DevSecOps with AI: Automating Security in the Pipeline Training Course

Introduction to DevSecOps and AI Integration

• Core principles and objectives of DevSecOps
• The contribution of AI and ML to DevSecOps
• Current trends in security automation and tool categorization

Static and Dynamic Code Analysis Enhanced by AI

• Performing static analysis with SonarQube, Semgrep, or Snyk Code
• Conducting dynamic testing via AI-assisted test case generation
• Interpreting results and integrating findings with version control systems

Detection of Secrets and Credential Leaks

• AI-enhanced identification of hardcoded secrets (e.g., GitHub Advanced Security, Gitleaks)
• Strategies to prevent secrets from entering source control
• Establishing automated blocking and alerting mechanisms

AI-Powered Dependency and Container Scanning

• Scanning containers using Trivy and AI-enabled plugins
• Monitoring third-party libraries and SBOMs
• Automated remediation suggestions and patch notifications

Intelligent Threat Modeling and Risk Assessment

• Automating threat modeling with AI-based solutions
• Prioritizing risks using machine learning models
• Correlating business impact with technical vulnerabilities

CI/CD Pipeline Integration and Automation

• Embedding security checks in Jenkins, GitHub Actions, or GitLab CI
• Developing policies-as-code to enforce rules across environments
• Generating AI-assisted reports for audits and compliance

Case Studies and Security Automation Patterns

• Real-world applications of AI in security pipelines
• Selecting appropriate tools for your specific ecosystem
• Best practices for building and sustaining secure pipelines

Summary and Future Steps