""" Project Deployment Orchestrator for Cyber-LLM Complete deployment automation across cloud platforms with enterprise features Author: Muzan Sano """ import asyncio import json import logging import subprocess from datetime import datetime, timedelta from typing import Dict, List, Any, Optional, Tuple, Union from dataclasses import dataclass, field from enum import Enum from pathlib import Path import yaml import boto3 import kubernetes from azure.identity import DefaultAzureCredential from azure.mgmt.containerservice import ContainerServiceClient from google.cloud import container_v1 from ..utils.logging_system import CyberLLMLogger, CyberLLMError, ErrorCategory from ..monitoring.prometheus import PrometheusMonitoring from ..governance.enterprise_governance import EnterpriseGovernanceManager class DeploymentPlatform(Enum): """Supported deployment platforms""" AWS_EKS = "aws_eks" AZURE_AKS = "azure_aks" GCP_GKE = "gcp_gke" ON_PREMISE = "on_premise" HYBRID_CLOUD = "hybrid_cloud" MULTI_CLOUD = "multi_cloud" class DeploymentEnvironment(Enum): """Deployment environments""" DEVELOPMENT = "development" STAGING = "staging" PRODUCTION = "production" DISASTER_RECOVERY = "disaster_recovery" class DeploymentStatus(Enum): """Deployment status""" PENDING = "pending" DEPLOYING = "deploying" DEPLOYED = "deployed" FAILED = "failed" ROLLING_BACK = "rolling_back" ROLLED_BACK = "rolled_back" UPDATING = "updating" @dataclass class DeploymentConfiguration: """Deployment configuration""" platform: DeploymentPlatform environment: DeploymentEnvironment # Resource configuration cpu_requests: str = "1000m" memory_requests: str = "2Gi" cpu_limits: str = "2000m" memory_limits: str = "4Gi" # Scaling configuration min_replicas: int = 2 max_replicas: int = 10 target_cpu_utilization: int = 70 # Security configuration enable_security_policies: bool = True enable_network_policies: bool = True enable_pod_security_policies: bool = True # Storage configuration storage_class: str = "fast-ssd" persistent_volume_size: str = "100Gi" # Monitoring configuration enable_monitoring: bool = True monitoring_namespace: str = "monitoring" # Additional configuration custom_annotations: Dict[str, str] = field(default_factory=dict) custom_labels: Dict[str, str] = field(default_factory=dict) environment_variables: Dict[str, str] = field(default_factory=dict) @dataclass class DeploymentResult: """Deployment result""" deployment_id: str status: DeploymentStatus platform: DeploymentPlatform environment: DeploymentEnvironment # Deployment details deployed_at: Optional[datetime] = None deployment_duration: Optional[timedelta] = None # Resources created services_created: List[str] = field(default_factory=list) deployments_created: List[str] = field(default_factory=list) configmaps_created: List[str] = field(default_factory=list) secrets_created: List[str] = field(default_factory=list) # Access information external_endpoints: List[str] = field(default_factory=list) internal_endpoints: List[str] = field(default_factory=list) # Monitoring information monitoring_dashboard_url: Optional[str] = None health_check_endpoint: Optional[str] = None # Error information error_message: Optional[str] = None rollback_available: bool = False class ProjectDeploymentOrchestrator: """Complete project deployment orchestration system""" def __init__(self, governance_manager: EnterpriseGovernanceManager, monitoring: PrometheusMonitoring, logger: Optional[CyberLLMLogger] = None): self.governance_manager = governance_manager self.monitoring = monitoring self.logger = logger or CyberLLMLogger(name="deployment_orchestrator") # Deployment tracking self.active_deployments = {} self.deployment_history = {} # Platform clients self._aws_client = None self._azure_client = None self._gcp_client = None self._k8s_client = None # Deployment templates self.deployment_templates = {} self.logger.info("Project Deployment Orchestrator initialized") async def deploy_complete_project(self, platform: DeploymentPlatform, environment: DeploymentEnvironment, config: Optional[DeploymentConfiguration] = None) -> DeploymentResult: """Deploy complete Cyber-LLM project""" deployment_id = f"cyber_llm_{environment.value}_{datetime.now().strftime('%Y%m%d_%H%M%S')}" try: self.logger.info("Starting complete project deployment", deployment_id=deployment_id, platform=platform.value, environment=environment.value) # Initialize deployment configuration if not config: config = self._get_default_configuration(platform, environment) # Mark deployment as starting deployment_result = DeploymentResult( deployment_id=deployment_id, status=DeploymentStatus.DEPLOYING, platform=platform, environment=environment ) self.active_deployments[deployment_id] = deployment_result start_time = datetime.now() # Phase 1: Infrastructure setup await self._setup_infrastructure(deployment_id, platform, environment, config) # Phase 2: Deploy core services await self._deploy_core_services(deployment_id, platform, environment, config) # Phase 3: Deploy AI agents await self._deploy_ai_agents(deployment_id, platform, environment, config) # Phase 4: Deploy orchestration layer await self._deploy_orchestration_layer(deployment_id, platform, environment, config) # Phase 5: Deploy API gateway and web interface await self._deploy_api_gateway(deployment_id, platform, environment, config) # Phase 6: Setup monitoring and observability await self._setup_monitoring(deployment_id, platform, environment, config) # Phase 7: Configure security and compliance await self._configure_security(deployment_id, platform, environment, config) # Phase 8: Run deployment validation await self._validate_deployment(deployment_id, platform, environment, config) # Update deployment result end_time = datetime.now() deployment_result.status = DeploymentStatus.DEPLOYED deployment_result.deployed_at = end_time deployment_result.deployment_duration = end_time - start_time # Move to history self.deployment_history[deployment_id] = deployment_result del self.active_deployments[deployment_id] self.logger.info("Project deployment completed successfully", deployment_id=deployment_id, duration=deployment_result.deployment_duration) return deployment_result except Exception as e: self.logger.error("Project deployment failed", deployment_id=deployment_id, error=str(e)) # Update deployment result with failure deployment_result.status = DeploymentStatus.FAILED deployment_result.error_message = str(e) deployment_result.rollback_available = True # Attempt rollback await self._rollback_deployment(deployment_id) return deployment_result async def _setup_infrastructure(self, deployment_id: str, platform: DeploymentPlatform, environment: DeploymentEnvironment, config: DeploymentConfiguration): """Setup underlying infrastructure""" self.logger.info("Setting up infrastructure", deployment_id=deployment_id) if platform == DeploymentPlatform.AWS_EKS: await self._setup_aws_infrastructure(deployment_id, environment, config) elif platform == DeploymentPlatform.AZURE_AKS: await self._setup_azure_infrastructure(deployment_id, environment, config) elif platform == DeploymentPlatform.GCP_GKE: await self._setup_gcp_infrastructure(deployment_id, environment, config) elif platform == DeploymentPlatform.ON_PREMISE: await self._setup_onpremise_infrastructure(deployment_id, environment, config) # Create namespace await self._create_namespace(deployment_id, environment) # Setup RBAC await self._setup_rbac(deployment_id, environment, config) # Create secrets and configmaps await self._create_secrets_configmaps(deployment_id, environment, config) async def _deploy_core_services(self, deployment_id: str, platform: DeploymentPlatform, environment: DeploymentEnvironment, config: DeploymentConfiguration): """Deploy core services""" self.logger.info("Deploying core services", deployment_id=deployment_id) # Deploy databases await self._deploy_databases(deployment_id, environment, config) # Deploy message queues await self._deploy_message_queues(deployment_id, environment, config) # Deploy caching layer await self._deploy_caching_layer(deployment_id, environment, config) # Deploy logging and metrics collection await self._deploy_logging_metrics(deployment_id, environment, config) async def _deploy_ai_agents(self, deployment_id: str, platform: DeploymentPlatform, environment: DeploymentEnvironment, config: DeploymentConfiguration): """Deploy AI agent services""" self.logger.info("Deploying AI agents", deployment_id=deployment_id) # Deploy reconnaissance agent await self._deploy_service("recon-agent", deployment_id, environment, config) # Deploy C2 agent await self._deploy_service("c2-agent", deployment_id, environment, config) # Deploy post-exploit agent await self._deploy_service("post-exploit-agent", deployment_id, environment, config) # Deploy explainability agent await self._deploy_service("explainability-agent", deployment_id, environment, config) # Deploy safety agent await self._deploy_service("safety-agent", deployment_id, environment, config) async def _deploy_orchestration_layer(self, deployment_id: str, platform: DeploymentPlatform, environment: DeploymentEnvironment, config: DeploymentConfiguration): """Deploy orchestration layer""" self.logger.info("Deploying orchestration layer", deployment_id=deployment_id) # Deploy main orchestrator await self._deploy_service("orchestrator", deployment_id, environment, config) # Deploy workflow engine await self._deploy_service("workflow-engine", deployment_id, environment, config) # Deploy external tool integration await self._deploy_service("tool-integration", deployment_id, environment, config) # Deploy learning systems await self._deploy_service("learning-system", deployment_id, environment, config) async def _deploy_api_gateway(self, deployment_id: str, platform: DeploymentPlatform, environment: DeploymentEnvironment, config: DeploymentConfiguration): """Deploy API gateway and web interface""" self.logger.info("Deploying API gateway", deployment_id=deployment_id) # Deploy API gateway await self._deploy_service("api-gateway", deployment_id, environment, config) # Deploy web interface await self._deploy_service("web-interface", deployment_id, environment, config) # Deploy CLI interface await self._deploy_service("cli-interface", deployment_id, environment, config) # Setup ingress/load balancer await self._setup_ingress(deployment_id, environment, config) async def _setup_monitoring(self, deployment_id: str, platform: DeploymentPlatform, environment: DeploymentEnvironment, config: DeploymentConfiguration): """Setup monitoring and observability""" if not config.enable_monitoring: return self.logger.info("Setting up monitoring", deployment_id=deployment_id) # Deploy Prometheus await self._deploy_prometheus(deployment_id, environment, config) # Deploy Grafana await self._deploy_grafana(deployment_id, environment, config) # Deploy alerting await self._deploy_alertmanager(deployment_id, environment, config) # Deploy distributed tracing await self._deploy_jaeger(deployment_id, environment, config) # Setup custom dashboards await self._setup_custom_dashboards(deployment_id, environment, config) async def _configure_security(self, deployment_id: str, platform: DeploymentPlatform, environment: DeploymentEnvironment, config: DeploymentConfiguration): """Configure security and compliance""" self.logger.info("Configuring security", deployment_id=deployment_id) # Setup network policies if config.enable_network_policies: await self._setup_network_policies(deployment_id, environment, config) # Setup security policies if config.enable_security_policies: await self._setup_security_policies(deployment_id, environment, config) # Setup pod security policies if config.enable_pod_security_policies: await self._setup_pod_security_policies(deployment_id, environment, config) # Setup certificate management await self._setup_certificate_management(deployment_id, environment, config) # Setup secrets management await self._setup_secrets_management(deployment_id, environment, config) async def _validate_deployment(self, deployment_id: str, platform: DeploymentPlatform, environment: DeploymentEnvironment, config: DeploymentConfiguration): """Validate deployment""" self.logger.info("Validating deployment", deployment_id=deployment_id) # Health checks await self._run_health_checks(deployment_id, environment) # Connectivity tests await self._run_connectivity_tests(deployment_id, environment) # Performance tests await self._run_performance_tests(deployment_id, environment) # Security validation await self._run_security_validation(deployment_id, environment) # Compliance validation await self._run_compliance_validation(deployment_id, environment) async def _deploy_service(self, service_name: str, deployment_id: str, environment: DeploymentEnvironment, config: DeploymentConfiguration): """Deploy a specific service""" self.logger.info(f"Deploying {service_name}", deployment_id=deployment_id) # Generate Kubernetes manifests manifests = self._generate_service_manifests(service_name, environment, config) # Apply manifests for manifest in manifests: await self._apply_k8s_manifest(manifest) # Wait for deployment to be ready await self._wait_for_deployment_ready(service_name, environment) # Update deployment result if deployment_id in self.active_deployments: self.active_deployments[deployment_id].deployments_created.append(service_name) def _generate_service_manifests(self, service_name: str, environment: DeploymentEnvironment, config: DeploymentConfiguration) -> List[Dict[str, Any]]: """Generate Kubernetes manifests for a service""" namespace = f"cyber-llm-{environment.value}" # Deployment manifest deployment = { "apiVersion": "apps/v1", "kind": "Deployment", "metadata": { "name": service_name, "namespace": namespace, "labels": { "app": service_name, "version": "v1.0.0", "environment": environment.value, **config.custom_labels }, "annotations": config.custom_annotations }, "spec": { "replicas": config.min_replicas, "selector": { "matchLabels": { "app": service_name } }, "template": { "metadata": { "labels": { "app": service_name, "version": "v1.0.0" } }, "spec": { "containers": [{ "name": service_name, "image": f"cyber-llm/{service_name}:latest", "ports": [{ "containerPort": 8080, "name": "http" }], "resources": { "requests": { "cpu": config.cpu_requests, "memory": config.memory_requests }, "limits": { "cpu": config.cpu_limits, "memory": config.memory_limits } }, "env": [ {"name": k, "value": v} for k, v in config.environment_variables.items() ], "livenessProbe": { "httpGet": { "path": "/health", "port": 8080 }, "initialDelaySeconds": 30, "periodSeconds": 10 }, "readinessProbe": { "httpGet": { "path": "/ready", "port": 8080 }, "initialDelaySeconds": 5, "periodSeconds": 5 } }] } } } } # Service manifest service = { "apiVersion": "v1", "kind": "Service", "metadata": { "name": service_name, "namespace": namespace, "labels": { "app": service_name } }, "spec": { "selector": { "app": service_name }, "ports": [{ "port": 80, "targetPort": 8080, "name": "http" }], "type": "ClusterIP" } } # HPA manifest hpa = { "apiVersion": "autoscaling/v2", "kind": "HorizontalPodAutoscaler", "metadata": { "name": f"{service_name}-hpa", "namespace": namespace }, "spec": { "scaleTargetRef": { "apiVersion": "apps/v1", "kind": "Deployment", "name": service_name }, "minReplicas": config.min_replicas, "maxReplicas": config.max_replicas, "metrics": [{ "type": "Resource", "resource": { "name": "cpu", "target": { "type": "Utilization", "averageUtilization": config.target_cpu_utilization } } }] } } return [deployment, service, hpa] def _get_default_configuration(self, platform: DeploymentPlatform, environment: DeploymentEnvironment) -> DeploymentConfiguration: """Get default deployment configuration""" # Adjust resources based on environment if environment == DeploymentEnvironment.PRODUCTION: return DeploymentConfiguration( platform=platform, environment=environment, cpu_requests="2000m", memory_requests="4Gi", cpu_limits="4000m", memory_limits="8Gi", min_replicas=3, max_replicas=20, target_cpu_utilization=70 ) elif environment == DeploymentEnvironment.STAGING: return DeploymentConfiguration( platform=platform, environment=environment, cpu_requests="1000m", memory_requests="2Gi", cpu_limits="2000m", memory_limits="4Gi", min_replicas=2, max_replicas=10, target_cpu_utilization=75 ) else: return DeploymentConfiguration( platform=platform, environment=environment, cpu_requests="500m", memory_requests="1Gi", cpu_limits="1000m", memory_limits="2Gi", min_replicas=1, max_replicas=5, target_cpu_utilization=80 ) # Factory function def create_deployment_orchestrator(governance_manager: EnterpriseGovernanceManager, monitoring: PrometheusMonitoring, **kwargs) -> ProjectDeploymentOrchestrator: """Create project deployment orchestrator""" return ProjectDeploymentOrchestrator(governance_manager, monitoring, **kwargs)