DevSecOps with AI: Automating Security in the Pipeline Training Course

DevSecOps Fundamentals and AI Integration

• Core DevSecOps principles and objectives
• The contribution of AI and ML to DevSecOps practices
• Emerging trends in security automation and tool classification

AI-Enhanced Static and Dynamic Code Analysis

• Employing SonarQube, Semgrep, or Snyk Code for static inspection
• Conducting dynamic tests via AI-assisted test case generation
• Analyzing outcomes and syncing with version control systems

Detecting Secrets and Credential Leaks

• Utilizing AI-boosted tools like GitHub Advanced Security or Gitleaks to find hardcoded secrets
• Blocking secrets from being committed to source control
• Setting up automatic blocking mechanisms and alerting rules

AI-Driven Dependency and Container Scanning

• Inspecting containers using Trivy and AI-enabled plugins
• Overseeing third-party libraries and generating SBOMs
• Receiving automated remediation suggestions and patch notifications

Smart Threat Modeling and Risk Evaluation

• Automating threat modeling using AI-based applications
• Prioritizing risks through machine learning models
• Connecting business impact to specific technical vulnerabilities

Integrating and Automating CI/CD Pipelines

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

Case Studies and Security Automation Frameworks

• Real-world instances of AI application in security pipelines
• Selecting appropriate tools for your specific ecosystem
• Best practices for constructing and sustaining secure pipelines

Conclusions and Future Directions