Learn to validate, audit, and govern AI-generated code using GitHub Copilot. This course teaches you systematic techniques for catching security vulnerabilities, logical flaws, and hallucinated APIs in Copilot output — skills essential for any team adopting AI-assisted development.

GitHub: Governing AI-Generated Code

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What you'll learn
Apply techniques including static analysis and security scanning to audit AI-generated code for vulnerabilities, flaws, and hallucinations
Create custom Copilot configurations using instructions to enforce team coding standards and project-specific conventions across all generated code
Evaluate LLM capabilities, performance benchmarks, and cost-benefit trade-offs to select the right model for specific development tasks
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3 assignments
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There are 3 modules in this course
Covers AI code validation fundamentals, systematic verification techniques, security vulnerability identification, detecting logical flaws and hallucinations, and hands-on security auditing exercises.
What's included
6 videos3 readings1 assignment
Covers custom Copilot instructions via copilot-instructions.md, enforcing team coding standards, custom rule creation and testing, LLM selection criteria, model performance comparison, cost-benefit analysis, and continuous learning resources.
What's included
10 videos4 readings1 assignment
Apply validation, security auditing, custom configuration, and LLM selection techniques in an end-to-end governance scenario that synthesizes all course concepts.
What's included
1 reading1 assignment
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Frequently asked questions
No prior security experience is required. The course teaches security validation techniques from the ground up, starting with common vulnerability patterns in AI-generated code and building to structured audit workflows.
It is a configuration file that lives in your repository and tells GitHub Copilot about your project's coding standards. The course teaches you to create, test, and maintain these instructions so that every Copilot suggestion follows your team's conventions automatically.
Yes. The LLM selection module covers how to compare models from providers like OpenAI, Anthropic, and Google using performance benchmarks, cost-benefit analysis, and practical evaluation on real development tasks.
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Financial aid available,



