FOR FINANCIAL SERVICES
EVERY WRONG ACTION HAS A DOLLAR SIGN ON IT.
Fraud review, reconciliation, customer service, trading support. AI now works inside the flows where a single wrong action costs real money. Mountain Theory checks every action against your policy before it executes: the payment that matches goes out, the one that does not is stopped before it is sent. Every decision lands in an append-only record your examiners can read.
The question your examiner is about to ask
Model risk management covers the model: its design, its validation, its drift. It does not cover what an agent built on that model does at 2am with a payment rail and valid credentials. A fraud-review agent decides a payment is fine and releases it. A customer-service agent changes a beneficiary because a caller asked nicely. A reconciliation agent deletes a trade record to make the books balance.
None of those is a breach. Every one runs on valid credentials, inside the permissions you granted. Your regulators ask how you control the model. This is the layer that controls what it does.
Running document or IT agents under GLBA? The DMV packet scenario
What we do
We control what autonomous AI does. The model still decides. The action does not run until it has been checked.
- Stops tool chaining: blocked once, blocked again on the workaround
- Catches agent drift: the goal it was given, not the one it wandered to
- Zero-day agent behavior stopped the day it appears
- No new rule, no signature, no patch to do it
Personal data is checked in and out: Social Security numbers, account and payment data, customer records, trade and position data. Stopped before it leaves your environment, whether it was typed, pasted, or pulled from a system the agent was allowed to read. Every action gets one of three outcomes, ALLOW, HOLD, or BLOCK, and every decision is logged with the rule that matched it.
How personal data is stopped at the action
The scenario
An agent with valid credentials is told to clean up old evidence after a review, and the deletion is stopped before it executes. It goes looking for another way to reach the same result, chaining tools together, and every attempt is stopped too. Ungoverned, in the same run, the deletion completed.
That is the same move a reconciliation agent makes when it deletes a record to make the books balance, and the reason the control has to sit where the action fires rather than where the request is written.
The Optimo AI run, with the misses
One rule, as your risk owner would write it
Policy
Changing a beneficiary, deleting a trade record, or sending account data outside the firm is prohibited for every agent. Releasing a payment above the limit goes to HOLD for a named approver. Log every attempt.
Rules sit in one place and apply everywhere the AI runs, whichever model or platform it is built on. Change a rule once and the change is live. Model risk, compliance and audit can each read the same rule without a translator.
What you hand the examiner
An append-only record of every decision: the action proposed, the rule that matched, the outcome, the time, and who wrote the rule. It exists because enforcement produced it, not because someone assembled a report after the loss. That is the record an examiner asks for under SR 11-7, SEC, FINRA, SOX, GLBA and PCI DSS when an AI system is in scope, and it is what your own audit committee asks for first.
What the record has to contain, and why the usual logs do not count
The ask
A 30-day paid proof of concept on your agents and your action set, in your environment, not ours. Then one flat annual price for the institution, with no per-agent count. Tests published, misses included, before anyone signs.
Book 30 minutes and bring the workflow with a dollar sign on it
Proof