DevSecOps with AI: Automating Security in the Pipeline Training Course

Introduction to DevSecOps and AI Integration

• Core principles and objectives of DevSecOps
• The contribution of AI and ML to DevSecOps practices
• Trends in security automation and categories of tools

AI-Assisted Static and Dynamic Code Analysis

• Applying SonarQube, Semgrep, or Snyk Code for static analysis
• Dynamic testing supported by AI-generated test cases
• Analyzing outcomes and integrating with version control systems

Detection of Secrets and Credential Leaks

• AI-enhanced identification of hardcoded secrets (e.g., GitHub Advanced Security, Gitleaks)
• Preventing the introduction of secrets into source control
• Establishing rules for automatic blocking and alerting

AI-Driven Dependency and Container Scanning

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

Smart Threat Modeling and Risk Assessment

• Automated threat modeling using AI-based tools
• Prioritizing risks with machine learning models
• Connecting technical vulnerabilities to business impact

CI/CD Pipeline Integration and Automation

• Embedding security checks in Jenkins, GitHub Actions, or GitLab CI
• Implementing policies-as-code to enforce rules across environments
• Generating AI-assisted reports for audit and compliance purposes

Case Studies and Security Automation Patterns

• Real-world applications of AI in security pipelines
• Selecting the appropriate tools for your ecosystem
• Best practices for developing and maintaining secure pipelines

Summary and Future Directions