What does the EU AI Act require for AI provenance?

Articles 13 and 15 of the EU AI Act require transparency and record-keeping for high-risk AI: deployers must be able to show how the system reached an output and keep records that make that traceable. In practice that means documenting which sources informed an output, when they were verified, and that the record hasn't been altered.

What is an audit-grade AI evidence pack?

An audit-grade evidence pack is a portable, hash-signed record for a model output showing which sources were used, when each was last verified, whether any contradicted each other, and pre-mapped citations to the regulatory clauses it satisfies — exportable as CSV, JSON, or PDF for SOC 2, ISO 27001, and regulator submissions.

Which regulations care about AI source documentation?

The EU AI Act (Articles 13/15), India's DPDP Act, FINRA Rule 3110, and HIPAA §164.312 all expect a regulated organization to show where an AI-influenced decision's information came from and that it was handled and logged appropriately.

Audit-Grade Provenance: What Regulators Actually Want to See (AI Act, DPDP, FINRA, HIPAA)

Four regulators, one question: where did your AI get this, and can you prove it hasn’t changed since? The EU AI Act, India’s DPDP Act, FINRA, and HIPAA arrive at it from different directions, but every one of them now expects a regulated organization to account for the information behind an AI-influenced decision — not just the decision.

Most AI deployments cannot answer that question without a fire drill. Here’s what each regime actually asks for, and what evidence satisfies it.

EU AI Act — Articles 13 and 15

For high-risk systems, Article 13 requires transparency: deployers must be able to interpret an output and understand how the system reached it. Article 15 requires accuracy, robustness, and record-keeping that make the system’s behaviour traceable. Translated to operations: you need a durable record of which sources informed an output, the version read, and when each was last verified — not a verbal assurance that “the model was trained well.”

This is a different obligation from testing the system for safety. Pre-deployment security and conformity testing is its own discipline — see the security-side view of EU AI Act testing. Provenance is the runtime half: proving, output by output, what the system relied on.

India DPDP — fairness and records for automated decisions

The DPDP Act’s fairness and record-keeping expectations mean that when automated processing touches personal data, you should be able to reconstruct the basis of a decision. A provenance trail that shows which sources a model used — and that those sources were current and handled within your perimeter — is the practical artifact behind that obligation.

FINRA Rule 3110 — supervisory documentation

For AI-assisted advice or communications, FINRA Rule 3110 expects supervisory systems and documentation: a firm must be able to show it reviewed and can account for what its systems produced. When an AI drafts or informs client-facing output, “the model said so” is not supervision. A time-stamped record of the sources behind that output is.

HIPAA §164.312 — access and audit logs

When AI touches protected health information, HIPAA §164.312 technical safeguards expect access controls and audit logging — a record of what was accessed and when. AI that retrieves from PHI-bearing sources needs the same logging discipline applied to what the model read, not just who logged in.

What “audit-grade” actually means

Across all four, the same evidence satisfies the requirement. An audit-grade evidence pack for a model output contains:

Hash-signed records. Every record is cryptographically signed with a KMS-backed key, so an auditor can verify integrity offline. The evidence carries the weight of a signed financial statement — not “trust us, the AI saw the right document.”
Pre-mapped regulatory citations. Each pack carries citations to the clauses it satisfies — AI Act Article 13/15, DPDP fairness/records, FINRA 3110, HIPAA §164.312 — so reviewers aren’t reverse-engineering the mapping.
A time-stamped trail. Every source ingest, drift detection, contradiction flag, and human override, in order.
Exportable formats. CSV, JSON, or PDF, ready for SOC 2, ISO 27001, internal audit, and regulator submissions.

The operational payoff is the part compliance teams feel: reviews that used to consume a quarter ship in days, because the evidence is already assembled and verifiable instead of reconstructed under deadline.

Where to start

Regulated deployments are where provenance stops being nice-to-have. Talk to a compliance partner about audit-grade evidence packs — the Regulated tier includes hash-signed packs, AI Act / DPDP / FINRA / HIPAA mapping, and a dedicated partner for your reviews. If you’re earlier in the journey, start with the basics of AI provenance.

ProvenanceOps is part of the AI Vyuh AI-operations stack.

This article is general information about regulatory expectations, not legal advice. Confirm specific obligations with your compliance counsel.