DevSecOps with AI: Automating Security in the Pipeline Training Course

Introduction to DevSecOps and AI Integration

• Core principles and objectives of DevSecOps
• The function of AI and ML within DevSecOps frameworks
• Current trends in security automation and tool classifications

Static and Dynamic Code Analysis Utilizing AI

• Employing SonarQube, Semgrep, or Snyk Code for static analysis
• Conducting dynamic tests via AI-assisted test case generation
• Analyzing 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 rules

AI-Driven Dependency and Container Scanning

• Container scanning using Trivy and AI-enabled plugins
• Monitoring third-party libraries and SBOMs
• Generating automated remediation advice and patch notifications

Intelligent Threat Modeling and Risk Evaluation

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

CI/CD Pipeline Integration and Automation

• 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

Case Studies and Security Automation Patterns

• Real-world examples of AI application in security pipelines
• Selecting the most suitable tools for your specific ecosystem
• Best practices for developing and maintaining secure pipelines

Summary and Subsequent Steps