DevSecOps with AI: Automating Security in the Pipeline Training Course

Introduction to DevSecOps and AI Integration

• Core principles and strategic goals of DevSecOps.
• The evolving role of AI and ML within the DevSecOps landscape.
• Current trends in security automation and classification of tool categories.

Static and Dynamic Code Analysis with AI

• Applying static analysis using tools such as SonarQube, Semgrep, or Snyk Code.
• Conducting dynamic testing through AI-assisted test case generation.
• Analyzing results and integrating findings with version control systems.

Secrets and Credential Leak Detection

• Enhancing the detection of hardcoded secrets using solutions like GitHub Advanced Security or Gitleaks.
• Preventing the introduction of sensitive secrets into source control.
• Developing automated blocking mechanisms and alerting rules.

AI-Powered Dependency and Container Scanning

• Scanning container images with Trivy and integrating AI-enabled plugins.
• Monitoring third-party libraries and managing Software Bills of Materials (SBOMs).
• Utilizing automated remediation recommendations and patch alerting systems.

Intelligent Threat Modeling and Risk Assessment

• Employing AI-based tools for automated threat modeling.
• Prioritizing risks through the application of machine learning models.
• Connecting technical vulnerabilities to broader business impact.

CI/CD Pipeline Integration and Automation

• Embedding security checks into Jenkins, GitHub Actions, or GitLab CI.
• Implementing policies-as-code to enforce standards across various environments.
• Generating AI-assisted reports to support audits and compliance efforts.

Case Studies and Security Automation Patterns

• Examining real-world examples of AI application in security pipelines.
• Selecting the most appropriate tools for your specific technology ecosystem.
• Adopting best practices for building and sustaining secure pipelines.

Summary and Next Steps