DevSecOps with AI: Automating Security in the Pipeline Training Course

Foundations of DevSecOps and AI Integration

• Core principles and objectives of DevSecOps
• The impact of AI and Machine Learning in DevSecOps
• Current trends in security automation and tool classification

AI-Enhanced Static and Dynamic Code Analysis

• Leveraging SonarQube, Semgrep, or Snyk Code for static analysis
• Conducting dynamic tests via AI-assisted test case creation
• Analyzing outcomes and synchronizing with version control systems

Detection of Secrets and Credential Leaks

• Employing AI-enhanced tools like GitHub Advanced Security or Gitleaks to identify hardcoded secrets
• Preventing secrets from being committed to source control
• Establishing automated blocking mechanisms and alerting protocols

AI-Driven Dependency and Container Scanning

• Scanning containers using Trivy and AI-capable plugins
• Tracking third-party libraries and SBOMs
• Providing automated remediation guidance and patch notifications

Intelligent Threat Modeling and Risk Evaluation

• Automating threat modeling with AI-based solutions
• Prioritizing risks using machine learning models
• Correlating business impact with technical vulnerabilities

Integration and Automation within CI/CD Pipelines

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

Case Studies and Security Automation Frameworks

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

Recap and Path Forward