Reviewing the code that exists is not the same as finding the controls that don’t.
Clover’s AI agents are good at finding what could go wrong with the code in front of them. Frontier models have made that work fast and cheap. ThreatModeler is the platform underneath the AI: a decade of curated threat intelligence (thousands of components, hundreds of protocols, attack patterns, countermeasures, and compliance mappings), a system of record for secure-by-design decisions, and the ability to find what AI agents structurally can’t: the 30% of risk that lives in what’s missing.
Charles Schwab runs this pattern across infrastructure handling six million trades a day, with 10x more threat models at 50% less effort per model, built on threat content no model can shortcut.
AI is 99% accurate at finding what’s in your code. About 70% at finding what isn’t.
The 30% is where breaches live.
Sources: AbsenceBench (U. Chicago & Stanford, 2025) and GrAlgoBench (HKUST, ICLR 2026). Reasoning models improve this by less than 8% at three times the cost.
Three things every enterprise threat modeling program needs underneath the AI.
Find what’s missing.
The most dangerous risk isn’t a bug in the code. It’s a control that was never built. AI agents bounded by what they can read can’t find it. ThreatModeler can, because it reasons from architecture, not artifacts.
Hold the answer.
Six months from now, someone will ask why a risk was accepted. ThreatModeler is the durable record of what was decided, by whom, against which controls, and why. It holds across team turnover, audits, and architecture change.
Reason against expertise.
ThreatModeler’s AI runs on a decade of curated threat intelligence: 2,900+ components, 100+ protocols, the VAST framework. Not a general model’s guess about your stack.
What it looks like when the platform underneath was built for the work.
More threat models created across the enterprise.
Less effort per model.
Trades per day on the infrastructure ThreatModeler secures.
Schwab didn’t reach these numbers by reviewing more changes. They reached them by treating threat modeling as a continuous, architecture-aware, governed practice, with a system of record underneath it and a curated library inside it.
See the difference in 30 minutes.
We’ll walk through a working threat model of a stack like yours, show you what reviewing what isn’t there actually looks like, and answer how ThreatModeler fits alongside the AI security tools you already have.
See it in 30 minutesSee it in 30 minutes.
Tell us where to reach you and the team will walk through a threat model of a stack like yours.