AI Security Review
Drop your system description in. Four rival AIs think like attackers, map the threats that actually apply, and hand back the mitigations in priority order.
A threat model with ranked attack paths and the mitigations that matter.
Signed off by an independent Final QA audit — with a “verify before you rely on this” list, never a black box.
Why this team
Threat models drift into checkbox theatre without an adversarial mind in the room, so the Security Engineer walks the attack paths while the Devil’s Advocate plays the motivated attacker who does not care about your compliance certificate. The Software Architect maps where the trust boundaries actually sit versus where the diagram claims, the Privacy Counsel prices data exposure in regulatory terms, and the Risk Officer forces the ranking — because a model that flags everything protects nothing.
Each seat runs on a different frontier model — GPT, Claude, Gemini, Grok — so it’s a genuine cross-model review, not one AI in costumes. How a run works →
What the debate sounds like
An illustrative excerpt — A telehealth startup stores consultation notes and prescriptions, authenticates with magic links, and has just added a patient-facing API for a partner’s mobile app.
The new partner API moves the perimeter and nobody redrew it: it authenticates the partner app, not the patient behind it. If the partner’s mobile client is compromised or its token leaks, every record it can query is exposed. Per-patient scoping on API tokens is the finding to fix first — the crown jewels are consultation notes, and this is the shortest path to them.
As the attacker, I skip your API entirely: magic links mean patient security equals email security, and patients reuse webmail passwords. Credential-stuff a thousand email accounts, request magic links, harvest sessions. No exotic exploit, no CVE — just your own login flow used as designed. What in this architecture notices that happening?
The severity maths is different for this data class: prescriptions and consultation notes are special-category data, so a breach is not just an engineering incident but a notifiable regulatory event with per-record consequences. That multiplies the impact score of both paths just described, and it means retention is a control too — data you deleted on schedule cannot leak.
Questions
Is this a replacement for a professional penetration test?
No, and the output says so explicitly where it matters — the deliverable flags which findings genuinely need a professional review or pen test. What it replaces is the blank page: you arrive at the security review knowing your ranked attack paths, trust boundaries and priorities instead of paying a consultancy to discover them from scratch.
What do I describe — I don’t want to paste credentials or code?
Architecture, not secrets: what data you hold, who uses the system, how authentication works, what third parties connect, and where the boundaries sit. The threat model reasons about structure. Never paste live credentials — structure is what the attack paths are made of.
How does the council decide what is worth fixing versus security theatre?
Every mitigation is scored as risk-reduced versus friction-added, and the model explicitly names what you are over-protecting. In most runs something on the existing to-do list turns out to be theatre while an unglamorous control — token scoping, rate limits, retention — tops the list.
Your material is used only to run your review — never to train public models. Encrypted in transit and at rest. Security & privacy →
Want full control — pick your own minds, set the depth? Open the full council →