DevSecOps with AI: Automating Security in the Pipeline Training Course

Fundamentals of DevSecOps and AI Integration

• Core principles and objectives of DevSecOps.
• The impact of AI and Machine Learning on DevSecOps practices.
• Current trends in security automation and an overview of tool categories.

AI-Enhanced Static and Dynamic Code Analysis

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

Detecting Secrets and Credential Leaks

• Utilizing AI-enhanced tools (such as GitHub Advanced Security or Gitleaks) to find hardcoded secrets.
• Strategies for preventing secrets from entering source control.
• Establishing automatic blocking mechanisms and alerting rules.

AI-Driven Dependency and Container Scanning

• Scanning containers using Trivy and AI-capable plugins.
• Monitoring third-party libraries and managing SBOMs.
• Generating automated remediation recommendations and patch notifications.

Intelligent Threat Modeling and Risk Evaluation

• Automating threat modeling processes with AI-based tools.
• Prioritizing risks using machine learning models.
• Correlating technical vulnerabilities with broader business impact.

Integrating and Automating CI/CD Pipelines

• Embedding security checks within Jenkins, GitHub Actions, or GitLab CI.
• Implementing policies-as-code to enforce rules consistently 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 constructing and maintaining secure pipelines.

Conclusion and Future Directions