Overview

Enterprise AI has a security problem, and it is not the one most programs are working on. The industry is shipping guardrail products, AI firewalls, model-risk frameworks, and responsible-AI posters. Meanwhile, a dealership chatbot sold a sixty-thousand-dollar vehicle for a dollar because a passerby typed an instruction into it. A finance worker in Hong Kong wired twenty-five million dollars to an attacker after a video call full of deepfaked executives. An AI coding agent deleted a production database during a change freeze and then tried to cover it up. A semiconductor company lost internal source code to a public model because three engineers pasted it into a chat window. A small-claims tribunal held an airline liable for a policy its chatbot invented. These are not lab findings. They are the public record. The gap between the industry's control narrative and the industry's incident record is the subject of this book. The gap exists because enterprise AI security, as it is being practised in early 2026, is still importing frameworks from adjacent disciplines that do not quite fit. Application security built its discipline around deterministic code, bounded input, and a handful of attack classes catalogued across two decades. Machine-learning security, the field that became prominent during the classification-model era, built its discipline around training-data attacks and model robustness for narrow models. Enterprise AI as it now exists is neither. It is probabilistic, it is language-native, it calls external tools with consequences, it is embedded in SaaS products the enterprise did not approve as AI, and it is used by employees through channels the enterprise cannot see. A control library imported from app-sec misses half of the threats. A control library imported from ML-sec misses a different half. Enterprises that recognise the mismatch buy more products. The gap does not close. This book argues that the right response is a threat-model-first one. Begin with what is actually happening to real organisations. Catalogue the failure modes that have produced material loss. Map each failure mode to the control that would have caught it. Prioritise the controls that change the shape of the risk rather than the ones that look complete on a slide. Treat the rest as theatre. Mehul Jain is an AI consultant and founder who helps enterprises move AI from strategy to production. His work spans the full arc covered by this series: scoping which tasks warrant AI, designing the architecture, operating it once deployed, and building the organisational program that sustains it at scale.

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