Course Code: devsecopsai
Duration: 14 hours
Prerequisites:

  • An understanding of the DevOps lifecycle and CI/CD pipelines
  • Basic 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

Overview:

DevSecOps with AI is the practice of integrating artificial intelligence into DevOps pipelines to proactively detect vulnerabilities, enforce security policies, and automate response actions throughout the software delivery lifecycle.

This instructor-led, live training (online or onsite) is aimed at intermediate-level DevOps and security professionals who wish to apply AI-based tools and practices to enhance security automation across development and deployment pipelines.

By the end of this training, participants will be able to:

  • Embed AI-driven security tools into CI/CD pipelines.
  • Use static and dynamic analysis powered by AI to detect issues earlier.
  • Automate secrets detection, code vulnerability scanning, and dependency risk analysis.
  • Enable proactive threat modeling and policy enforcement using intelligent techniques.

Format of the Course

  • Interactive lecture and discussion.
  • Lots of exercises and practice.
  • Hands-on implementation in a live-lab environment.

Course Customization Options

  • To request a customized training for this course, please contact us to arrange.

Course Outline:

Introduction to DevSecOps and AI Integration

  • DevSecOps principles and goals
  • The role of AI and ML in DevSecOps
  • Security automation trends and tool categories

Static and Dynamic Code Analysis with AI

  • Using SonarQube, Semgrep, or Snyk Code for static analysis
  • Dynamic testing with 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
  • Creating automatic blocking and alerting rules

AI-Powered Dependency and Container Scanning

  • Scanning containers with 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 with AI-based tools
  • Risk prioritization using machine learning models
  • Linking business impact to technical vulnerabilities

CI/CD Pipeline Integration and Automation

  • Embedding security checks in Jenkins, GitHub Actions, or GitLab CI
  • Creating 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 in security pipelines
  • Choosing the right tools for your ecosystem
  • Best practices for building and maintaining secure pipelines

Summary and Next Steps