DevSecOps with AI: Automating Security in the Pipeline Training Course

Foundations of DevSecOps and AI Integration

• Core principles and objectives of DevSecOps
• The function of AI and ML within DevSecOps frameworks
• Current trends in security automation and tool classifications

AI-Enhanced Static and Dynamic Code Analysis

• Applying static analysis with tools like SonarQube, Semgrep, or Snyk Code
• Conducting dynamic testing through AI-assisted test case generation
• Analyzing outcomes and syncing results with version control systems

Identifying Secrets and Credential Leaks

• Detecting hardcoded secrets using AI-enhanced solutions such as GitHub Advanced Security or Gitleaks
• Preventing the introduction of sensitive credentials into source control
• Establishing automated blocking mechanisms and alerting protocols

AI-Driven Dependency and Container Scanning

• Performing container scans with Trivy and AI-enabled plugins
• Overseeing third-party libraries and Software Bill of Materials (SBOMs)
• Generating automated remediation suggestions and patch notifications

Intelligent Threat Modeling and Risk Evaluation

• Utilizing AI-based tools for automated threat modeling
• Prioritizing risks through machine learning models
• Correlating business impacts with technical vulnerabilities

CI/CD Pipeline Integration and Workflow Automation

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

Case Studies and Security Automation Patterns

• Real-world applications of AI within security pipelines
• Selecting appropriate tools for your specific technology ecosystem
• Best practices for developing and sustaining secure pipelines

Wrap-up and Future Directions