what human review do you apply to AI-generated output?

The buyer is asking who is accountable for a wrong answer that reaches their customer or their staff.

They expect the control to be proportionate. A support draft reviewed by an agent before sending is a real control, and it is enough for many use cases.

Answer per use case, not for the company as a whole. Different outputs carry different consequences.

For each one, say who reviews, at what point, and what happens when the output is wrong.

"Support replies are drafted by a model and edited by a person before sending. Our in-product summary is labelled as generated, and errors are reported through the same path as any product bug."

If a use case has no human in the loop, say so and say why the consequence is tolerable. Reviewers accept that far more often than they accept silence.

• A short list of AI use cases with the review step named for each.
• The escalation path: where a wrong output goes and who owns it.
• Whether generated content is labelled to the end user.
• A record that reported errors are actually tracked, even if that record is your normal issue tracker.

The assessment asks who reviews outputs, at what point, and what happens when an output is wrong.

bytecloud generates a risk review per AI use case and an incident procedure from those answers, and publishes both on the trust page.

Where the assessment finds no review step, it is named as a gap rather than written over.

bytecloud runs on AI agents itself, on NanoCorp, so the human review question is one we answer about our own work too.

bytecloud is not an auditor, not a law firm and not a certification body. nothing here promises that a review will pass.

• do you maintain an inventory of the AI models and systems you use?
• do you align with the NIST AI RMF or hold ISO 42001?