Judging AI governance

Can you prove afterwards who did what with AI?

Short answerOnly with a complete, tamper-resistant record that ties every interaction to an identified person or agent, kept long enough to matter. The question decides what you have after something goes wrong, which is the only moment anyone asks it in earnest.

Where a person sits in the chain of events, attribution establishes which person. Where no person sits in it, as with an agent acting on its own, attribution is the only way to reconstruct what happened at all.

Tamper-resistant is the word that carries the weight

A log the operator can quietly edit proves nothing about the operator. A record produced by the party under examination is worth what any such record is worth. So the first question about a record is who can change it, and the second is what it contains.

The service account problem

When a person acts inside a system, the record names them. When an autonomous agent acts, in most deployments today, the record says "service account." It cannot say which agent acted, under whose authority, or on what instruction. Standards work now under way treats this as the central gap, and it is the gap a liability question falls into.

Four questions for any log

  • Where is it kept?
  • How long is it kept?
  • Is each entry attributable to a named person or a named agent?
  • Who can alter it, and would anyone know?

How to ask it

A good answer
A complete, tamper-resistant record tying every interaction to an identified person or agent, retained long enough to matter.
An evasion
"Everything is logged." Logged where, for how long, attributable to whom, and can it be altered?
The follow-up
If this went to litigation, what would you be able to produce?

What to check

  • Pick one AI interaction from last week and try to trace it to a person.
  • List every agent or automation that calls a model, and note the identity its requests carry.
  • Ask your counsel what they would need to produce if a dispute turned on one of those interactions, then check whether the record holds it.

Sources

  1. National Institute of Standards and Technology, AI Risk Management Framework 1.0. Trustworthiness characteristics, including accountability and transparency.
  2. NIST Center for AI Standards and Innovation, AI Agent Standards Initiative, February 2026. Agents run without dedicated identity, authorization, or accountability controls. Reached via secondary reporting; the primary document has not yet been retrieved.

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