Your AI Ships Code Faster Than Anyone Can Review It

You shipped code this week that an AI mostly wrote. Did a human read every line before it merged? GitHub logged a billion commits in all of 2025; this year it is on pace for 14 billion, a 14x jump in a single year, almost all of it AI-driven. We now produce code faster than any person, or any scanner, can review it. The risks that matter most in this new world usually are not line-level bugs. They are architectural: an over-permissioned service, a coding agent that can be steered into misusing a tool, an MCP server with more reach than anyone mapped, a prompt-injection path that crosses a trust boundary. Traditional SAST reads files one at a time and cannot see these. A human reviewing one PR at a time cannot hold the whole system in their head. So threat modeling, the one review built to catch architectural risk, is exactly the review that stops happening, because it is slow, manual, and needs a senior security engineer you probably cannot spare. This talk is about closing that gap. I will show why AI coding velocity breaks traditional security review, the real difference between a code-level bug and an architecture-level threat, and what continuous, automated threat modeling looks like when it runs inside the developer workflow instead of in a quarterly audit. Then I will show the loop Oplane runs: it finds the real architectural risks, drives the fix in the pull request and the AI coding loop, and proves the fix actually closed the gap. Find, fix, prove, at the speed you already ship. If you build with AI and want security to keep up without slowing you down, this one is for you.