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Duration 14 hours
Course Outline
Introduction to 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
Static and Dynamic Code Analysis with AI
- Utilizing SonarQube, Semgrep, or Snyk Code for static analysis
- Dynamic testing through AI-assisted test case generation
- Interpreting results and integrating with version control systems
Secrets and Credential Leak Detection
- AI-enhanced detection of hardcoded secrets (e.g., GitHub Advanced Security, Gitleaks)
- Preventing secrets from entering source control
- Establishing automatic blocking and alerting rules
AI-Powered Dependency and Container Scanning
- Scanning containers using Trivy and AI-enabled plugins
- Monitoring third-party libraries and SBOMs
- Automated remediation recommendations and patch alerts
Intelligent Threat Modeling and Risk Assessment
- Automated threat modeling utilizing AI-based tools
- Risk prioritization using machine learning models
- Connecting business impact to 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
- Generating AI-assisted reports for audits and compliance
Case Studies and Security Automation Patterns
- Real-world examples of AI implementation in security pipelines
- Selecting the right tools for your ecosystem
- Best practices for building and maintaining secure pipelines
Summary and Next Steps
Requirements
- A solid understanding of the DevOps lifecycle and CI/CD pipelines
- Fundamental knowledge of application security principles
- Familiarity with code repositories and infrastructure-as-code tools
Audience
- Security-focused DevOps teams
- DevSecOps engineers and cloud security specialists
- Compliance and risk management professionals
