EXA10
— Satisfy a regulator —
AI Training Data
Prove your AI model meets every regulatory test — without revealing the data or the weights.
The problem to solve
The EU AI Act, NIST AI RMF, and UK regulators all require proof that high-risk models were trained on lawful, representative, and bias-tested data. The natural answer — open the training set to inspectors — exposes the IP that the model rests on. The fallback — submit summary statistics — is too weak to satisfy a serious audit. Neither side gets what they need; deployments stall.
The solution it enables
Polymonial lets the AI lab seal the training set, validation results, and bias metrics. The regulator's compliance schema runs against the seals. Each test produces a verifiable answer: training data met the agreed sourcing rules, demographic representation meets thresholds, accuracy holds across subgroups. The lab's IP stays sealed. The regulator's confidence is grounded in proofs, not trust.
What it looks like
An AI company deploying a medical diagnostic model must satisfy the EU AI Act (August 2026). They seal their training dataset statistics, bias metrics, and validation results. A regulator generates a compliance schema. The AI company proves: training data met the agreed sourcing rules, demographic representation meets thresholds, accuracy exceeds minimum on all subgroups. The regulator verifies the proof. They never see the training data, the model weights, or the architecture.
“Evidence for Article 10 data-governance checks, without exposing the training corpus.”
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