DevSecOps with AI: Automating Security in the Pipeline Training Course

Fundamentals of DevSecOps and AI Integration

• Core principles and objectives of DevSecOps.
• The function of AI and ML within DevSecOps strategies.
• Current trends in security automation and relevant tool categories.

Static and Dynamic Code Analysis using AI

• Applying SonarQube, Semgrep, or Snyk Code for static analysis.
• Conducting dynamic tests with AI-assisted test case creation.
• Analysing results and synchronising 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 sensitive data from entering source control.
• Establishing automated blocking mechanisms and alerting protocols.

AI-Driven Dependency and Container Scanning

• Scanning containers using Trivy and AI-enabled plugins.
• Overseeing third-party libraries and SBOMs.
• Automated remediation suggestions and patch notifications.

Intelligent Threat Modeling and Risk Evaluation

• Automated threat modeling supported by AI-based tools.
• Prioritising risks through the use of machine learning models.
• Correlating business impact with technical vulnerabilities.

CI/CD Pipeline Integration and Automation

• Incorporating security checks into Jenkins, GitHub Actions, or GitLab CI.
• Developing policies-as-code to apply rules consistently across environments.
• Producing AI-assisted reports for audit and compliance purposes.

Case Studies and Security Automation Patterns

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

Recap and Future Directions