DevSecOps with AI: Automating Security in the Pipeline Training Course
DevSecOps with AI involves integrating artificial intelligence into DevOps workflows to proactively identify vulnerabilities, enforce security policies, and automate response mechanisms throughout the software development lifecycle.
This instructor-led training (available online or onsite) is designed for intermediate-level DevOps and security professionals looking to leverage AI-driven tools and methodologies to strengthen security automation within their development and deployment pipelines.
Upon completion of this course, participants will be able to:
- Integrate AI-powered security tools into CI/CD pipelines.
- Utilize AI-enhanced static and dynamic analysis to identify issues at an earlier stage.
- Automate the detection of secrets, scanning for code vulnerabilities, and analyzing dependency risks.
- Implement proactive threat modeling and policy enforcement through intelligent techniques.
Course Format
- Interactive lectures and discussions.
- Extensive exercises and practical applications.
- Hands-on implementation within a live-lab environment.
Customization Options
- For tailored training on this subject, please contact us to make arrangements.
Course Outline
Introduction to DevSecOps and AI Integration
- Core principles and objectives of DevSecOps
- The role of AI and machine learning in DevSecOps
- Current trends and categories of security automation tools
Static and Dynamic Code Analysis with AI
- Leveraging tools like SonarQube, Semgrep, or Snyk Code for static analysis
- Dynamic testing supported by AI-driven test case generation
- Interpreting analysis results and integrating them with version control systems
Secrets and Credential Leak Detection
- AI-enhanced identification of hardcoded secrets (e.g., GitHub Advanced Security, Gitleaks)
- Preventing sensitive data from entering source control
- Establishing automated blocking mechanisms and alerting rules
AI-Powered Dependency and Container Scanning
- Scanning containers using Trivy and AI-enabled plugins
- Monitoring third-party libraries and Software Bill of Materials (SBOMs)
- Receiving automated remediation recommendations and patch alerts
Intelligent Threat Modeling and Risk Assessment
- Automated threat modeling using AI-based tools
- Risk prioritization driven by machine learning models
- Correlating business impact with technical vulnerabilities
CI/CD Pipeline Integration and Automation
- Integrating security checks into Jenkins, GitHub Actions, or GitLab CI
- Developing policies-as-code to enforce rules across various environments
- Generating AI-assisted reports for audits and compliance purposes
Case Studies and Security Automation Patterns
- Real-world examples of AI implementation in security pipelines
- Selecting appropriate tools for your specific ecosystem
- Best practices for constructing and maintaining secure pipelines
Summary and Next Steps
Requirements
- A solid understanding of the DevOps lifecycle and CI/CD pipelines
- Foundational knowledge of application security principles
- Familiarity with code repositories and infrastructure-as-code tools
Target Audience
- Security-focused DevOps teams
- DevSecOps engineers and cloud security specialists
- Compliance and risk management professionals
Open Training Courses require 5+ participants.
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