This ebook is written for development leaders trying to capture AI’s productivity gains without letting security become a release blocker. It introduces Secure at Inception as the answer to AI-era development risk: rather than checking code only after it is written, security should shape code from the first prompt onward. The guide is organized as a four-step blueprint. Step 1 shifts security from gatekeeping to enablement. Step 2 integrates protection across IDEs, repos, and CI/CD. Step 3 emphasizes automation so secure defaults scale without slowing teams. Step 4 expands the discussion to AI-native systems, calling for platform-level visibility, AI-BOMs, posture management, threat modeling, and long-term governance. Its broader message is that speed and safety reinforce each other when s

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