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.

You will start by building a validation workflow that combines static analysis, manual review, and security scanning to audit AI-generated code against OWASP patterns. Hands-on challenges walk you through identifying injection vulnerabilities, detecting hallucinated function calls, and documenting remediation steps. The course then covers custom Copilot configurations using copilot-instructions.md, where you define project-specific coding standards that Copilot follows automatically. You will create, test, and iterate on custom rules that enforce team conventions across all generated code. Finally, you will evaluate Large Language Models for development tasks — comparing capabilities across providers like OpenAI, Anthropic, and Google — using performance benchmarks and cost-benefit analysis to select the right model for each coding requirement. By the end of this course, you will have a governance framework for integrating AI code generation into production workflows with confidence.