Provenance can show the trail. It cannot grant permission.
A traceable history is essential evidence, but an authorized use still requires a separate governance decision.
Dr. Benjamin Harvey
Watch the 45-second visual essay
Two questions that should never be collapsed
Where did this output come from? May we use it here?
Those questions are related, but they are not interchangeable. Provenance describes the history of an asset: its origin, ingredients, edits, tools and transformations. Governance resolves a different question: whether a proposed use is authorized for this recipient, purpose and moment.
When teams collapse the two, a clean lineage record can be mistaken for permission. Or a policy approval can be mistaken for proof of contribution. A dependable AI workflow needs both records, linked without asking either one to carry claims it cannot support.
What current provenance standards can establish
C2PA's current 2.4 specifications describe Content Credentials as a standardized, cryptographically bound way to record facts about a digital asset's history. The record can include origin, modifications, ingredients and whether AI was used. Validation can show that the credential is well formed, associated with the asset and free from tampering.
That is valuable infrastructure. It is also deliberately bounded. C2PA's explainer says Content Credentials do not make value judgments about whether provenance is good or bad, and provenance alone cannot establish whether content is true. The core specification also does not directly identify individual creators or organizations.
The design lesson is not that provenance is weak. It is that provenance is evidence—not the entire decision.
Add an authority record beside the lineage record
Imagine an AI system assembling a product brief from an internal memo, licensed research, public data and a generated visualization. The lineage record can preserve which versions entered the work and how they changed. The authority record must still answer: who issued the rule, what use is permitted, which recipient is allowed, what exception applies, when the decision expires and what consequence follows.
A useful release object links the exact asset version to the exact decision. It preserves uncertainty, human edits and expert review. It also records the proposed action, the control decision and the observed effect as separate events.
That separation makes correction possible. If rights, scope or evidence change, the decision can be revisited without rewriting the history.
Test whether a reviewer can reconstruct both
The practical evaluation is a reconstruction test. Give an authorized reviewer a completed workflow and ask whether they can identify the source assets, material transformations, unresolved gaps, governing rule, decision owner, permitted recipient and observed release.
Then measure the tradeoffs: missing evidence, incorrect approvals, unnecessary holds, review time, correction acceptance and legitimate task completion. A perfect trail that blocks all useful work is not enough. Neither is a fast approval whose evidence disappears after the release.
NIST's AI Risk Management Framework remains a useful risk-management foundation, but NIST notes that AI RMF 1.0 is now being revised. Teams should date the framework version they use rather than treating a living standard as timeless.
A research and building principle
My current AI governance and AI IP attribution programs treat this as an active research and product-design problem. The public record includes working papers, technical proposals and proposed controlled studies—not a claim of completed experimental performance or a legal determination.
The principle is simple: provenance should make contribution history inspectable; governance should make authorized use explicit. Keep the records distinct. Link them at the action boundary. Preserve what remains unknown.
Before an AI-assisted asset is released, ask two questions: can we reconstruct how it came to be, and can we show why this use was permitted?