One kernel, many risk problems.

An AI agent fails overnight, for example an onboarding agent passing hundreds of synthetic identities, and nobody has rehearsed who decides what when the regulator and the press call.

You get a facilitated exercise for your executive and risk team on public and synthetic data, a scored after-action report and a written record of decision rights.

Nobody can say, in money terms, how much damage a production AI agent could do if it were fully hijacked.

You get a signed, independent statement of the worst-case loss, built on deterministic checks of the agent's tool scopes, limits and approval steps, with the assumptions that bound it written out. The fix is not sold in the same engagement, so the number stays independent.

Your bank's model-risk team must validate an LLM before go-live, and the public benchmarks it was scored on may already sit in its training data.

You get held-out test packs (fresh cases the model has never seen) generated from templates, with the right answer computed by code wherever it can be calculated, plus trick cases that look for blind spots and exploitable edges, a written test method signed by its author, and, for lenders whose models serve several partner institutions, a per-partner evidence pack.

You want AI agents to work fraud and AML alerts, but you have no case file where the right answer is known in advance.

You get a synthetic back office (customers, transactions, alerts, KYC files, disputes) with planted fraud rings and mule networks and a hidden answer key, and a scored run of your investigator agent against it.

Your scam warning screens exist, but you do not know whether they stop a customer who is mid-scam.

You get a lab where a scripted scammer plays against a copy of your real warning screens, a report on which warnings hold and which fail, and an optional pilot of a short explainer in the message you already send to suspected money mules.

Deepfake celebrity ads for fake supplements and crypto get paid through merchant accounts that looked clean at onboarding.

You get a one-off sweep that traces live scam ads to the merchants processing them, with the statement descriptor, checkout domain, acquirer where visible, ad IDs, a funnel recording and the receipt for each finding.

Platforms that pay out to thousands of payees lose money to fake and duplicate payee accounts, takeovers that change where payouts go, and mule payees who cash out fast.

You get a review of how a fraudster could open, take over or rent payee accounts on your platform and move money out through your payout rails, with the weak points ranked by exposure and plain fixes for each.

Free-credit farming, multi-accounting, key resale and metering gaps quietly drain usage-priced AI and API platforms.

You get a fixed-scope forensic audit of your signup, payment and usage logs that sizes the loss and hands over blocking rules.

Your transaction monitoring and card fraud rules raise alerts every day, but nobody can say how many launderers, mules and fraudsters they let through.

You get a synthetic set of customers and transactions with planted laundering, mule and card-fraud cases and a hidden answer key, run through your own rules and thresholds, and a scored report of what they caught, what they missed and how many false alerts they raised.

Your fraud defense depends on one outside AI vendor, and you have never tested what happens if it goes away.

You get an exit-readiness scan, then, if you choose, a backup fraud model built inside your own cloud and tested on held-out data, with an exit-test report signed by Amir Shachar.

A bank or insurer reviewing your AI feature wants proof it resists prompt injection and data leakage, and written answers stall the deal.

You get a dated attack test of your live AI feature, run with written permission, covering prompt injection, data leakage and retention, and an answer pack that cites only what was observed.