This ebook provides a practical framework for safely adopting AI coding tools like GitHub Copilot and Gemini Code Assist. It explains that while AI accelerates development, it also introduces security risks, with a significant portion of AI-generated code containing vulnerabilities. The guide recommends implementing guardrails such as requiring proof of local security testing before granting access, using IDE-based scanning, and enforcing pull request checks. It also highlights the importance of conditional access policies, automated remediation, and continuous monitoring to prevent insecure code from reaching production. The overall message is that organizations must embed security controls directly into AI workflows to maintain both speed and safety.

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