdriver
from selenium.webdriver.chrome.options import Options

class GovernmentSecurityTester:
    """
    Comprehensive security testing framework specifically designed for
    government AI cloud environments. This class demonstrates how to
    perform security testing that addresses government-specific threats
    while maintaining operational security.
    """
    
    def __init__(self, test_config_path: str):
        with open(test_config_path, 'r') as f:
            self.test_config = yaml.safe_load(f)
        
        logging.basicConfig(level=logging.INFO)
        self.logger = logging.getLogger(__name__)
        
        # Initialize test results tracking
        self.test_results = {
            'infrastructure': [],
            'applications': [],
            'data_protection': [],
            'compliance': [],
            'ai_security': []
        }
    
    def execute_comprehensive_security_assessment(self):
        """
        Execute comprehensive security assessment covering all aspects
        of government AI cloud security requirements.
        """
        try:
            self.logger.info("Starting comprehensive security assessment")
            
            # Phase 1: Infrastructure Security Testing
            self._test_infrastructure_security()
            
            # Phase 2: Application Security Testing
            self._test_application_security()
            
            # Phase 3: Data Protection Testing
            self._test_data_protection()
            
            # Phase 4: Compliance Validation
            self._test_compliance_controls()
            
            # Phase 5: AI-Specific Security Testing
            self._test_ai_security()
            
            # Phase 6: Incident Response Testing
            self._test_incident_response()
            
            # Generate comprehensive report
            self._generate_security_assessment_report()
            
            self.logger.info("Comprehensive security assessment completed")
            
        except Exception as e:
            self.logger.error(f"Security assessment failed: {str(e)}")
            raise
    
    def _test_infrastructure_security(self):
        """
        Test infrastructure security including network segmentation,
        access controls, and system hardening.
        """
        try:
            self.logger.info("Testing infrastructure security")
            
            # Test network segmentation
            segmentation_results = self._test_network_segmentation()
            self.test_results['infrastructure'].extend(segmentation_results)
            
            # Test access controls
            access_control_results = self._test_access_controls()
            self.test_results['infrastructure'].extend(access_control_results)
            
            # Test system hardening
            hardening_results = self._test_system_hardening()
            self.test_results['infrastructure'].extend(hardening_results)
            
            # Test encryption implementation
            encryption_results = self._test_encryption_implementation()
            self.test_results['infrastructure'].extend(encryption_results)
            
        except Exception as e:
            self.logger.error(f"Infrastructure security testing failed: {str(e)}")
            raise
    
    def _test_network_segmentation(self) -> List[Dict]:
        """
        Test network segmentation to ensure proper isolation between
        different security zones (management, compute, data, external).
        """
        results = []
        
        try:
            # Define network zones and expected isolation
            network_zones = {
                'management': {'subnet': '10.1.0.0/16', 'allowed_outbound': ['dns', 'ntp']},
                'compute': {'subnet': '10.2.0.0/16', 'allowed_outbound': ['storage', 'api']},
                'storage': {'subnet': '10.3.0.0/16', 'allowed_outbound': ['backup']},
                'external': {'subnet': '10.4.0.0/16', 'allowed_outbound': ['internet']}
            }
            
            for zone_name, zone_config in network_zones.items():
                self.logger.info(f"Testing network segmentation for zone: {zone_name}")
                
                # Test unauthorized inter-zone communication
                unauthorized_access = self._test_unauthorized_network_access(zone_name, zone_config)
                
                results.append({
                    'test_name': f'network_segmentation_{zone_name}',
                    'category': 'infrastructure',
                    'severity': 'high',
                    'passed': not unauthorized_access['violations_found'],
                    'details': unauthorized_access,
                    'timestamp': datetime.now().isoformat()
                })
                
                # Test firewall rules effectiveness
                firewall_test = self._test_firewall_rules(zone_name, zone_config)
                
                results.append({
                    'test_name': f'firewall_rules_{zone_name}',
                    'category': 'infrastructure',
                    'severity': 'high',
                    'passed': firewall_test['rules_effective'],
                    'details': firewall_test,
                    'timestamp': datetime.now().isoformat()
                })
            
        except Exception as e:
            self.logger.error(f"Network segmentation testing failed: {str(e)}")
            results.append({
                'test_name': 'network_segmentation_test',
                'category': 'infrastructure',
                'severity': 'high',
                'passed': False,
                'error': str(e),
                'timestamp': datetime.now().isoformat()
            })
        
        return results
    
    def _test_unauthorized_network_access(self, zone_name: str, zone_config: Dict) -> Dict:
        """
        Test for unauthorized network access between security zones.
        This simulates lateral movement attempts by attackers.
        """
        try:
            violations = []
            
            # Use nmap to test connectivity from different zones
            nm = nmap.PortScanner()
            
            # Define prohibited connections based on security policy
            prohibited_connections = {
                'management': ['external', 'compute'],
                'compute': ['external'],
                'storage': ['external'],
                'external': ['management', 'storage']
            }
            
            if zone_name in prohibited_connections:
                for prohibited_zone in prohibited_connections[zone_name]:
                    # Attempt connection to prohibited zone
                    test_result = self._attempt_zone_connection(zone_name, prohibited_zone)
                    
                    if test_result['connection_successful']:
                        violations.append({
                            'source_zone': zone_name,
                            'target_zone': prohibited_zone,
                            'connection_type': test_result['connection_type'],
                            'risk_level': 'high'
                        })
            
            return {
                'violations_found': len(violations) > 0,
                'violation_count': len(violations),
                'violations': violations
            }
            
        except Exception as e:
            self.logger.error(f"Unauthorized network access test failed: {str(e)}")
            return {'violations_found': True, 'error': str(e)}
    
    def _test_ai_security(self):
        """
        Test AI-specific security concerns including model poisoning protection,
        adversarial input detection, and model extraction prevention.
        """
        try:
            self.logger.info("Testing AI-specific security measures")
            
            # Test model access controls
            model_access_results = self._test_model_access_controls()
            self.test_results['ai_security'].extend(model_access_results)
            
            # Test adversarial input detection
            adversarial_results = self._test_adversarial_input_detection()
            self.test_results['ai_security'].extend(adversarial_results)
            
            # Test model extraction prevention
            extraction_results = self._test_model_extraction_prevention()
            self.test_results['ai_security'].extend(extraction_results)
            
            # Test training data protection
            training_data_results = self._test_training_data_protection()
            self.test_results['ai_security'].extend(training_data_results)
            
        except Exception as e:
            self.logger.error(f"AI security testing failed: {str(e)}")
            raise
    
    def _test_adversarial_input_detection(self) -> List[Dict]:
        """
        Test the system's ability to detect and handle adversarial inputs
        designed to fool AI models. This is crucial for government AI systems
        that might be targeted by sophisticated attackers.
        """
        results = []
        
        try:
            # Define adversarial test cases
            adversarial_tests = [
                {
                    'name': 'text_adversarial_attack',
                    'description': 'Text inputs designed to bypass content filters',
                    'test_inputs': [
                        'Legitimate request with hidden malicious intent',
                        'Social engineering attempt disguised as help request',
                        'Prompt injection attempt to extract sensitive information'
                    ]
                },
                {
                    'name': 'image_adversarial_attack',
                    'description': 'Images with subtle perturbations to fool classifiers',
                    'test_inputs': [
                        'Modified government document images',
                        'Subtly altered identity documents',
                        'Adversarial patches on legitimate images'
                    ]
                }
            ]
            
            for test_case in adversarial_tests:
                self.logger.info(f"Testing adversarial detection: {test_case['name']}")
                
                detection_results = []
                
                for test_input in test_case['test_inputs']:
                    # Submit adversarial input to AI system
                    response = self._submit_ai_input(test_input, test_case['name'])
                    
                    # Check if adversarial input was detected
                    adversarial_detected = self._check_adversarial_detection(response)
                    
                    detection_results.append({
                        'input': test_input,
                        'detected': adversarial_detected,
                        'response': response
                    })
                
                # Calculate detection rate
                detection_rate = sum(1 for r in detection_results if r['detected']) / len(detection_results)
                
                results.append({
                    'test_name': f'adversarial_detection_{test_case["name"]}',
                    'category': 'ai_security',
                    'severity': 'high',
                    'passed': detection_rate >= 0.8,  # Require 80% detection rate
                    'detection_rate': detection_rate,
                    'details': detection_results,
                    'timestamp': datetime.now().isoformat()
                })
                
        except Exception as e:
            self.logger.error(f"Adversarial input detection testing failed: {str(e)}")
            results.append({
                'test_name': 'adversarial_input_detection',
                'category': 'ai_security',
                'severity': 'high',
                'passed': False,
                'error': str(e),
                'timestamp': datetime.now().isoformat()
            })
        
        return results

class ComplianceValidator:
    """
    Validates compliance with Australian government regulations including
    Privacy Act, Australian Government Information Security Manual (ISM),
    and other relevant standards.
    """
    
    def __init__(self, compliance_config_path: str):
        with open(compliance_config_path, 'r') as f:
            self.compliance_config = yaml.safe_load(f)
        
        logging.basicConfig(level=logging.INFO)
        self.logger = logging.getLogger(__name__)
    
    def validate_privacy_act_compliance(self) -> Dict:
        """
        Validate compliance with Australian Privacy Act requirements.
        This includes data handling, consent management, and access controls.
        """
        try:
            self.logger.info("Validating Privacy Act compliance")
            
            compliance_results = {
                'overall_compliant': True,
                'principle_results': {},
                'violations': [],
                'recommendations': []
            }
            
            # Australian Privacy Principles validation
            privacy_principles = [
                'open_and_transparent_management',
                'anonymity_and_pseudonymity',
                'collection_of_solicited_information',
                'dealing_with_unsolicited_information',
                'notification_of_collection',
                'use_or_disclosure',
                'direct_marketing',
                'cross_border_disclosure',
                'adoption_use_or_disclosure_of_government_identifiers',
                'quality_of_personal_information',
                'security_of_personal_information',
                'access_to_personal_information',
                'correction_of_personal_information'
            ]
            
            for principle in privacy_principles:
                principle_result = self._validate_privacy_principle(principle)
                compliance_results['principle_results'][principle] = principle_result
                
                if not principle_result['compliant']:
                    compliance_results['overall_compliant'] = False
                    compliance_results['violations'].extend(principle_result['violations'])
                
                compliance_results['recommendations'].extend(principle_result.get('recommendations', []))
            
            return compliance_results
            
        except Exception as e:
            self.logger.error(f"Privacy Act compliance validation failed: {str(e)}")
            return {
                'overall_compliant': False,
                'error': str(e)
            }
    
    def _validate_privacy_principle(self, principle: str) -> Dict:
        """
        Validate specific Australian Privacy Principle compliance.
        Each principle has specific technical requirements that must be verified.
        """
        try:
            if principle == 'security_of_personal_information':
                return self._validate_security_of_personal_information()
            elif principle == 'access_to_personal_information':
                return self._validate_access_to_personal_information()
            elif principle == 'cross_border_disclosure':
                return self._validate_cross_border_disclosure()
            elif principle == 'collection_of_solicited_information':
                return self._validate_collection_of_solicited_information()
            else:
                # Generic validation for other principles
                return self._validate_generic_principle(principle)
                
        except Exception as e:
            return {
                'compliant': False,
                'violations': [f"Validation error for {principle}: {str(e)}"],
                'recommendations': [f"Review implementation of {principle}"]
            }
    
    def _validate_security_of_personal_information(self) -> Dict:
        """
        Validate APP 11 - Security of personal information.
        This principle requires reasonable steps to protect personal information.
        """
        violations = []
        recommendations = []
        
        try:
            # Check encryption at rest
            encryption_check = self._check_data_encryption_at_rest()
            if not encryption_check['encrypted']:
                violations.append("Personal information not encrypted at rest")
                recommendations.append("Implement AES-256 encryption for all personal information storage")
            
            # Check encryption in transit
            transit_check = self._check_data_encryption_in_transit()
            if not transit_check['encrypted']:
                violations.append("Personal information not encrypted in transit")
                recommendations.append("Implement TLS 1.3 for all data transmission")
            
            # Check access controls
            access_check = self._check_personal_information_access_controls()
            if not access_check['adequate']:
                violations.append("Inadequate access controls for personal information")
                recommendations.append("Implement role-based access controls with principle of least privilege")
            
            # Check audit logging
            audit_check = self._check_personal_information_audit_logging()
            if not audit_check['comprehensive']:
                violations.append("Insufficient audit logging for personal information access")
                recommendations.append("Implement comprehensive audit logging for all personal information access")
            
            # Check data retention policies
            retention_check = self._check_data_retention_policies()
            if not retention_check['compliant']:
                violations.append("Data retention policies not properly implemented")
                recommendations.append("Implement automated data retention and deletion policies")
            
            return {
                'compliant': len(violations) == 0,
                'violations': violations,
                'recommendations': recommendations,
                'technical_details': {
                    'encryption_at_rest': encryption_check,
                    'encryption_in_transit': transit_check,
                    'access_controls': access_check,
                    'audit_logging': audit_check,
                    'data_retention': retention_check
                }
            }
            
        except Exception as e:
            return {
                'compliant': False,
                'violations': [f"Security validation error: {str(e)}"],
                'recommendations': ["Review security implementation"]
            }

## 11. Go-Live and Maintenance

### 11.1 Production Deployment

Understanding production deployment for a sovereign AI cloud requires recognizing that government services demand zero-downtime deployment strategies. Citizens and government agencies depend on these services, making traditional maintenance windows unacceptable for critical systems.

**Step 41: Implement Blue-Green Deployment Strategy**

The blue-green deployment strategy I'm about to demonstrate operates on the principle of maintaining two identical production environments. This approach is particularly valuable for government services because it allows for comprehensive testing in a production-like environment before switching traffic, ensuring service continuity.

```python
# production_deployment.py
import kubernetes
import boto3
import time
import logging
import yaml
import requests
from datetime import datetime, timedelta
from typing import Dict, List, Optional
import subprocess
import threading

class ProductionDeploymentManager:
    """
    Manages production deployments for sovereign AI cloud using blue-green
    deployment strategies. This class demonstrates how to achieve zero-downtime
    deployments while maintaining government service availability requirements.
    """
    
    def __init__(self, deployment_config_path: str):
        with open(deployment_config_path, 'r') as f:
            self.deployment_config = yaml.safe_load(f)
        
        # Initialize Kubernetes client
        kubernetes.config.load_incluster_config()
        self.k8s_apps_v1 = kubernetes.client.AppsV1Api()
        self.k8s_core_v1 = kubernetes.client.CoreV1Api()
        self.k8s_networking_v1 = kubernetes.client.NetworkingV1Api()
        
        # Initialize logging
        logging.basicConfig(level=logging.INFO)
        self.logger = logging.getLogger(__name__)
        
        # Track deployment state
        self.deployment_state = {
            'active_environment': 'blue',
            'deployment_in_progress': False,
            'last_deployment': None,
            'health_checks': {}
        }
    
    def execute_blue_green_deployment(self, new_version: str, service_manifest: Dict):
        """
        Execute blue-green deployment for government AI services.
        This method ensures zero-downtime deployment with comprehensive validation.
        """
        try:
            self.logger.info(f"Starting blue-green deployment for version: {new_version}")
            self.deployment_state['deployment_in_progress'] = True
            
            # Determine current and target environments
            current_env = self.deployment_state['active_environment']
            target_env = 'green' if current_env == 'blue' else 'blue'
            
            self.logger.info(f"Current environment: {current_env}, Target environment: {target_env}")
            
            # Phase 1: Deploy to inactive environment
            self._deploy_to_environment(target_env, new_version, service_manifest)
            
            # Phase 2: Comprehensive health checks
            if not self._perform_comprehensive_health_checks(target_env):
                raise Exception("Health checks failed for target environment")
            
            # Phase 3: Gradual traffic shifting
            self._execute_gradual_traffic_shift(current_env, target_env)
            
            # Phase 4: Final validation
            if not self._validate_deployment_success(target_env):
                self.logger.error("Deployment validation failed, initiating rollback")
                self._rollback_deployment(current_env, target_env)
                raise Exception("Deployment validation failed")
            
            # Phase 5: Update deployment state
            self.deployment_state['active_environment'] = target_env
            self.deployment_state['last_deployment'] = {
                'version': new_version,
                'timestamp': datetime.now().isoformat(),
                'previous_environment': current_env
            }
            
            # Phase 6: Clean up old environment (keep for rollback capability)
            self._prepare_rollback_environment(current_env)
            
            self.deployment_state['deployment_in_progress'] = False
            self.logger.info(f"Blue-green deployment completed successfully. Active environment: {target_env}")
            
        except Exception as e:
            self.deployment_state['deployment_in_progress'] = False
            self.logger.error(f"Blue-green deployment failed: {str(e)}")
            self._send_deployment_alert("failed", str(e))
            raise
    
    def _deploy_to_environment(self, environment: str, version: str, service_manifest: Dict):
        """
        Deploy services to specified environment (blue or green).
        This method handles the complexities of deploying AI workloads including
        GPU resources, model artifacts, and data dependencies.
        """
        try:
            self.logger.info(f"Deploying version {version} to {environment} environment")
            
            # Update manifest with environment-specific configurations
            env_manifest = self._prepare_environment_manifest(service_manifest, environment, version)
            
            # Deploy AI model services
            self._deploy_ai_model_services(env_manifest, environment)
            
            # Deploy data processing services
            self._deploy_data_processing_services(env_manifest, environment)
            
            # Deploy API gateway and ingress
            self._deploy_api_services(env_manifest, environment)
            
            # Deploy monitoring and logging services
            self._deploy_monitoring_services(env_manifest, environment)
            
            # Wait for all deployments to be ready
            self._wait_for_deployment_ready(environment)
            
            self.logger.info(f"Deployment to {environment} environment completed")
            
        except Exception as e:
            self.logger.error(f"Deployment to {environment} environment failed: {str(e)}")
            raise
    
    def _deploy_ai_model_services(self, manifest: Dict, environment: str):
        """
        Deploy AI model services including MLflow, model serving, and inference endpoints.
        This method demonstrates deploying government AI services with proper resource allocation.
        """
        try:
            ai_services = manifest.get('ai_services', {})
            
            for service_name, service_config in ai_services.items():
                self.logger.info(f"Deploying AI service: {service_name} to {environment}")
                
                # Create deployment manifest
                deployment_manifest = {
                    'apiVersion': 'apps/v1',
                    'kind': 'Deployment',
                    'metadata': {
                        'name': f"{service_name}-{environment}",
                        'namespace': 'ai-services',
                        'labels': {
                            'app': service_name,
                            'environment': environment,
                            'version': manifest['version']
                        }
                    },
                    'spec': {
                        'replicas': service_config.get('replicas', 3),
                        'selector': {
                            'matchLabels': {
                                'app': service_name,
                                'environment': environment
                            }
                        },
                        'template': {
                            'metadata': {
                                'labels': {
                                    'app': service_name,
                                    'environment': environment,
                                    'version': manifest['version']
                                }
                            },
                            'spec': {
                                'containers': [{
                                    'name': service_name,
                                    'image': f"{service_config['image']}:{manifest['version']}",
                                    'ports': service_config.get('ports', []),
                                    'env': self._build_environment_variables(service_config, environment),
                                    'resources': {
                                        'requests': {
                                            'memory': service_config.get('memory_request', '1Gi'),
                                            'cpu': service_config.get('cpu_request', '500m')
                                        },
                                        'limits': {
                                            'memory': service_config.get('memory_limit', '2Gi'),
                                            'cpu': service_config.get('cpu_limit', '1000m')
                                        }
                                    },
                                    'volumeMounts': service_config.get('volume_mounts', []),
                                    'livenessProbe': {
                                        'httpGet': {
                                            'path': service_config.get('health_check_path', '/health'),
                                            'port': service_config.get('health_check_port', 8080)
                                        },
                                        'initialDelaySeconds': 30,
                                        'periodSeconds': 10
                                    },
                                    'readinessProbe': {
                                        'httpGet': {
                                            'path': service_config.get('readiness_check_path', '/ready'),
                                            'port': service_config.get('health_check_port', 8080)
                                        },
                                        'initialDelaySeconds': 5,
                                        'periodSeconds': 5
                                    }
                                }],
                                'volumes': service_config.get('volumes', []),
                                'nodeSelector': service_config.get('node_selector', {}),
                                'tolerations': service_config.get('tolerations', [])
                            }
                        }
                    }
                }
                
                # Add GPU resources if required
                if service_config.get('gpu_required', False):
                    deployment_manifest['spec']['template']['spec']['containers'][0]['resources']['limits']['nvidia.com/gpu'] = service_config.get('gpu_count', 1)
                
                # Deploy to Kubernetes
                self.k8s_apps_v1.create_namespaced_deployment(
                    namespace='ai-services',
                    body=deployment_manifest
                )
                
                # Create service for the deployment
                service_manifest = {
                    'apiVersion': 'v1',
                    'kind': 'Service',
                    'metadata': {
                        'name': f"{service_name}-{environment}",
                        'namespace': 'ai-services',
                        'labels': {
                            'app': service_name,
                            'environment': environment
                        }
                    },
                    'spec': {
                        'selector': {
                            'app': service_name,
                            'environment': environment
                        },
                        'ports': [
                            {
                                'port': port['port'],
                                'targetPort': port['targetPort'],
                                'protocol': port.get('protocol', 'TCP')
                            } for port in service_config.get('ports', [])
                        ],
                        'type': service_config.get('service_type', 'ClusterIP')
                    }
                }
                
                self.k8s_core_v1.create_namespaced_service(
                    namespace='ai-services',
                    body=service_manifest
                )
                
                self.logger.info(f"AI service {service_name} deployed successfully to {environment}")
                
        except Exception as e:
            self.logger.error(f"AI model services deployment failed: {str(e)}")
            raise
    
    def _execute_gradual_traffic_shift(self, source_env: str, target_env: str):
        """
        Execute gradual traffic shifting from source to target environment.
        This method demonstrates safe traffic migration with monitoring and rollback capability.
        """
        try:
            self.logger.info(f"Starting gradual traffic shift from {source_env} to {target_env}")
            
            # Define traffic shift stages
            traffic_stages = [
                {'target_percentage': 10, 'duration_minutes': 5},
                {'target_percentage': 25, 'duration_minutes': 10},
                {'target_percentage': 50, 'duration_minutes': 15},
                {'target_percentage': 75, 'duration_minutes': 10},
                {'target_percentage': 100, 'duration_minutes': 5}
            ]
            
            for stage in traffic_stages:
                self.logger.info(f"Shifting {stage['target_percentage']}% traffic to {target_env}")
                
                # Update ingress controller weights
                self._update_traffic_weights(source_env, target_env, stage['target_percentage'])
                
                # Monitor for specified duration
                monitor_start = datetime.now()
                monitor_end = monitor_start + timedelta(minutes=stage['duration_minutes'])
                
                while datetime.now() < monitor_end:
                    # Check health metrics during traffic shift
                    health_status = self._monitor_traffic_shift_health(target_env)
                    
                    if not health_status['healthy']:
                        self.logger.error(f"Health issues detected during traffic shift: {health_status['issues']}")
                        # Rollback traffic shift
                        self._update_traffic_weights(source_env, target_env, 0)
                        raise Exception(f"Traffic shift failed due to health issues: {health_status['issues']}")
                    
                    # Wait before next health check
                    time.sleep(30)
                
                self.logger.info(f"Traffic shift stage completed: {stage['target_percentage']}% to {target_env}")
            
            self.logger.info("Gradual traffic shift completed successfully")
            
        except Exception as e:
            self.logger.error(f"Gradual traffic shift failed: {str(e)}")
            # Attempt to rollback traffic
            self._update_traffic_weights(source_env, target_env, 0)
            raise
    
    def _monitor_traffic_shift_health(self, environment: str) -> Dict:
        """
        Monitor system health during traffic shifting.
        This includes response times, error rates, and AI model performance.
        """
        try:
            health_status = {
                'healthy': True,
                'issues': [],
                'metrics': {}
            }
            
            # Check response times
            response_times = self._check_response_times(environment)
            health_status['metrics']['response_times'] = response_times
            
            if response_times['p95'] > 2000:  # 2 second threshold
                health_status['healthy'] = False
                health_status['issues'].append(f"High response times: {response_times['p95']}ms")
            
            # Check error rates
            error_rates = self._check_error_rates(environment)
            health_status['metrics']['error_rates'] = error_rates
            
            if error_rates['error_percentage'] > 1.0:  # 1% error threshold
                health_status['healthy'] = False
                health_status['issues'].append(f"High error rate: {error_rates['error_percentage']}%")
            
            # Check AI model performance
            model_performance = self._check_ai_model_performance(environment)
            health_status['metrics']['model_performance'] = model_performance
            
            if model_performance['accuracy_degradation'] > 0.05:  # 5% degradation threshold
                health_status['healthy'] = False
                health_status['issues'].append(f"AI model accuracy degradation: {model_performance['accuracy_degradation']}")
            
            # Check resource utilization
            resource_usage = self._check_resource_utilization(environment)
            health_status['metrics']['resource_usage'] = resource_usage
            
            if resource_usage['cpu_usage'] > 80 or resource_usage['memory_usage'] > 80:
                health_status['healthy'] = False
                health_status['issues'].append(f"High resource usage: CPU {resource_usage['cpu_usage']}%, Memory {resource_usage['memory_usage']}%")
            
            return health_status
            
        except Exception as e:
            return {
                'healthy': False,
                'issues': [f"Health monitoring error: {str(e)}"],
                'metrics': {}
            }

class MaintenanceScheduler:
    """
    Manages scheduled maintenance for sovereign AI cloud infrastructure.
    This class demonstrates how to perform maintenance activities while
    minimizing impact on government services.
    """
    
    def __init__(self, maintenance_config_path: str):
        with open(maintenance_config_path, 'r') as f:
            self.maintenance_config = yaml.safe_load(f)
        
        logging.basicConfig(level=logging.INFO)
        self.logger = logging.getLogger(__name__)
        
        # Track maintenance windows and activities
        self.maintenance_state = {
            'current_maintenance': None,
            'scheduled_maintenance': [],
            'maintenance_history': []
        }
    
    def schedule_routine_maintenance(self):
        """
        Schedule routine maintenance activities including security updates,
        performance optimization, and compliance validation.
        """
        try:
            self.logger.info("Scheduling routine maintenance activities")
            
            # Define maintenance activities
            maintenance_activities = [
                {
                    'name': 'security_updates',
                    'description': 'Apply security updates to all systems',
                    'frequency': 'weekly',
                    'duration_hours': 2,
                    'impact_level': 'low',
                    'requires_downtime': False
                },
                {
                    'name': 'ai_model_retraining',
                    'description': 'Retrain AI models with latest data',
                    'frequency': 'monthly',
                    'duration_hours': 8,
                    'impact_level': 'medium',
                    'requires_downtime': False
                },
                {
                    'name': 'database_optimization',
                    'description': 'Optimize database performance',
                    'frequency': 'monthly',
                    'duration_hours': 3,
                    'impact_level': 'medium',
                    'requires_downtime': False
                },
                {
                    'name': 'compliance_validation',
                    'description': 'Validate compliance with government regulations',
                    'frequency': 'quarterly',
                    'duration_hours': 4,
                    'impact_level': 'low',
                    'requires_downtime': False
                },
                {
                    'name': 'disaster_recovery_test',
                    'description': 'Test disaster recovery procedures',
                    'frequency': 'quarterly',
                    'duration_hours': 6,
                    'impact_level': 'high',
                    'requires_downtime': True
                }
            ]
            
            # Schedule each activity based on frequency
            for activity in maintenance_activities:
                next_execution = self._calculate_next_execution_time(activity)
                
                scheduled_maintenance = {
                    'activity': activity,
                    'scheduled_time': next_execution,
                    'status': 'scheduled',
                    'scheduled_by': 'automated_scheduler',
                    'scheduled_at': datetime.now().isoformat()
                }
                
                self.maintenance_state['scheduled_maintenance'].append(scheduled_maintenance)
                
                self.logger.info(f"Scheduled maintenance activity: {activity['name']} for {next_execution}")
            
            # Sort scheduled maintenance by execution time
            self.maintenance_state['scheduled_maintenance'].sort(
                key=lambda x: x['scheduled_time']
            )
            
        except Exception as e:
            self.logger.error(f"Maintenance scheduling failed: {str(e)}")
            raise
    
    def execute_maintenance_activity(self, activity: Dict):
        """
        Execute specific maintenance activity with proper coordination and monitoring.
        This method demonstrates safe maintenance execution for government systems.
        """
        try:
            self.logger.info(f"Starting maintenance activity: {activity['name']}")
            
            # Update maintenance state
            self.maintenance_state['current_maintenance'] = {
                'activity': activity,
                'start_time': datetime.now().isoformat(),
                'status': 'in_progress'
            }
            
            # Send maintenance start notification
            self._send_maintenance_notification('started', activity)
            
            # Execute pre-maintenance checks
            if not self._perform_pre_maintenance_checks(activity):
                raise Exception("Pre-maintenance checks failed")
            
            # Execute maintenance based on activity type
            if activity['name'] == 'security_updates':
                self._execute_security_updates()
            elif activity['name'] == 'ai_model_retraining':
                self._execute_ai_model_retraining()
            elif activity['name'] == 'database_optimization':
                self._execute_database_optimization()
            elif activity['name'] == 'compliance_validation':
                self._execute_compliance_validation()
            elif activity['name'] == 'disaster_recovery_test':
                self._execute_disaster_recovery_test()
            else:
                raise Exception(f"Unknown maintenance activity: {activity['name']}")
            
            # Execute post-maintenance validation
            if not self._perform_post_maintenance_validation(activity):
                raise Exception("Post-maintenance validation failed")
            
            # Update maintenance state
            self.maintenance_state['current_maintenance']['status'] = 'completed'
            self.maintenance_state['current_maintenance']['end_time'] = datetime.now().isoformat()
            
            # Move to maintenance history
            self.maintenance_state['maintenance_history'].append(
                self.maintenance_state['current_maintenance']
            )
            self.maintenance_state['current_maintenance'] = None
            
            # Send maintenance completion notification
            self._send_maintenance_notification('completed', activity)
            
            self.logger.info(f"Maintenance activity completed successfully: {activity['name']}")
            
        except Exception as e:
            # Update maintenance state with error
            if self.maintenance_state['current_maintenance']:
                self.maintenance_state['current_maintenance']['status'] = 'failed'
                self.maintenance_state['current_maintenance']['error'] = str(e)
                self.maintenance_state['current_maintenance']['end_time'] = datetime.now().isoformat()
            
            # Send maintenance failure notification
            self._send_maintenance_notification('failed', activity, str(e))
            
            self.logger.error(f"Maintenance activity failed: {activity['name']}: {str(e)}")
            raise
    
    def _execute_security_updates(self):
        """
        Execute security updates across all system components.
        This includes OS updates, container image updates, and security patches.
        """
        try:
            self.logger.info("Executing security updates")
            
            # Update base OS packages on all nodes
            self._update_node_packages()
            
            # Update container images with latest security patches
            self._update_container_images()
            
            # Update Kubernetes cluster components
            self._update_kubernetes_components()
            
            # Update security tools and signatures
            self._update_security_tools()
            
            # Validate security posture after updates
            self._validate_security_posture()
            
            self.logger.info("Security updates completed successfully")
            
        except Exception as e:
            self.logger.error(f"Security updates failed: {str(e)}")
            raise
    
    def _execute_ai_model_retraining(self):
        """
        Execute AI model retraining with latest government data.
        This ensures models remain accurate and relevant for government services.
        """
        try:
            self.logger.info("Executing AI model retraining")
            
            # Get list of models that need retraining
            models_to_retrain = self._identify_models_for_retraining()
            
            for model in models_to_retrain:
                self.logger.info(f"Retraining model: {model['name']}")
                
                # Prepare training data
                training_data = self._prepare_training_data(model)
                
                # Execute model training
                training_results = self._train_model(model, training_data)
                
                # Validate model performance
                if not self._validate_model_performance(model, training_results):
                    self.logger.warning(f"Model performance validation failed for {model['name']}")
                    continue
                
                # Deploy updated model (using blue-green deployment)
                self._deploy_updated_model(model, training_results)
                
                self.logger.info(f"Model retraining completed: {model['name']}")
            
            self.logger.info("AI model retraining completed successfully")
            
        except Exception as e:
            self.logger.error(f"AI model retraining failed: {str(e)}")
            raise

# Automated maintenance orchestration
def start_maintenance_orchestration():
    """
    Start automated maintenance orchestration service.
    This service continuously monitors and executes scheduled maintenance.
    """
    try:
        # Initialize maintenance scheduler
        scheduler = MaintenanceScheduler('/config/maintenance-config.yaml')
        
        # Schedule routine maintenance
        scheduler.schedule_routine_maintenance()
        
        # Start maintenance execution loop
        while True:
            # Check for scheduled maintenance
            scheduled_activities = scheduler.maintenance_state['scheduled_maintenance']
            current_time = datetime.now()
            
            for scheduled_activity in scheduled_activities.copy():
                scheduled_time = datetime.fromisoformat(scheduled_activity['scheduled_time'])
                
                if current_time >= scheduled_time and scheduled_activity['status'] == 'scheduled':
                    # Execute maintenance activity
                    try:
                        scheduler.execute_maintenance_activity(scheduled_activity['activity'])
                        scheduled_activities.remove(scheduled_activity)
                    except Exception as e:
                        logging.error(f"Failed to execute maintenance activity: {str(e)}")
                        scheduled_activity['status'] = 'failed'
            
            # Wait before checking again
            time.sleep(300)  # Check every 5 minutes
            
    except Exception as e:
        logging.error(f"Maintenance orchestration failed: {str(e)}")
        raise

if __name__ == "__main__":
    start_maintenance_orchestration()

Conclusion

This comprehensive guide has walked you through every aspect of establishing a sovereign AI cloud solution for the Australian government. From initial planning and infrastructure setup to ongoing maintenance and compliance validation, each step has been designed to meet the unique requirements of government operations while maintaining the highest standards of security and sovereignty.

Key Takeaways

Data Sovereignty: Every component of this solution ensures that government data remains within Australian borders and under Australian control. This includes not just storage, but processing, backup, and disaster recovery operations.

Security First: The security implementation goes beyond standard commercial practices to address government-specific threats, including nation-state actors and sophisticated attack vectors targeting government infrastructure.

Compliance by Design: Rather than treating compliance as an afterthought, this solution builds regulatory compliance into every layer, from data classification and access controls to audit logging and retention policies.

Operational Excellence: The monitoring, maintenance, and disaster recovery procedures ensure that government services remain available and performant, meeting the service level expectations of citizens and government agencies.

Future-Ready Architecture: The containerized, cloud-native approach ensures that the solution can evolve with changing technology and government requirements while maintaining security and compliance standards.

Next Steps

1. Assessment Phase: Begin with a thorough assessment of your current infrastructure and requirements

2. Pilot Implementation: Start with a small pilot deployment to validate the approach

3. Phased Rollout: Gradually expand the implementation across government agencies

4. Continuous Improvement: Establish ongoing processes for security updates, compliance validation, and performance optimization

Resources and References

• Australian Government Information Security Manual (ISM)

• Australian Privacy Act 1988

• Australian Cyber Security Centre (ACSC) Guidelines

• Kubernetes Documentation: https://kubernetes.io/docs/

• MLflow Documentation: https://mlflow.org/docs/

• Prometheus Monitoring: https://prometheus.io/docs/

• Australian Government Cloud Computing Policy

This guide provides the foundation for a world-class sovereign AI cloud solution that meets the unique needs of Australian government operations while maintaining the highest standards of security, compliance, and operational excellence.