AI Governance Evidence

The cryptographic evidence layer that turns AI governance policy into enforceable reality.

AI governance committees write policies. AI ethics boards approve risk classifications. AI compliance teams document procedures. Enterprises produce governance frameworks, AI ethics charters, and risk registers. All of this is necessary. None of it is enforcement. Enforcement requires evidence — verifiable, tamper-evident, third-party-checkable evidence that the policies were applied to the actual decisions the AI made. H33 produces that evidence.

The gap between governance policy and governance reality

A typical enterprise AI governance program produces a stack of artifacts: AI ethics charter, risk classification matrix, model approval workflows, model inventory, monitoring dashboards, quarterly governance reviews, AI risk register. These answer "what should our AI governance be?" They do not answer "what did our AI actually do?" The weakness becomes visible when a regulator examines an AI use case and asks for evidence the documented policy was applied to a specific decision; when an external auditor reviews the AI program and asks for cryptographic confirmation that the inventory matches reality; when a class-action complaint alleges discriminatory AI behavior and requires decision-level evidence; when an M&A diligence team evaluates the target's AI governance and asks for verifiable artifacts; when a board-level review asks "how do we know our governance actually constrained the AI?"

What governance evidence must demonstrate

Governance evidence at the decision level must establish, for each decision: Policy was in force — the policy that should have governed the decision was actually bound to the model. Authority was correct — the principal who triggered the decision was authorized. Model was approved — the model was the model approved by governance for this use case. Data was authorized — the data corpus consulted was the corpus the policy authorized. Confidence was assessed — the model's confidence and abstention behavior was captured. Citations were grounded — the output's citations to evidence were cryptographically bound. Each is captured in an H33 evidence bundle's eight EC objects.

How H33 turns governance into evidence

H33 evidence bundles are designed for governance enforcement. Each EC object maps to a governance concern. PolicyBind is the governance committee's policy made cryptographic. ModelFingerprint is the model inventory made cryptographic. AuthorityBind is the access control matrix made cryptographic. CalibratedAbstention is the confidence policy made cryptographic. PipelineDag is the architecture diagram made cryptographic. CorpusBind is the data classification matrix made cryptographic. EvidenceAttestation is the audit log of grounding evidence made cryptographic. ResultCitationBind is the citation discipline made cryptographic. Governance defined what should happen. Evidence demonstrates that it happened.

The board's view

For the board's risk and audit committees, AI governance evidence answers the question "how do we know?" A typical board oversight pattern relies on management's representations. With H33 evidence, the board can request sample verification: "Show us H33 bundles for ten high-risk AI decisions from the last quarter, and we'll have our independent auditor verify them." The verification is cryptographic and reproducible. The board's oversight becomes verifiable rather than purely representational.

Use cases

A bank's AI risk register. The bank's AI risk register lists every AI use case with risk classification and applicable controls. H33 bundles produced by each AI system map to the risk register entry via PolicyBind digest. A regulator can verify that the controls in the register were applied to specific decisions, not just listed in the register. An enterprise AI ethics committee's quarterly review. The committee receives a verifier-generated summary of all bundle verifications from the prior quarter. A federal agency's AI inventory. Under EO 14110 and OMB guidance, federal agencies maintain AI inventories. H33 bundles reference the inventory entry via PolicyBind and ModelFingerprint digests. A healthcare system's clinical AI governance. The medical executive committee approves clinical AI use cases. The committee's approval is digested and referenced in the AI's PolicyBind. A financial services firm's MRM program. Model Risk Management requires validation and governance for production models. H33 bundles provide cryptographic evidence.

Common questions

Does this replace our AI governance committee?
No. The committee defines policy. H33 provides evidence the policy was applied.

Will our existing governance platform integrate with H33?
Yes. Most governance platforms expose policy registries and model inventories via API.

Can governance failures be detected automatically?
Yes. Bundle verification produces deterministic verdicts. Aggregated verifier runs surface use cases with high fail rates.

What about ISO 42001?
H33 bundles produce the operational evidence (Annex A controls) that an ISO 42001 AI Management System requires.

Can the board's audit committee use this without specialized tooling?
The verifier is open source and runs as a single-command CLI tool.

Get Started

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