Proof Infrastructure for AI Governance
Give AI governance programs verifiable proof of what models actually did — turning policy and oversight claims into independently checkable evidence.
The problem
AI governance teams are responsible for ensuring models operate within policy, with appropriate human oversight, across a growing portfolio of automated decisions. Governance platforms provide monitoring and dashboards, but those live inside systems that must be trusted.
When a regulator or affected individual asks an organization to demonstrate what a model did and that oversight was in place, dashboards and reports are not independently verifiable.
The trust gap
Observability is not proof. Monitoring shows what a model appears to be doing on internal dashboards, but it cannot prove, to an outside party, that a specific decision occurred under a specific policy with the required oversight. Governance claims remain assertions rather than evidence.
The Proof Infrastructure approach
- Emit a proof artifact at each significant AI decision, committing to model, policy, and oversight.
- Attribute actions to specific model versions and authorized systems.
- Give regulators verifiable evidence that governance policies were applied.
- Support accountability at the level of individual AI decisions, not just program reports.
A proof artifact in this context
Sensitive details are committed to via a hash — the proof carries no private data.
Example verification flow
- 1A regulator asks an organization to demonstrate oversight of a high-risk AI decision.
- 2The organization provides the decision proof artifact.
- 3The regulator validates the model identity, version, and signature.
- 4They confirm the required human oversight was recorded before the action.
- 5Governance is proven for that decision — not merely asserted in a report.
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