For Financial Services
You've built the governance. The validation frameworks. The monitoring. But when examiners ask for evidence that your AI controls actually executed, you're showing them documentation of what should happen — not proof of what did.
GLACIS gives you cryptographic evidence that your AI controls work — verifiable by regulators, without exposing model inputs or customer data.
SR 11-7 requires effective challenge. Independent validation. Ongoing monitoring. Your model risk management program checks every box.
But the guidance was written before generative AI. Before models that produce different outputs every time. Before systems where "validation" means something fundamentally different.
Examiners are asking questions your current evidence can't answer. Not because your controls aren't working — because you can't prove they are.
GLACIS creates a verifiable record every time your AI controls execute — without exposing proprietary models or customer data.
Content filtering, bias checks, human review, output validation — whatever you've built. GLACIS observes without modifying.
Model inputs and outputs are hashed locally. Only cryptographic commitments leave your environment. Your IP stays protected.
Timestamped, third-party witnessed, cryptographically signed. Evidence that proves controls ran — not just that they exist.
Prove your validation tests actually ran against production models. Not recreated for audit, not simulated — the real thing, timestamped and witnessed.
Evidence that your challenge function is operational.
Every control check, every threshold evaluation, every human review decision — captured as verifiable evidence. Continuous, not periodic.
Monitoring you can demonstrate, not just describe.
Prove your bias controls executed on every decision. Cryptographic evidence that fairness checks ran — without exposing individual applications.
Verifiable fair lending, not just attestation.
When you use third-party AI, prove your oversight controls executed. Evidence that you validated vendor outputs, not just that you have a policy to.
Third-party risk management with teeth.
Model inputs, customer data, proprietary algorithms — none of it leaves your environment. GLACIS proves controls ran without seeing what they ran on.
Architecture-level data protection. Not policy — mathematics.
The OCC, Fed, and FDIC are paying attention. The EU AI Act treats credit scoring as high-risk. State regulators are adding AI-specific requirements to existing frameworks.
The pattern is consistent: regulators want evidence that AI governance is operational, not just documented. They want to see that controls executed, not just that they were planned.
Institutions that can demonstrate continuous, verifiable AI governance will face less friction. Those that can't will face more scrutiny, more MRAs, and more constraints on AI adoption.
We work with financial institutions to implement evidence infrastructure that fits your existing model risk management program. No rip-and-replace. No new frameworks to adopt.