What Enterprise Buyers Actually Ask About Your AI Features

The AI questions cluster into five themes: data handling (do you train on our data, where do prompts go), vendor and sub-processor (which model provider, is there a BAA or DPA), output safety and human oversight, data isolation at the model layer, and governance. Recognize the theme and you know what the buyer is actually worried about.

No. A CISO doesn't expect a Series B startup to have a formal AI governance board — they expect you to know where the data goes, who the model vendor is, and what happens when the model is confidently wrong. You need to show a documented process exists, not an ethics board.

If it's true, keep it short and reassuring: you don't train on customer data, prompts are processed by your model vendor under a no-training agreement, logged only in scoped-shape form for debugging with no content, and retained for a set window. Make sure it stays true — re-read your vendor's terms on a schedule, because a default that quietly flips to 'training unless you opt out' destroys the trust this whole exercise builds.

Say so and describe the guardrails honestly. The trust-building pattern is 'the model drafts; it does not decide,' with human review before consequential outputs take effect, but a vague 'we follow best practices' just earns you a follow-up call you don't want.

Build the AI answer bank the same way as the security one: every answer goes into a versioned document keyed by question, so the next questionnaire is a copy-paste plus a delta review. Tag each answer with a 'last verified' date and re-check anything older than three months, since the AI section changes faster than the rest.

No. The teams that handle the AI section well aren't the ones with the most sophisticated AI — they're the ones who can describe their unremarkable, well-governed AI in five honest sentences. That legibility is the product.