Real-life Examples

This guide provides practical examples of modern DevSecOps implementations across different platforms and environments.

1. Zero Trust Security Implementation

Azure Entra ID (formerly Azure AD) with Conditional Access

# Terraform configuration for Conditional Access
resource "azuread_conditional_access_policy" "zero_trust" {
  display_name = "Zero Trust Policy"
  state        = "enabled"

  conditions {
    client_app_types = ["all"]
    
    applications {
      included_applications = ["all"]
    }
    
    locations {
      included_locations = ["all"]
      excluded_locations = ["trusted_locations"]
    }
    
    platforms {
      included_platforms = ["all"]
    }
    
    users {
      included_users = ["all"]
      excluded_users = ["emergency_access_accounts"]
    }
  }

  grant_controls {
    operator = "AND"
    built_in_controls = [
      "mfa",
      "compliant_device",
      "domain_joined_device"
    ]
  }

  session_controls {
    application_enforced_restrictions = true
    cloud_app_security_policy        = "monitor_only"
    sign_in_frequency               = 4
    sign_in_frequency_period        = "hours"
  }
}

NixOS Hardened Configuration

# configuration.nix
{ config, pkgs, ... }:
{
  security = {
    # Enable TPM 2.0 support
    tpm2 = {
      enable = true;
      pkcs11.enable = true;
    };
    
    # System hardening
    lockKernelModules = true;
    protectKernelImage = true;
    
    # Audit system
    auditd.enable = true;
    audit.rules = [
      "-w /etc/passwd -p wa -k identity"
      "-w /etc/group -p wa -k identity"
      "-a exit,always -F arch=b64 -S execve -k exec"
    ];
    
    # SELinux configuration
    selinux = {
      enable = true;
      type = "strict";
    };
  };
  
  # Secure boot configuration
  boot.loader.systemd-boot.enable = true;
  boot.loader.efi.canTouchEfiVariables = true;
  boot.initrd.systemd.enable = true;
}

2. AI-Powered Security Monitoring

LLM-Enhanced Log Analysis

# Security Log Analysis with LLMs
import anthropic
from datetime import datetime

class SecurityLogAnalyzer:
    def __init__(self, api_key):
        self.client = anthropic.Anthropic(api_key=api_key)
        
    async def analyze_logs(self, log_entries):
        prompt = f"""
        Analyze these security logs for potential threats:
        {log_entries}
        
        Focus on:
        1. Unusual access patterns
        2. Potential data exfiltration
        3. Known attack signatures
        4. Policy violations
        """
        
        response = await self.client.messages.create(
            model="claude-3-opus-20240229",
            max_tokens=2000,
            temperature=0,
            system="You are a cybersecurity expert analyzing security logs.",
            messages=[{
                "role": "user",
                "content": prompt
            }]
        )
        
        return response.content

# Usage example
analyzer = SecurityLogAnalyzer(api_key="your-key")
analysis = await analyzer.analyze_logs(log_entries)

Automated Incident Response

# GitHub Actions Workflow for Automated Response
name: Security Incident Response
on:
  security_alert:
    types: [created]

jobs:
  analyze_and_respond:
    runs-on: ubuntu-latest
    permissions:
      security-events: write
      issues: write
    
    steps:
      - name: Analyze Alert with Claude
        uses: anthropic/claude-action@v2
        with:
          api_key: ${{ secrets.CLAUDE_API_KEY }}
          alert_data: ${{ toJson(github.event.alert) }}
          
      - name: Create Response Plan
        run: |
          # Generate incident response plan using Claude
          response_plan=$(claude generate-response-plan)
          echo "RESPONSE_PLAN=$response_plan" >> $GITHUB_ENV
          
      - name: Execute Response
        uses: security/automated-response@v2
        with:
          plan: ${{ env.RESPONSE_PLAN }}
          notify: ["[e-mail]"]

3. Cross-Platform Security Pipeline

WSL2 Development Environment

# Setup secure WSL2 development environment
wsl --install Ubuntu-22.04

# Configure WSL security settings
$wslConfig = @"
[wsl2]
memory=8GB
processors=4
kernelCommandLine = vsyscall=emulate
nestedVirtualization=true
pageReporting=true

[boot]
systemd=true
command=
"@

Set-Content -Path "$env:USERPROFILE\.wslconfig" -Value $wslConfig

# Install security tools
wsl -d Ubuntu-22.04 -u root bash -c '
apt update && apt install -y \
  apparmor \
  auditd \
  clamav \
  fail2ban \
  lynis \
  openscap-scanner \
  rkhunter \
  trivy
'

Multi-Platform Pipeline Security

# Azure Pipeline with cross-platform security checks
trigger:
  - main

variables:
  securityTools: |
    trivy
    snyk
    semgrep
    gosec
    
pool:
  vmImage: ubuntu-latest

stages:
- stage: SecurityScanning
  jobs:
  - job: LinuxScan
    pool:
      vmImage: ubuntu-latest
    steps:
    - script: |
        for tool in $(securityTools); do
          docker run --rm -v $(pwd):/app "$tool" scan /app
        done
        
  - job: WindowsScan
    pool:
      vmImage: windows-latest
    steps:
    - powershell: |
        Import-Module PSScriptAnalyzer
        Invoke-ScriptAnalyzer -Path .\ -Recurse
        
  - job: ContainerScan
    steps:
    - task: Container-Security-Scan@1
      inputs:
        imageName: $(Build.Repository.Name)
        severityThreshold: CRITICAL

4. Infrastructure Security as Code

Cloud-Native Security Controls

# Terraform AWS Security Configuration
module "security_baseline" {
  source = "github.com/nozaq/terraform-aws-secure-baseline"

  audit_log_bucket_name           = "audit-logs-${data.aws_caller_identity.current.account_id}"
  aws_account_id                  = data.aws_caller_identity.current.account_id
  region                          = data.aws_region.current.name
  target_regions                  = ["us-east-1", "eu-west-1"]
  
  # Enable security features
  enable_guardduty                = true
  enable_vpc_flow_logs           = true
  enable_cloud_trail             = true
  enable_config                  = true
  enable_security_hub           = true
  enable_macie                  = true
  
  # Configure CloudTrail
  cloudtrail_config = {
    enable_log_file_validation = true
    is_multi_region_trail     = true
    include_global_service_events = true
    enable_logging            = true
  }
  
  # Configure GuardDuty
  guardduty_config = {
    enable_s3_protection     = true
    enable_kubernetes_protection = true
    finding_publishing_frequency = "FIFTEEN_MINUTES"
  }
}

Kubernetes Security Policies

# OPA/Gatekeeper Policy
apiVersion: constraints.gatekeeper.sh/v1beta1
kind: K8sRestrictedEndpoints
metadata:
  name: restrict-endpoint-access
spec:
  match:
    kinds:
      - apiGroups: [""]
        kinds: ["Pod"]
    namespaces:
      - "production"
  parameters:
    allowedEndpoints:
      - ipBlock:
          cidr: "10.0.0.0/8"
      - ipBlock:
          cidr: "172.16.0.0/12"
    restrictedPorts:
      - 22
      - 3389

5. AI-Enhanced Threat Detection

Real-time LLM Analysis

# Real-time threat detection with Claude
from anthropic import Anthropic
import json

class ThreatDetector:
    def __init__(self):
        self.client = Anthropic()
        self.context = []
        
    async def analyze_event(self, event_data):
        # Add event to context window
        self.context.append(event_data)
        
        # Maintain context window size
        if len(self.context) > 10:
            self.context.pop(0)
            
        prompt = f"""
        Analyze this security event in context of recent events:
        Recent events: {json.dumps(self.context[:-1])}
        Current event: {json.dumps(event_data)}
        
        Determine:
        1. Threat level (0-10)
        2. Attack pattern recognition
        3. Recommended immediate actions
        4. False positive likelihood
        """
        
        response = await self.client.messages.create(
            model="claude-3-opus-20240229",
            temperature=0,
            max_tokens=1000,
            messages=[{
                "role": "user",
                "content": prompt
            }]
        )
        
        return json.loads(response.content)

6. LLM-Enhanced Observability

Intelligent Log Correlation

# Advanced log correlation with Claude
from anthropic import Anthropic
import opensearch_client
import json

class IntelligentObservability:
    def __init__(self):
        self.client = Anthropic()
        self.opensearch = opensearch_client.OpenSearch()
        
    async def correlate_incidents(self, timeframe_minutes=60):
        # Fetch logs from different sources
        logs = {
            'security': self.opensearch.get_logs('security-*', timeframe_minutes),
            'application': self.opensearch.get_logs('app-*', timeframe_minutes),
            'infrastructure': self.opensearch.get_logs('infra-*', timeframe_minutes)
        }
        
        prompt = f"""
        Analyze these logs from different sources within a {timeframe_minutes} minute window:
        {json.dumps(logs, indent=2)}
        
        Identify:
        1. Correlated events across different systems
        2. Potential root causes
        3. Impact assessment
        4. Recommended actions
        """
        
        analysis = await self.client.messages.create(
            model="claude-3-opus-20240229",
            temperature=0,
            messages=[{
                "role": "user",
                "content": prompt
            }]
        )
        
        return self._structure_analysis(analysis.content)

Automatic Runbook Generation

# Flux GitOps Configuration for Runbook Generation
apiVersion: source.toolkit.fluxcd.io/v1
kind: GitRepository
metadata:
  name: runbook-generator
  namespace: flux-system
spec:
  interval: 1h
  url: https://github.com/org/runbooks
  ref:
    branch: main
---
apiVersion: automation.toolkit.fluxcd.io/v1
kind: RunbookGenerator
metadata:
  name: security-runbooks
spec:
  interval: 6h
  llmConfig:
    provider: anthropic
    model: claude-3-opus-20240229
    prompts:
      - template: |
          Generate a detailed runbook for handling security incident:
          ${incident_type}
          
          Include:
          1. Initial assessment steps
          2. Containment procedures
          3. Evidence collection
          4. Recovery steps
          5. Post-incident analysis
  sourceRef:
    kind: GitRepository
    name: runbook-generator
  path: ./runbooks/security

7. GitOps Security Automation

Flux Security Controller

# Custom Flux Security Controller
apiVersion: source.toolkit.fluxcd.io/v1
kind: GitRepository
metadata:
  name: security-policies
  namespace: flux-system
spec:
  interval: 1m
  url: https://github.com/org/security-policies
---
apiVersion: kustomize.toolkit.fluxcd.io/v1
kind: Kustomization
metadata:
  name: security-controls
spec:
  interval: 5m
  path: ./controls
  prune: true
  sourceRef:
    kind: GitRepository
    name: security-policies
  validation:
    llm:
      provider: anthropic
      model: claude-3-opus-20240229
      validationPrompt: |
        Validate this security policy change:
        ${POLICY_CONTENT}
        
        Check for:
        1. Compliance violations
        2. Security risks
        3. Best practice adherence
        4. Potential impacts

Automated Policy Updates

# Policy Update Automation with LLM
from git import Repo
from anthropic import Anthropic
import yaml

class SecurityPolicyAutomation:
    def __init__(self, repo_path):
        self.repo = Repo(repo_path)
        self.client = Anthropic()
        
    async def update_security_policies(self, new_cve_data):
        # Analyze CVE and generate policy updates
        prompt = f"""
        Given this new CVE data:
        {json.dumps(new_cve_data, indent=2)}
        
        Generate appropriate security policy updates for:
        1. Network policies
        2. RBAC rules
        3. Pod security standards
        4. Runtime security controls
        """
        
        response = await self.client.messages.create(
            model="claude-3-opus-20240229",
            temperature=0,
            messages=[{
                "role": "user",
                "content": prompt
            }]
        )
        
        # Create branch and update policies
        branch_name = f"security-update-{new_cve_data['id']}"
        new_branch = self.repo.create_head(branch_name)
        new_branch.checkout()
        
        # Apply updates
        policies = yaml.safe_load(response.content)
        for policy_file, content in policies.items():
            with open(f"policies/{policy_file}", 'w') as f:
                yaml.dump(content, f)
        
        # Commit and push
        self.repo.index.add(['policies/*'])
        self.repo.index.commit(f"fix: Security policy update for {new_cve_data['id']}")
        self.repo.remotes.origin.push(branch_name)

8. Cross-Platform Development Security

NixOS Development Container

# development.nix
{ pkgs ? import <nixpkgs> {} }:

pkgs.mkShell {
  buildInputs = with pkgs; [
    # Security tools
    clamav
    trivy
    snyk
    semgrep
    
    # Development tools
    git
    docker
    kubernetes-helm
    
    # Language-specific security tools
    nodePackages.audit
    python39Packages.bandit
    python39Packages.safety
    
    # Cloud security tools
    aws-nuke
    azure-cli
    google-cloud-sdk
  ];
  
  shellHook = ''
    # Setup security scanning pre-commit hooks
    git config core.hooksPath .githooks
    chmod +x .githooks/*
    
    # Configure environment
    export SNYK_TOKEN="''${SNYK_TOKEN}"
    export SEMGREP_APP_TOKEN="''${SEMGREP_APP_TOKEN}"
  '';
}

WSL2 Security Integration

# WSL2 Security Integration Script
$wslConfig = @"
[wsl2]
memory=16GB
processors=4
kernelCommandLine = vsyscall=emulate
nestedVirtualization=true

[automount]
enabled = true
options = "metadata,uid=1000,gid=1000,umask=022"

[experimental]
sparseVhd=true
networkingMode=mirrored
dnsTunneling=true
firewall=true
"@

Set-Content -Path "$env:USERPROFILE\.wslconfig" -Value $wslConfig

# Install security tools in WSL
wsl -d Ubuntu-22.04 bash -c '
# Add security repositories
curl -fsSL https://pkg.snyk.io/key | sudo gpg --dearmor -o /usr/share/keyrings/snyk-archive-keyring.gpg
echo "deb [signed-by=/usr/share/keyrings/snyk-archive-keyring.gpg] https://pkg.snyk.io/deb stable main" | sudo tee /etc/apt/sources.list.d/snyk.list

# Install tools
sudo apt update && sudo apt install -y \
  snyk \
  trivy \
  clamav \
  rkhunter \
  lynis \
  aide \
  auditd \
  apparmor \
  seccomp \
  fail2ban

# Configure AppArmor profiles
sudo aa-enforce /etc/apparmor.d/*

# Setup audit rules
sudo auditctl -e 1
sudo auditctl -w /etc/passwd -p wa -k identity
sudo auditctl -w /etc/group -p wa -k identity
'

9. Continuous Security Validation

Automated Security Testing

# GitHub Actions Security Validation
name: Security Validation
on:
  schedule:
    - cron: '0 */6 * * *'  # Every 6 hours
  push:
    branches: [main, develop]
  pull_request:
    branches: [main]

jobs:
  security-validation:
    runs-on: ubuntu-latest
    strategy:
      matrix:
        include:
          - type: static-analysis
            tools: [semgrep, codeql]
          - type: dependency-check
            tools: [snyk, osv-scanner]
          - type: container-scan
            tools: [trivy, grype]
          - type: iac-security
            tools: [checkov, tfsec]
    
    steps:
      - uses: actions/checkout@v4
      
      - name: LLM Security Review
        uses: anthropic/claude-security@v2
        with:
          api_key: ${{ secrets.CLAUDE_API_KEY }}
          scan_type: ${{ matrix.type }}
          
      - name: Run Security Tools
        run: |
          for tool in ${{ join(matrix.tools, ' ') }}; do
            docker run --rm -v $(pwd):/code "$tool" scan /code
          done
          
      - name: Analyze Results with Claude
        uses: anthropic/claude-analysis@v2
        with:
          results: ${{ steps.security-scan.outputs.results }}
          threshold: high

10. LLM Integration Patterns (2025)

Automated Code Review with LLM

from anthropic import Anthropic
import git
from datetime import datetime

class SecureCodeReviewer:
    def __init__(self, api_key: str):
        self.client = Anthropic(api_key=api_key)
        self.repo = git.Repo(".")
        
    async def review_pr(self, pr_diff: str) -> dict:
        prompt = f"""
        Perform a security-focused code review of these changes:
        {pr_diff}

        Consider:
        1. Security vulnerabilities (OWASP Top 10)
        2. Secure coding practices
        3. Input validation
        4. Authentication/Authorization
        5. Data protection
        6. Error handling
        7. Logging/Monitoring
        """

        response = await self.client.messages.create(
            model="claude-3-opus-20240229",
            system="You are an expert security code reviewer focusing on finding security vulnerabilities.",
            messages=[{"role": "user", "content": prompt}]
        )

        return self._structure_review(response.content)

    def _structure_review(self, review: str) -> dict:
        return {
            "timestamp": datetime.utcnow().isoformat(),
            "findings": self._parse_findings(review),
            "recommendations": self._parse_recommendations(review),
            "risk_level": self._calculate_risk(review)
        }

Infrastructure Validation

class InfrastructureValidator:
    def __init__(self, api_key: str):
        self.client = Anthropic(api_key=api_key)
        
    async def validate_terraform(self, plan_output: str) -> dict:
        prompt = f"""
        Analyze this Terraform plan for security risks:
        {plan_output}

        Check for:
        1. Public exposure of resources
        2. IAM/RBAC misconfigurations
        3. Encryption settings
        4. Network security
        5. Compliance violations
        """

        response = await self.client.messages.create(
            model="claude-3-opus-20240229",
            temperature=0,
            messages=[{"role": "user", "content": prompt}]
        )

        return {
            "validation_time": datetime.utcnow().isoformat(),
            "risks": self._extract_risks(response.content),
            "compliance": self._check_compliance(response.content),
            "recommendations": self._get_recommendations(response.content)
        }

Security Policy Generation

# GitOps-based Security Policy Generation
apiVersion: policy.fluxcd.io/v1
kind: PolicyGenerator
metadata:
  name: security-policies
spec:
  interval: 1h
  llmConfig:
    provider: anthropic
    model: claude-3-opus-20240229
    prompts:
webhooks:
  - name: validate.security.io
    clientConfig:
      service:
        name: llm-validator
        namespace: security
        path: "/validate"
    rules:
      - operations: ["CREATE", "UPDATE"]
        apiGroups: ["*"]
        apiVersions: ["*"]
        resources: ["pods", "deployments", "services"]

# LLM Validation Logic
class K8sValidator:
    def __init__(self):
        self.client = Anthropic()
        
    async def validate_manifest(self, manifest):
        prompt = f"""
        Validate this Kubernetes manifest for security issues:
        {manifest}
        
        Check for:
        1. Container security settings
        2. Network policies
        3. Resource constraints
        4. Service account permissions
        5. Security context
        """
        
        response = await self.client.messages.create(
            model="claude-3-opus-20240229",
            temperature=0,
            messages=[{
                "role": "user",
                "content": prompt
            }]
        )
        
        validation = self._parse_validation(response.content)
        return {
            "allowed": validation["risk_level"] < 8,
            "risks": validation["risks"],
            "recommendations": validation["recommendations"]
        }

Pipeline Configuration Generator

# CI/CD Pipeline Generator with LLM
class PipelineGenerator:
    def __init__(self):
        self.client = Anthropic()
        
    async def generate_pipeline(self, project_analysis):
        prompt = f"""
        Generate a secure CI/CD pipeline configuration for:
        {json.dumps(project_analysis, indent=2)}
        
        Include:
        1. Security scanning stages
        2. Compliance checks
        3. Automated testing
        4. Deployment safeguards
        5. Monitoring integration
        """
        
        response = await self.client.messages.create(
            model="claude-3-opus-20240229",
            temperature=0,
            messages=[{
                "role": "user",
                "content": prompt
            }]
        )
        
        return self._format_pipeline_config(response.content)

Incident Response Automation

# Automated Incident Response with LLM
class IncidentResponder:
    def __init__(self):
        self.client = Anthropic()
        self.incident_history = []
        
    async def handle_incident(self, alert_data):
        # Add context from similar past incidents
        relevant_history = self._find_similar_incidents(alert_data)
        
        prompt = f"""
        Create an incident response plan for:
        Alert: {json.dumps(alert_data, indent=2)}
        
        Similar past incidents:
        {json.dumps(relevant_history, indent=2)}
        
        Provide:
        1. Severity assessment
        2. Immediate actions
        3. Investigation steps
        4. Containment strategy
        5. Recovery procedures
        """
        
        response = await self.client.messages.create(
            model="claude-3-opus-20240229",
            temperature=0,
            messages=[{
                "role": "user",
                "content": prompt
            }]
        )
        
        return self._execute_response_plan(response.content)

Best Practices for LLM Integration

1. Rate Limiting and Caching

from functools import lru_cache
import time

class RateLimitedLLM:
    def __init__(self, calls_per_minute=60):
        self.rate_limit = calls_per_minute
        self.calls = []
        
    def _check_rate_limit(self):
        now = time.time()
        minute_ago = now - 60
        self.calls = [t for t in self.calls if t > minute_ago]
        return len(self.calls) < self.rate_limit
    
    @lru_cache(maxsize=1000)
    async def query(self, prompt):
        if not self._check_rate_limit():
            raise RateLimitExceeded("Rate limit exceeded")
        self.calls.append(time.time())
        return await self._make_request(prompt)

1. Error Handling

class LLMHandler:
    def __init__(self):
        self.client = Anthropic()
        self.fallback_client = OpenAI()
        
    async def safe_query(self, prompt, retries=3):
        for attempt in range(retries):
            try:
                return await self.client.messages.create(
                    model="claude-3-opus-20240229",
                    messages=[{"role": "user", "content": prompt}]
                )
            except Exception as e:
                if attempt == retries - 1:
                    return await self._fallback_query(prompt)
                await asyncio.sleep(2 ** attempt)

1. Context Management

class ContextManager:
    def __init__(self, max_size=10):
        self.context = []
        self.max_size = max_size
        
    def add_context(self, item):
        self.context.append(item)
        if len(self.context) > self.max_size:
            self.context.pop(0)
            
    def get_relevant_context(self, query):
        return [c for c in self.context 
                if self._calculate_relevance(c, query) > 0.7]

1. Security Considerations

class SecureLLM:
    def __init__(self):
        self.client = Anthropic()

11. Supply Chain Security with LLM Integration

SBOM Analysis and Validation

# AI-Enhanced SBOM Analysis
from anthropic import Anthropic
import cyclonedx
import json

class SBOMAnalyzer:
    def __init__(self, api_key: str):
        self.client = Anthropic(api_key=api_key)
        
    async def analyze_sbom(self, sbom_data: str) -> dict:
        prompt = f"""
        Analyze this Software Bill of Materials (SBOM):
        {sbom_data}

        Identify:
        1. High-risk dependencies
        2. Known vulnerabilities
        3. License compliance issues
        4. Supply chain risks
        5. Outdated components
        """

        response = await self.client.messages.create(
            model="claude-3-opus-20240229",
            temperature=0,
            messages=[{"role": "user", "content": prompt}]
        )

        return {
            "risks": self._extract_risks(response.content),
            "recommendations": self._extract_recommendations(response.content),
            "compliance": self._check_compliance(response.content)
        }

Artifact Signing and Verification

# Cosign Configuration with LLM Validation
apiVersion: security.cosign.io/v1alpha1
kind: SignaturePolicy
metadata:
  name: artifact-verification
spec:
  images:
    - glob: "registry.company.com/**"
  authorities:
    - name: company-authority
      key: company-signing-key
      
  validationHooks:
    - name: llm-validation
      type: webhook
      endpoint: "http://llm-validator:8080/validate"
      timeout: 30s

# LLM Validation Service
class ArtifactValidator:
    def __init__(self, api_key: str):
        self.client = Anthropic(api_key=api_key)
        
    async def validate_artifact(self, artifact_metadata: dict) -> bool:
        prompt = f"""
        Validate this artifact for security risks:
        {json.dumps(artifact_metadata, indent=2)}

        Check:
        1. Build provenance
        2. Signature verification
        3. Source repository validation
        4. Build environment security
        5. Dependency chain
        """

        response = await self.client.messages.create(
            model="claude-3-opus-20240229",
            temperature=0,
            messages=[{"role": "user", "content": prompt}]
        )

        validation = self._parse_validation(response.content)
        return validation["risk_score"] < self.risk_threshold

Dependency Update Automation

# Intelligent Dependency Updater
class DependencyManager:
    def __init__(self, api_key: str):
        self.client = Anthropic(api_key=api_key)
        
    async def analyze_update(self, 
                           package: str, 
                           current_version: str, 
                           new_version: str) -> dict:
        changelog = await self._fetch_changelog(package, current_version, new_version)
        
        prompt = f"""
        Analyze this dependency update:
        Package: {package}
        Current: {current_version}
        New: {new_version}

        Changelog:
        {changelog}

        Assess:
        1. Breaking changes
        2. Security implications
        3. Performance impact
        4. Compatibility issues
        5. Required adaptations
        """

        response = await self.client.messages.create(
            model="claude-3-opus-20240229",
            temperature=0,
            messages=[{"role": "user", "content": prompt}]
        )

        return self._structure_analysis(response.content)

Container Image Security

# AI-Enhanced Container Security
class ContainerSecurityAnalyst:
    def __init__(self, api_key: str):
        self.client = Anthropic(api_key=api_key)
        
    async def analyze_dockerfile(self, dockerfile: str) -> dict:
        prompt = f"""
        Analyze this Dockerfile for security best practices:
        {dockerfile}

        Check for:
        1. Base image security
        2. Layer optimization
        3. Secret management
        4. Permission settings
        5. Update practices
        6. CVE vulnerabilities
        """

        response = await self.client.messages.create(
            model="claude-3-opus-20240229",
            temperature=0,
            messages=[{"role": "user", "content": prompt}]
        )

        return {
            "security_score": self._calculate_score(response.content),
            "violations": self._extract_violations(response.content),
            "recommendations": self._get_recommendations(response.content)
        }

Supply Chain Monitoring

# Supply Chain Monitoring Configuration
apiVersion: monitoring.security.io/v1
kind: SupplyChainMonitor
metadata:
  name: supply-chain-security
spec:
  sources:
    - type: github
      repositories:
        - org/repo1
        - org/repo2
    - type: container-registry
      registries:
        - registry.company.com
    - type: artifact-repository
      repositories:
        - type: maven
          url: https://maven.company.com
        - type: npm
          url: https://npm.company.com

  analysis:
    llm:
      provider: anthropic
      model: claude-3-opus-20240229
      analyzers:
        - type: dependency
          schedule: "0 */6 * * *"
        - type: vulnerability
          schedule: "0 */4 * * *"
        - type: license
          schedule: "0 0 * * *"

  alerts:
    - name: critical-vulnerability
      severity: critical
      channels:
        - slack: "#security-alerts"
        - email: "[e-mail]"
    - name: license-violation
      severity: high
      channels:
        - jira:
            project: COMPLIANCE
            type: Security

Best Practices for Supply Chain Security

1. Continuous Verification

• Regular SBOM generation and analysis

• Automated dependency updates

• Container image scanning

• Build environment security

• Artifact signing and verification

1. Risk Management

• Supply chain threat modeling

• Vendor security assessment

• Third-party code review

• Dependency impact analysis

• Update strategy planning

1. Compliance and Documentation

• License compliance tracking

• Security documentation

• Audit trail maintenance

• Policy enforcement

• Incident response procedures

1. Monitoring and Alerts

• Real-time vulnerability monitoring

• Dependency update notifications

• Security scoring

• Compliance violations

• Build process anomalies

Remember to:

• Regularly update security tools

• Monitor supply chain threats

• Maintain security documentation

• Train teams on security practices

• Review and update policies

• Validate third-party components

• Implement least privilege access

• Use version pinning

• Monitor build environments