AI has opened two simultaneous security fronts. Frontier models now generate working CVE exploits in under fifteen minutes at approximately one dollar per finding — meaning every vulnerability in your existing backlog, regardless of severity, is a live target. At the same time, AI coding tools are introducing 1.7 times more defects per unit of code than human-authored equivalents, flooding the pipeline with new risk faster than traditional security can absorb it. The instinct is to solve an AI problem with more AI. It is the wrong instinct. LLMs cannot govern their own security, not because they are insufficiently capable today, but because the architecture makes it structurally impossible. A model that shares the same computational boundary as the code it produces cannot serve as a trustworthy instrument of its own security assessment. Asking an LLM to certify the safety of its output is, in the most literal sense, asking the student to grade their own exam. This session maps the response across both fronts: a remediation-led push to close the existing backlog before adversaries exploit it, and prevention embedded at the moment of code creation, at the prompt, in the IDE, across AI pipelines. Attendees will leave with a concrete hybrid architecture, deterministic ground truth combined with AI-augmented reasoning, operating outside the trust boundary of the systems it governs, and a governance framework built for the velocity the current threat landscape demands.