Security Operations with AI

Security operations have evolved from reactive firefighting to proactive, AI-driven defense systems. This guide explores how AI transforms DevSecOps, enabling automated vulnerability detection, intelligent threat analysis, and rapid remediation at scale.

Understanding AI-Powered DevSecOps

Modern security operations integrate AI throughout the development lifecycle:

Shift-Left Security

Early Detection: Find vulnerabilities during development
Automated Scanning: Continuous security analysis
Developer-Friendly: Integrated into existing workflows
Cost-Effective: Fix issues before production

Intelligent Threat Detection

Behavioral Analysis: Detect anomalies in real-time
Pattern Recognition: Identify attack signatures
Predictive Analytics: Anticipate security threats
Adaptive Defense: Learn from new attack vectors

Setting Up AI Security Stack

PRD: AI-Powered Security Operations Platform

# Security Operations Platform PRD

## Objective
Implement comprehensive AI-driven security scanning and threat detection

## Requirements
- Automated vulnerability scanning across code and dependencies
- Real-time threat detection and response
- Compliance automation for SOC2, GDPR, HIPAA
- Secret scanning and rotation
- Container and Kubernetes security

## Success Metrics
- < 1 hour mean time to detect (MTTD)
- < 4 hours mean time to respond (MTTR)
- 100% secret scanning coverage
- Zero critical vulnerabilities in production

Implementation Plan

# Use security MCP servers for comprehensive coverage
"Connect to GitHub MCP for repository security scanning"
"Connect to Sentry MCP for security event monitoring"
"Connect to SonarQube MCP for code quality and security"
"Connect to AWS/Azure MCP for cloud security posture"

# Plan the security implementation
"Based on MCP analysis, create security implementation plan:
1. Code scanning infrastructure
2. Runtime threat detection
3. Compliance automation
4. Incident response workflows"

Todo List

- [ ] Set up security MCP server connections
- [ ] Deploy code scanning pipeline
- [ ] Implement secret detection
- [ ] Configure runtime monitoring
- [ ] Create compliance reports
- [ ] Set up incident response
- [ ] Test security workflows
- [ ] Document security procedures

Install Security Tools and AI Assistants

Node.js
Python

# Install security scanning tools
npm install --save-dev \
  @snyk/protect \
  npm-audit-html \
  eslint-plugin-security \
  better-sqlite3-multiple-ciphers

# Install AI integration libraries
npm install @anthropic-ai/sdk openai zod

# Install security tools
pip install \
  bandit \
  safety \
  semgrep \
  detect-secrets

# Install AI libraries
pip install anthropic openai langchain

Configure AI-Powered Security Scanner

import { Anthropic } from '@anthropic-ai/sdk';
import { execSync } from 'child_process';
import * as fs from 'fs';

interface SecurityFinding {
  severity: 'critical' | 'high' | 'medium' | 'low';
  type: string;
  file: string;
  line: number;
  description: string;
  recommendation: string;
  aiConfidence: number;
}

class AISecurityScanner {
  private anthropic: Anthropic;

  constructor() {
    this.anthropic = new Anthropic({
      apiKey: process.env.ANTHROPIC_API_KEY,
    });
  }

  async scanCodebase(): Promise<SecurityFinding[]> {
    // Run traditional security tools
    const semgrepResults = this.runSemgrep();
    const dependencyResults = this.scanDependencies();
    const secretsResults = this.scanSecrets();

    // Analyze with AI for context and patterns
    const aiAnalysis = await this.analyzeWithAI({
      semgrep: semgrepResults,
      dependencies: dependencyResults,
      secrets: secretsResults
    });

    return this.consolidateFindings(aiAnalysis);
  }

  private async analyzeWithAI(scanResults: any) {
    const prompt = `
    Analyze these security scan results and:
    1. Identify critical security patterns
    2. Detect potential attack chains
    3. Suggest remediation priorities
    4. Find false positives

    Results: ${JSON.stringify(scanResults)}
    `;

    const response = await this.anthropic.messages.create({
      model: 'claude-3-opus-20240229',
      messages: [{ role: 'user', content: prompt }],
      max_tokens: 4096,
    });

    return JSON.parse(response.content[0].text);
  }

  private runSemgrep(): any {
    // AI-generated custom rules
    const customRules = `
    rules:
      - id: ai-sql-injection
        pattern-either:
          - pattern: |
              $QUERY = f"SELECT * FROM $TABLE WHERE $COLUMN = {$USER_INPUT}"
          - pattern: |
              db.execute($STR + $USER_INPUT)
        message: Potential SQL injection vulnerability
        severity: ERROR

      - id: ai-auth-bypass
        pattern: |
          if ($USER.role == "admin" or $PARAM == "debug")
        message: Potential authentication bypass
        severity: WARNING
    `;

    fs.writeFileSync('.semgrep-ai.yml', customRules);
    const output = execSync('semgrep --config=.semgrep-ai.yml --json .');
    return JSON.parse(output.toString());
  }
}

Implement Automated Vulnerability Remediation

import { AISecurityScanner } from './security-scanner';
import { generatePatch } from './ai-patch-generator';

class AutoRemediator {
  private scanner: AISecurityScanner;

  async remediateVulnerabilities() {
    const findings = await this.scanner.scanCodebase();

    for (const finding of findings) {
      if (finding.severity === 'critical' && finding.aiConfidence > 0.9) {
        await this.autoFix(finding);
      } else {
        await this.createSecurityPR(finding);
      }
    }
  }

  private async autoFix(finding: SecurityFinding) {
    // Generate fix with AI
    const patch = await generatePatch({
      vulnerability: finding,
      context: await this.getCodeContext(finding.file, finding.line),
      securityBestPractices: this.loadSecurityPatterns()
    });

    // Apply patch with validation
    if (await this.validatePatch(patch)) {
      await this.applyPatch(patch);
      await this.runSecurityTests();
    }
  }

  private async createSecurityPR(finding: SecurityFinding) {
    const prBody = `
    ## Security Finding: ${finding.type}

    **Severity**: ${finding.severity}
    **File**: ${finding.file}:${finding.line}
    **Confidence**: ${finding.aiConfidence * 100}%

    ### Description
    ${finding.description}

    ### Recommendation
    ${finding.recommendation}

    ### AI-Generated Fix
    \`\`\`diff
    ${await this.generateSuggestedFix(finding)}
    \`\`\`

    ### Security Impact Analysis
    ${await this.analyzeSecurityImpact(finding)}
    `;

    await this.createPullRequest({
      title: `[Security] Fix ${finding.type} in ${finding.file}`,
      body: prBody,
      branch: `security-fix-${finding.type}-${Date.now()}`
    });
  }
}

Continuous Security Monitoring

Real-Time Threat Detection

# PRD: Real-time Security Monitoring
# Use MCP servers for comprehensive monitoring

"Connect to Sentry MCP for security events:
1. Get recent security alerts
2. Analyze error patterns
3. Identify potential attacks
4. Generate threat report"

"Use GitHub MCP for code security:
1. Scan for exposed secrets
2. Check security advisories
3. Review PR security impacts
4. Monitor dependency vulnerabilities"

import { EventEmitter } from 'events';
import { OpenTelemetry } from '@opentelemetry/api';
import { anomalyDetection } from './ml-models';

class AIThreatDetector extends EventEmitter {
  private models = {
    behavioral: null,
    pattern: null,
    anomaly: null
  };

  async detectThreats(telemetryData: any) {
    // Behavioral analysis
    const behaviorScore = await this.analyzeBehavior(telemetryData);

    // Pattern matching against known attacks
    const patternMatches = await this.matchAttackPatterns(telemetryData);

    // Anomaly detection
    const anomalies = await this.detectAnomalies(telemetryData);

    // AI correlation engine
    const threatAssessment = await this.correlateFindings({
      behavior: behaviorScore,
      patterns: patternMatches,
      anomalies: anomalies
    });

    if (threatAssessment.risk > 0.8) {
      this.emit('critical-threat', threatAssessment);
      await this.initiateResponse(threatAssessment);
    }
  }

  private async analyzeBehavior(data: any) {
    // AI-powered behavioral analysis
    const features = this.extractBehavioralFeatures(data);
    const prediction = await this.models.behavioral.predict(features);

    // Check for:
    // - Unusual access patterns
    // - Privilege escalation attempts
    // - Data exfiltration patterns
    // - Lateral movement indicators

    return {
      score: prediction.confidence,
      indicators: prediction.behaviors,
      recommendations: await this.generateBehaviorResponse(prediction)
    };
  }

  private async initiateResponse(threat: any) {
    // Automated response orchestration
    const response = await this.planResponse(threat);

    // Execute response actions
    for (const action of response.actions) {
      await this.executeAction(action);
      await this.logAction(action);
    }

    // Notify security team
    await this.notifySecurityTeam(threat, response);
  }
}

Intelligent Log Analysis

class SecurityLogAnalyzer {
  private aiClient: Anthropic;

  async analyzeLogs(timeWindow: string) {
    const logs = await this.fetchLogs(timeWindow);

    // AI-powered log correlation
    const analysis = await this.aiClient.messages.create({
      model: 'claude-3-opus-20240229',
      messages: [{
        role: 'user',
        content: `
        Analyze these security logs for:
        1. Attack patterns (brute force, SQL injection, XSS, etc.)
        2. Anomalous user behavior
        3. System compromise indicators
        4. Data exfiltration attempts
        5. Correlation between events

        Logs: ${JSON.stringify(logs)}

        Provide structured analysis with severity ratings.
        `
      }],
      max_tokens: 4096
    });

    return this.processAIAnalysis(analysis);
  }

  async detectAttackChains() {
    // Multi-stage attack detection
    const query = `
      WITH attack_sequences AS (
        SELECT
          user_id,
          ip_address,
          array_agg(event_type ORDER BY timestamp) as event_chain,
          array_agg(timestamp ORDER BY timestamp) as timestamps
        FROM security_events
        WHERE timestamp > NOW() - INTERVAL '1 hour'
        GROUP BY user_id, ip_address
      )
      SELECT * FROM attack_sequences
      WHERE event_chain @> ARRAY['failed_login', 'privilege_check', 'data_access']
    `;

    const chains = await this.db.query(query);
    return this.analyzeAttackChains(chains);
  }
}

Container and Kubernetes Security

# Use Kubernetes MCP for cluster security
"Connect to Kubernetes MCP and:
1. Scan pod security policies
2. Check RBAC configurations
3. Identify exposed services
4. Review network policies
5. Audit container images"

Container Scanning
K8s Policies

apiVersion: v1
kind: ConfigMap
metadata:
  name: ai-security-scanner
data:
  scanner.py: |
    import anthropic
    import docker
    import json

    class ContainerSecurityScanner:
        def __init__(self):
            self.client = docker.from_env()
            self.ai = anthropic.Client()

        def scan_image(self, image_name):
            # Layer analysis
            layers = self.analyze_layers(image_name)

            # Vulnerability scanning
            vulns = self.scan_vulnerabilities(image_name)

            # Configuration audit
            config = self.audit_configuration(image_name)

            # AI security assessment
            assessment = self.ai_assessment({
                'layers': layers,
                'vulnerabilities': vulns,
                'configuration': config
            })

            return self.generate_report(assessment)

        def analyze_layers(self, image):
            # Check for security anti-patterns
            antipatterns = [
                'running as root',
                'exposed secrets',
                'unnecessary packages',
                'outdated base images'
            ]

            findings = []
            for layer in self.get_layers(image):
                for pattern in antipatterns:
                    if self.check_pattern(layer, pattern):
                        findings.append({
                            'layer': layer.id,
                            'issue': pattern,
                            'severity': self.assess_severity(pattern)
                        })

            return findings

apiVersion: policy/v1beta1
kind: PodSecurityPolicy
metadata:
  name: ai-enforced-security
spec:
  privileged: false
  allowPrivilegeEscalation: false
  requiredDropCapabilities:
    - ALL
  volumes:
    - 'configMap'
    - 'emptyDir'
    - 'projected'
    - 'secret'
    - 'downwardAPI'
    - 'persistentVolumeClaim'
  hostNetwork: false
  hostIPC: false
  hostPID: false
  runAsUser:
    rule: 'MustRunAsNonRoot'
  seLinux:
    rule: 'RunAsAny'
  supplementalGroups:
    rule: 'RunAsAny'
  fsGroup:
    rule: 'RunAsAny'
  readOnlyRootFilesystem: true
---
# Network policy with AI-based rules
apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
  name: ai-adaptive-network-policy
spec:
  podSelector:
    matchLabels:
      security-tier: sensitive
  policyTypes:
    - Ingress
    - Egress
  ingress:
    - from:
      - podSelector:
          matchLabels:
            security-clearance: high
      ports:
      - protocol: TCP
        port: 443
  egress:
    - to:
      - podSelector:
          matchLabels:
            service: security-scanner
      ports:
      - protocol: TCP
        port: 8080

API Security Automation

# PRD: API Security Gateway
# Plan: Implement comprehensive API protection

"Use Context7 to research:
1. OWASP API Security Top 10
2. Rate limiting best practices
3. JWT security patterns
4. API gateway implementations"

# Todo:
# - [ ] Implement rate limiting
# - [ ] Add authentication middleware
# - [ ] Deploy WAF rules
# - [ ] Set up API monitoring
# - [ ] Create security headers

import { RequestHandler } from 'express';
import { z } from 'zod';
import { RateLimiter } from './rate-limiter';
import { ThreatIntelligence } from './threat-intel';

class AISecurityGateway {
  private threatIntel: ThreatIntelligence;
  private rateLimiter: RateLimiter;

  middleware(): RequestHandler {
    return async (req, res, next) => {
      try {
        // AI-powered request analysis
        const threatScore = await this.analyzeThreatLevel(req);

        if (threatScore > 0.9) {
          return res.status(403).json({ error: 'Suspicious activity detected' });
        }

        // Adaptive rate limiting
        const rateLimit = await this.calculateRateLimit(req, threatScore);
        if (!await this.rateLimiter.check(req, rateLimit)) {
          return res.status(429).json({ error: 'Rate limit exceeded' });
        }

        // Input validation with AI
        await this.validateInput(req);

        // Add security headers
        this.addSecurityHeaders(res, threatScore);

        next();
      } catch (error) {
        this.handleSecurityError(error, res);
      }
    };
  }

  private async analyzeThreatLevel(req: any): Promise<number> {
    const features = {
      ip: req.ip,
      userAgent: req.headers['user-agent'],
      path: req.path,
      method: req.method,
      body: req.body,
      headers: req.headers,
      geoLocation: await this.getGeoLocation(req.ip),
      timeOfDay: new Date().getHours(),
      requestPattern: await this.getRequestPattern(req)
    };

    // Check threat intelligence
    const threatData = await this.threatIntel.check(features.ip);

    // AI threat scoring
    const aiScore = await this.calculateAIThreatScore(features, threatData);

    // Log for continuous learning
    await this.logThreatAnalysis(features, aiScore);

    return aiScore;
  }

  private async validateInput(req: any) {
    // Dynamic schema generation based on endpoint
    const schema = await this.generateValidationSchema(req.path, req.method);

    // AI-enhanced validation
    const validationResult = await this.aiValidate(req.body, schema);

    if (!validationResult.success) {
      throw new SecurityError('Invalid input detected', validationResult.errors);
    }
  }
}

// OWASP Top 10 Protection
class OWASPProtection {
  async protectAgainstInjection(input: string): Promise<string> {
    // AI-based injection detection
    const injectionPatterns = await this.detectInjectionPatterns(input);

    if (injectionPatterns.length > 0) {
      throw new SecurityError('Injection attempt detected', injectionPatterns);
    }

    // Parameterized query generation
    return this.sanitizeForQuery(input);
  }

  async preventXSS(content: string): Promise<string> {
    // Context-aware XSS prevention
    const context = await this.detectRenderContext(content);

    switch (context) {
      case 'html':
        return this.escapeHTML(content);
      case 'javascript':
        return this.escapeJS(content);
      case 'css':
        return this.escapeCSS(content);
      case 'url':
        return this.escapeURL(content);
      default:
        return this.escapeAll(content);
    }
  }
}

Security Testing Integration

Using Playwright MCP for Security Testing

# Connect Playwright MCP for security testing
"Connect to Playwright MCP for automated security testing"

# Use MCP to test security scenarios
"Using Playwright MCP:
1. Test SQL injection vulnerabilities
2. Verify XSS protection
3. Check authentication flows
4. Test CSRF protection
5. Validate secure headers"

Setup AI-Powered Security Testing

import { test, expect } from '@playwright/test';
import { SecurityTester } from './ai-security-tester';

const securityTester = new SecurityTester();

test.describe('AI Security Tests', () => {
  test('SQL Injection Protection', async ({ page }) => {
    const payloads = await securityTester.generateSQLIPayloads();

    for (const payload of payloads) {
      await page.goto('/login');
      await page.fill('#username', payload.username);
      await page.fill('#password', payload.password);
      await page.click('#submit');

      // Verify protection
      const response = await page.waitForResponse('**/api/login');
      expect(response.status()).not.toBe(500);
      expect(await page.textContent('.error')).toContain('Invalid credentials');
    }
  });

  test('XSS Protection', async ({ page }) => {
    const xssVectors = await securityTester.generateXSSVectors();

    for (const vector of xssVectors) {
      await page.goto('/comment');
      await page.fill('#comment', vector);
      await page.click('#submit');

      // Check if XSS is prevented
      const content = await page.textContent('.comment-display');
      expect(content).not.toContain('<script>');
      expect(content).toBe(securityTester.expectedSanitized(vector));
    }
  });

  test('Authentication Bypass Attempts', async ({ page }) => {
    const bypassAttempts = await securityTester.generateAuthBypass();

    for (const attempt of bypassAttempts) {
      const response = await page.request.get('/api/admin', {
        headers: attempt.headers,
        params: attempt.params
      });

      expect(response.status()).toBe(401);
    }
  });
});

Implement Continuous Security Scanning

name: AI Security Scan

on:
  push:
  pull_request:
  schedule:
    - cron: '0 */4 * * *'  # Every 4 hours

jobs:
  security-scan:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3

      - name: AI-Powered Security Scan
        run: |
          npm run security:ai-scan

      - name: Dependency Vulnerability Check
        run: |
          npm audit --json > audit.json
          node scripts/ai-analyze-vulnerabilities.js audit.json

      - name: Container Security Scan
        run: |
          docker build -t app:scan .
          docker run --rm \
            -v /var/run/docker.sock:/var/run/docker.sock \
            -v $(pwd):/workspace \
            ai-security-scanner:latest \
            scan app:scan

      - name: SAST with AI Analysis
        run: |
          semgrep --config=auto --json > semgrep.json
          node scripts/ai-analyze-sast.js semgrep.json

      - name: AI Threat Modeling
        run: |
          node scripts/ai-threat-model.js \
            --architecture docs/architecture.md \
            --data-flow docs/data-flow.md \
            --output threat-model.json

Compliance Automation

# PRD: Automated Compliance Management
# Plan: Implement compliance scanning and reporting

"Use relevant MCPs for compliance:
1. GitHub MCP for code compliance
2. Database MCP for data privacy
3. Cloud MCPs for infrastructure compliance
4. Document findings and gaps"

# Todo:
# - [ ] Map compliance requirements
# - [ ] Scan for violations
# - [ ] Generate evidence
# - [ ] Create audit reports
# - [ ] Automate remediation

class AIComplianceAutomation {
  private standards = {
    'SOC2': this.loadSOC2Requirements(),
    'GDPR': this.loadGDPRRequirements(),
    'HIPAA': this.loadHIPAARequirements(),
    'PCI-DSS': this.loadPCIDSSRequirements()
  };

  async assessCompliance(standard: string) {
    const requirements = this.standards[standard];
    const evidence = await this.collectEvidence();

    // AI compliance assessment
    const assessment = await this.aiAssess({
      standard,
      requirements,
      evidence,
      context: await this.getOrganizationContext()
    });

    return {
      score: assessment.complianceScore,
      gaps: assessment.gaps,
      recommendations: assessment.recommendations,
      automationOpportunities: assessment.automatable
    };
  }

  async generateComplianceReport(standard: string) {
    const assessment = await this.assessCompliance(standard);

    // AI-generated report
    const report = await this.aiClient.messages.create({
      model: 'claude-3-opus-20240229',
      messages: [{
        role: 'user',
        content: `
        Generate a comprehensive ${standard} compliance report:

        Assessment: ${JSON.stringify(assessment)}

        Include:
        1. Executive summary
        2. Detailed findings
        3. Risk analysis
        4. Remediation roadmap
        5. Evidence documentation
        `
      }],
      max_tokens: 8192
    });

    return this.formatReport(report);
  }

  async automateControls(gaps: any[]) {
    for (const gap of gaps) {
      if (gap.automatable) {
        const control = await this.generateControl(gap);
        await this.deployControl(control);
        await this.validateControl(control);
      }
    }
  }
}

// Privacy compliance automation
class PrivacyAutomation {
  async implementPrivacyByDesign() {
    // Data minimization
    await this.implementDataMinimization();

    // Consent management
    await this.deployConsentManagement();

    // Right to deletion
    await this.implementDataDeletion();

    // Data portability
    await this.enableDataExport();
  }

  async scanForPII() {
    const query = `
      SELECT
        table_name,
        column_name,
        data_type,
        ai_classify_pii(sample_data) as pii_type,
        ai_sensitivity_score(sample_data) as sensitivity
      FROM information_schema.columns
      JOIN LATERAL (
        SELECT array_agg(column_name) as sample_data
        FROM table_name
        LIMIT 100
      ) samples ON true
      WHERE ai_contains_pii(column_name, data_type)
    `;

    const piiLocations = await this.db.query(query);
    return this.generatePIIReport(piiLocations);
  }
}

MCP Security Integrations

Using MCP Servers for Security

# Available Security MCP Servers

# 1. GitHub MCP for repository security
"Use GitHub MCP to:
- Scan for exposed secrets
- Monitor security advisories
- Check branch protection rules
- Review security policies"

# 2. Sentry MCP for monitoring
"Use Sentry MCP to:
- Track security events
- Monitor error patterns
- Detect anomalies
- Generate alerts"

# 3. SonarQube MCP for code security
"Use SonarQube MCP to:
- Scan for vulnerabilities
- Check security hotspots
- Monitor code quality
- Track technical debt"

# 4. Cloud Provider MCPs
"Use AWS/Azure/GCP MCPs to:
- Check IAM policies
- Scan for misconfigurations
- Monitor security groups
- Audit resource access"

import { MCPClient } from '@modelcontextprotocol/sdk';

class SecurityMCPIntegration {
  private clients = {
    github: new MCPClient('github-mcp-server'),
    sentry: new MCPClient('sentry-mcp-server'),
    vault: new MCPClient('vault-mcp-server')
  };

  async scanGitHubSecrets() {
    // Scan for exposed secrets in GitHub
    const repos = await this.clients.github.listRepositories();

    for (const repo of repos) {
      const files = await this.clients.github.searchCode({
        query: 'password OR api_key OR secret',
        repo: repo.full_name
      });

      for (const file of files) {
        const content = await this.clients.github.getFileContent(file);
        const secrets = await this.detectSecrets(content);

        if (secrets.length > 0) {
          await this.remediateSecrets(repo, file, secrets);
        }
      }
    }
  }

  async monitorSecurityEvents() {
    // Real-time security monitoring with Sentry
    const events = await this.clients.sentry.getSecurityEvents({
      timeRange: '1h',
      severity: ['error', 'critical']
    });

    for (const event of events) {
      const analysis = await this.analyzeSecurityEvent(event);

      if (analysis.isThreat) {
        await this.respondToThreat(analysis);
      }
    }
  }

  async rotateSecrets() {
    // Automated secret rotation with Vault
    const secrets = await this.clients.vault.listSecrets();

    for (const secret of secrets) {
      if (await this.shouldRotate(secret)) {
        const newSecret = await this.generateSecureSecret(secret.type);
        await this.clients.vault.updateSecret(secret.path, newSecret);
        await this.updateApplicationSecrets(secret, newSecret);
      }
    }
  }
}

Best Practices

Security MCP Server Usage

Repository Security

# GitHub MCP for comprehensive repo security
"Use GitHub MCP to enable:
- Dependabot alerts
- Secret scanning
- Code scanning with CodeQL
- Security policies"

Monitoring and Alerting

# Sentry MCP for security monitoring
"Configure Sentry MCP to:
- Track authentication failures
- Monitor API abuse
- Detect injection attempts
- Alert on anomalies"

Infrastructure Security

# Cloud MCPs for infrastructure
"Use cloud MCPs to:
- Scan for misconfigurations
- Check compliance posture
- Monitor access patterns
- Audit resource changes"

Security as Code

export const securityPolicies = {
  authentication: {
    mfa: 'required',
    sessionTimeout: 3600,
    passwordPolicy: {
      minLength: 12,
      requireUppercase: true,
      requireNumbers: true,
      requireSpecial: true,
      preventReuse: 10
    }
  },
  encryption: {
    atRest: 'AES-256-GCM',
    inTransit: 'TLS 1.3',
    keyRotation: '90d'
  },
  monitoring: {
    logRetention: '1y',
    alertThreshold: 0.8,
    incidentResponse: '15m'
  }
};

Zero Trust Architecture

Never Trust: Verify everything
Least Privilege: Minimal access rights
Micro-segmentation: Isolate workloads
Continuous Verification: Real-time validation

Troubleshooting Security Issues

class BreachResponseAutomation {
  async handleDataBreach(incident: SecurityIncident) {
    // 1. Immediate containment
    await this.isolateAffectedSystems(incident.systems);

    // 2. Evidence collection
    const evidence = await this.collectForensicData(incident);

    // 3. Impact assessment
    const impact = await this.assessBreachImpact(evidence);

    // 4. Notification automation
    if (impact.affectsUsers) {
      await this.notifyAffectedUsers(impact.users);
    }

    // 5. Remediation
    await this.executeRemediationPlan(incident, impact);

    // 6. Post-incident analysis
    await this.generatePostMortem(incident, evidence, impact);
  }
}

class DDoSMitigation {
  async detectAndMitigate() {
    const trafficAnalysis = await this.analyzeTrafficPatterns();

    if (trafficAnalysis.isDDoS) {
      // Enable DDoS protection
      await this.enableCloudflareProtection();

      // Implement rate limiting
      await this.adaptiveRateLimiting(trafficAnalysis);

      // Block malicious IPs
      await this.blockMaliciousIPs(trafficAnalysis.sources);

      // Scale infrastructure
      await this.autoScale(trafficAnalysis.load);
    }
  }
}

Future of AI Security

The future of security operations will see:

Autonomous Security Systems: Self-healing infrastructure
Predictive Threat Intelligence: Anticipate attacks before they happen
Quantum-Resistant Cryptography: Prepare for quantum computing threats
AI vs AI: Defensive AI combating AI-powered attacks
Behavioral Biometrics: Advanced user authentication
Privacy-Preserving AI: Security without compromising privacy