This guide outlines six best practices for building secure AI-driven development workflows without slowing innovation. It emphasizes establishing governance early, securing AI-generated code at the source, monitoring the AI supply chain, testing runtime risks, prioritizing issues with context, and continuously measuring progress. The visual panels across pages 1–3 show how each practice maps to real actions like IDE scanning, SBOM tracking, dynamic testing, and analytics dashboards. It highlights that up to 48% of AI-generated code contains vulnerabilities, making early detection critical. The key takeaway is that secure AI adoption requires embedding controls across the entire lifecycle—from code generation to runtime monitoring—while maintaining developer velocity.

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