Evidence over assurances: what a useful AI security questionnaire answer looks like

Most AI security questionnaires get the same kind of answer. "We take security seriously." "Access is role-based." "Data is encrypted." These may all be true. None of them lets the reader check anything.

A useful answer points at something the reader can inspect. It is narrow, specific and tied to one workflow.

Take a hypothetical example, not a client engagement: an assistant that drafts replies to internal IT help requests. A person reads each draft and sends it or discards it.

A weak answer to "How do you control access to data?" is "The assistant follows least privilege."

A stronger answer describes this workflow:

That answer is short. It also invites a test. Someone can open the queue, check the permissions and read the log.

An assurance has no date. Evidence does. "We confirmed on a stated date that the assistant's account could not read tickets outside its queue" tells the reader what was tested and how fresh it is.

If the workflow uses MCP, name the tools the server exposes and which ones this assistant can call. If it does not use MCP, do not add it to the answer.

A good answer says what it does not cover. Perhaps drafts can quote text from a ticket that contains a customer's personal details. Say so, and say what the reviewer is expected to catch. Non-regulated work still carries privacy and security obligations, and an honest limit is more credible than a blanket claim.

The person receiving the questionnaire may not be technical. Write the answer so they can follow it without you in the room. Name the workflow, the permissions, the human step, the log and the known gaps. Keep each to a sentence or two.

Read your answer and ask whether a stranger could verify it in an afternoon. If the only way to check it is to trust you, it is still an assurance.

We review bounded workflows like this one, and the output is a short write-up the buyer can actually read.

I'm Jonathan Melton. I run FusionAL, focused on reviewing and integrating lower-risk internal AI workflows, one bounded workflow at a time.