Secure Vibe Coding Making Generated Code Enterprise Ready

AI coding tools doubled the number of changes reaching review and made each one 154 percent larger. Reviewer headcount did not move. The result is not that models write insecure code, though they do. It is that your verification layer was sized for a rate of arrival that no longer exists, and your controls were built around a human actor who works at human speed and can be asked why.
Coding agents violate that assumption. They do not defeat branch protection, peer review, or change advisory. They take a path those controls do not sit on.
Secure Vibe Coding is a control model for the people accountable for code no human wrote. It argues that enterprise readiness is a property of the record rather than of the code, because six of the seven ways generated code fails an enterprise gate are absences, a check that was never written, and absence is not something reading finds.
The book is organized around four control domains: guardrails on generation, restriction of runtime, inspection that scales, and provenance and proof. It gives you forty-five controls with their tier, enforcement point, and cost behavior, and a ninety-day sequence that starts with the two whose absence cannot be repaired later.
Inside:
Why authorization never improves while injection does, and the test that predicts which defenses get better with the next model release.
The single line of configuration that defeats exfiltration, hallucinated dependencies, unauthorized model endpoints, and credential theft at once, without anyone having to anticipate any of them.
Why containment costs less than inspection, and why the gap widens every quarter that generation volume rises.
How package hallucination became an attack whose target list the models publish themselves, and the six-month age floor that defeats the entire class.
Escape rate, detection lag, and the tripwire that tells you whether you built the control or only the record.
Twenty questions for a board or audit committee, each with the answer that indicates a working program and the answer that only sounds reassuring.
Paired code listings throughout, in Python, Java, TypeScript, Terraform, and policy. Each pair shows what a model produces when asked for the feature in ordinary language, and what survives an enterprise gate. The insecure version is never written as a blunder, because a control that ignores why the insecure pattern is attractive will be routed around by the next engineer who wants what made it attractive.
For CTOs, heads of platform, application security leaders, and architects carrying accountability for AI-assisted delivery. Every chapter states the conditions under which its own recommendation should be abandoned, so you can hold the argument to its own standard rather than taking it on faith.

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