DevSecOps with AI: Automating Security in the Pipeline Training Course

Introduction to DevSecOps and AI Integration

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

Static and Dynamic Code Analysis with AI

• Utilizing SonarQube, Semgrep, or Snyk Code for static analysis
• Conducting dynamic testing via AI-assisted test case generation
• Analyzing results and integrating findings with version control systems

Secrets and Credential Leak Detection

• Enhanced detection of hardcoded secrets using tools like GitHub Advanced Security or Gitleaks
• Strategies to prevent secrets from entering source control
• Establishing automated blocking and alerting mechanisms

AI-Powered Dependency and Container Scanning

• Scanning containers using Trivy and AI-enabled plugins
• Tracking third-party libraries and SBOMs
• Receiving automated remediation advice and patch notifications

Intelligent Threat Modeling and Risk Assessment

• Performing automated threat modeling with AI-based tools
• Prioritizing risks through machine learning models
• Connecting business impact with technical vulnerabilities

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
• Producing AI-assisted reports for audits and compliance purposes

Case Studies and Security Automation Patterns

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

Summary and Next Steps