DevSecOps with AI: Automating Security in the Pipeline Training Course

Foundational Insights into DevSecOps and AI Integration

• Core principles and objectives of DevSecOps
• The contribution of AI and ML to DevSecOps practices
• Emerging trends in security automation and relevant tool classes

Applying AI to Static and Dynamic Code Analysis

• Utilizing SonarQube, Semgrep, or Snyk Code for static inspection
• Conducting dynamic tests via AI-assisted test case creation
• Analyzing outcomes and synchronizing with version control systems

Detecting Secrets and Credential Leaks

• Employing AI-enhanced tools like GitHub Advanced Security or Gitleaks to find hardcoded secrets
• Stopping secrets from being committed to source control
• Establishing automated blocking mechanisms and alerting protocols

AI-Driven Dependency and Container Scanning

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

Intelligent Threat Modeling and Risk Evaluation

• Performing automated threat modeling with AI-based utilities
• Prioritizing risks through machine learning models
• Connecting technical vulnerabilities to broader business impacts

Integrating and Automating CI/CD Pipelines

• Embedding security checks within Jenkins, GitHub Actions, or GitLab CI
• Enforcing cross-environment rules via policies-as-code
• Producing AI-assisted reports for audit and compliance purposes

Real-World Case Studies and Automation Best Practices

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

Recap and Future Directions