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 Machine Learning on DevSecOps practices
• Current trends in security automation and relevant tool categories

Static and Dynamic Code Analysis with AI

• Applying SonarQube, Semgrep, or Snyk Code for static code analysis
• Dynamic testing utilizing AI-assisted test case generation
• Analyzing outcomes and integrating findings with version control systems

Secrets and Credential Leak Detection

• Detecting hardcoded secrets using AI-enhanced solutions (e.g., GitHub Advanced Security, Gitleaks)
• Strategies to prevent secrets from being committed to source control
• Establishing automated blocking mechanisms and alerting rules

AI-Powered Dependency and Container Scanning

• Container scanning using Trivy and AI-enabled plugins
• Oversight of third-party libraries and Software Bill of Materials (SBOMs)
• Automated remediation advice and patch notifications

Intelligent Threat Modeling and Risk Assessment

• Utilizing AI-based tools for automated threat modeling
• Prioritizing risks through Machine Learning models
• Correlating business impact with technical vulnerabilities

CI/CD Pipeline Integration and Automation

• Embedding security controls within Jenkins, GitHub Actions, or GitLab CI
• Developing policies-as-code to enforce standards across environments
• Producing AI-assisted reports for audit and compliance purposes

Case Studies and Security Automation Patterns

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

Summary and Future Directions