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June 2026

Everyone Is Auditing the Workflow. Nobody Is Fixing the Knowledge.

Overview: Beyond the Audit Trail — Solving the AI Provenance Problem. The three problem areas, incomplete solutions, and the KCP/composable trust solution.

In February 2024, a Canadian small claims tribunal ruled against Air Canada. Their chatbot had told a passenger he could book a full-fare ticket and claim a bereavement discount retroactively. He couldn't. When he tried, Air Canada's position was: the chatbot said that, not us. The tribunal disagreed. You deployed it, you own what it says.

The ruling was correct. But the more interesting problem was underneath: when the incident happened, nobody could reconstruct what context the chatbot had been given. Nobody could confirm whether a human had ever reviewed the policy the bot was consulting. Nobody could determine whether the specific bereavement policy text had been modified between deployment and the passenger's interaction. The audit trail recorded that the system was deployed. It did not record what the system knew.

That's the provenance problem. And every organization running AI agents at enterprise scale is about to hit a version of it.

The Law Is Also Knowledge. Package It.

Overview: The Law as Code — Solving the AI Compliance Identity Crisis. The problem (shapeless regulations as prose), the KCP solution (typed regulatory packages), and real-world applications across NIS2 and EU AI Act.

In the previous post, I argued that the AI provenance problem is a format problem. The knowledge going into and out of AI systems -- policies, observations, interpretations -- has no stable shape. No type, no version, no cryptographic binding. The audit trail records that something was reviewed. It cannot record what was reviewed, because the thing itself is prose that could have drifted between the moment of review and the moment of use.

KCP solves this by giving knowledge a shape: typed, versioned, signed packages. A compliance observation becomes a structured declaration with type, version, signed_by, derived_from, review_depth, valid_until. The audit trail becomes precise: "reviewed a cryptographically signed declaration of type evaluation:nis2-art21-supply-chain v1.2.0" -- not "reviewed a document."

But there is a gap I did not address. I gave shape to the outputs -- the policies, the observations, the interpretations. I left the inputs shapeless. The regulations and laws that AI agents evaluate against are still prose in the system prompt.

This post closes that gap.