DevSecOps with AI: Automating Security in the Pipeline Training Course

Foundations of DevSecOps and AI Integration

• Core principles and objectives of DevSecOps
• The impact of AI and machine learning in DevSecOps
• Current trends in security automation and tool classifications

AI-Driven Static and Dynamic Code Analysis

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

Detection of Secrets and Credential Leaks

• Enhancing the detection of hardcoded secrets using tools like GitHub Advanced Security or Gitleaks
• Strategies to prevent secrets from being committed to source control
• Establishing automated blocking and alerting mechanisms

AI-Assisted Dependency and Container Scanning

• Scanning containers using Trivy and AI-enabled plugins
• Tracking third-party libraries and maintaining SBOMs
• Generating automated remediation suggestions and patch notifications

Intelligent Threat Modeling and Risk Evaluation

• Automating threat modeling with AI-based platforms
• Prioritizing risks using machine learning algorithms
• Aligning technical vulnerabilities with business impact

Integration and Automation in CI/CD Pipelines

• Embedding security checks into Jenkins, GitHub Actions, or GitLab CI
• Implementing policies-as-code to enforce rules across environments
• Producing AI-assisted reports for audit and compliance purposes

Real-World Case Studies and Automation Patterns

• Practical examples of AI application in security pipelines
• Selecting the appropriate tools for your specific ecosystem
• Best practices for developing and sustaining secure pipelines

Conclusion and Recommended Next Steps