Verifiable AI for compliance: proving your model decisions to auditors
As AI moves into regulated decisions — credit, hiring, insurance, healthcare, content moderation — supervisors increasingly demand that operators can show what a model decided and prove the record is genuine. From the EU AI Act’s logging requirements to financial and sectoral supervision, "trust our internal logs" is no longer sufficient. Verifiable AI turns decision records into evidence.
What auditors actually want
An auditor investigating a disputed AI decision needs to know exactly what the model received, what it output, which model version ran, and when — and to be confident that record was not edited after the fact. An operator-editable log cannot provide that assurance, because the operator could have changed it. The requirement is non-repudiation: records the operator provably could not have altered after they were created.
How verifiable records satisfy it
Sign each decision record (input hash, output, model identifier, timestamp) and chain entries to the hash of the previous one. Any edit or deletion breaks the chain and fails verification, so tampering is detectable. When an auditor asks, you hand over the signed history and they check it independently — the trust rests on cryptography, not on your word. This directly addresses traceability and record-integrity requirements across regimes.
Post-quantum and honest scope
Compliance records may need to hold up for years, so sign with post-quantum ML-DSA (FIPS 204); a classical signature becomes forgeable once quantum computers arrive. Honest scope: this proves the authenticity, ordering and integrity of what was logged — it does not prove the model’s decision was correct or fair, which is a separate governance question. The cryptography is resistant to known classical and quantum attacks per NIST, not unbreakable.
Try it yourself — live, free, verifiable in 30 seconds:
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