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 relevant tool categories

AI-Enhanced Static and Dynamic Code Analysis

• Implementing static analysis using SonarQube, Semgrep, or Snyk Code
• Conducting dynamic testing with AI-assisted test case generation
• Analyzing results and syncing with version control systems

Detecting Secrets and Credential Leaks

• Identifying hardcoded secrets using AI-enhanced tools like GitHub Advanced Security or Gitleaks
• Strategies to prevent secrets from being committed to source control
• Establishing automated blocking mechanisms and alert rules

AI-Driven Dependency and Container Scanning

• Scanning containers with Trivy and AI-capable plugins
• Tracking third-party libraries and Software Bill of Materials (SBOMs)
• Receiving automated remediation advice and patch notifications

Intelligent Threat Modeling and Risk Evaluation

• Performing automated threat modeling with AI-based utilities
• Prioritizing risks using machine learning models
• Correlating business impact with technical vulnerabilities

Integrating and Automating CI/CD Pipelines

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

Case Studies and Security Automation Best Practices

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

Recap and Future Recommendations