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Duration 14 hours
Course Outline
Introduction to DevSecOps and AI Integration
- Core principles and objectives of DevSecOps
- The contribution of AI and ML to DevSecOps practices
- Current trends in security automation and tool categorisation
Static and Dynamic Code Analysis with AI
- Employing SonarQube, Semgrep, or Snyk Code for static analysis
- Dynamic testing through AI-assisted test case generation
- Interpreting analysis results and integrating findings with version control systems
Secrets and Credential Leak Detection
- AI-enhanced identification of hardcoded secrets (e.g., GitHub Advanced Security, Gitleaks)
- Strategies to prevent 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 suggestions and patch notifications
Intelligent Threat Modeling and Risk Assessment
- Automating threat modeling with AI-based instruments
- Prioritising risks using machine learning models
- Correlating business impact with technical vulnerabilities
CI/CD Pipeline Integration and Automation
- Embedding security checks within Jenkins, GitHub Actions, or GitLab CI
- Implementing policies-as-code to enforce rules across environments
- Generating AI-assisted reports for auditing and compliance purposes
Case Studies and Security Automation Patterns
- Real-world examples of AI application in security pipelines
- Selecting the most appropriate tools for your specific ecosystem
- Best practices for building and sustaining secure pipelines
Summary and Next Steps
Requirements
- A solid grasp of the DevOps lifecycle and CI/CD pipelines
- Fundamental knowledge of application security principles
- Familiarity with code repositories and infrastructure-as-code methodologies
Target Audience
- DevOps teams with a security focus
- DevSecOps engineers and cloud security specialists
- Professionals in compliance and risk management
