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 classifications

Static and Dynamic Code Analysis with AI

• Performing static analysis using SonarQube, Semgrep, or Snyk Code
• Dynamic testing through AI-assisted test case generation
• Analyzing results and integrating with version control systems

Secrets and Credential Leak Detection

• Detecting hardcoded secrets using AI-enhanced tools (e.g., GitHub Advanced Security, Gitleaks)
• Preventing the introduction of secrets into source control
• Establishing automatic blocking and alerting rules

AI-Powered Dependency and Container Scanning

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

Intelligent Threat Modeling and Risk Assessment

• Conducting automated threat modeling with AI-based tools
• Prioritizing risks using machine learning models
• Correlating business impact with technical vulnerabilities

CI/CD Pipeline Integration and Automation

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

Case Studies and Security Automation Patterns

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

Summary and Next Steps