Enterprise AI deals are won or lost in the security review, not the demo. These are the questions that come up nearly every time, and what a good answer sounds like.
If you can't answer these crisply, your sales cycle will stretch by months regardless of how strong the product is.
The twelve questions
1. "Where is our data stored?"
What they want: a specific region, and confirmation it doesn't leave. Good answer: the region, the provider, and whether you can host in a region of their choosing. Vague answers here end conversations.
2. "Do you train on our data?"
What they want: a flat no, in writing, extending to your subprocessors. Good answer: no, plus the contractual terms with your model provider that make it true. "We've turned that setting off" is weaker than "our agreement prohibits it."
3. "Who at your company can see our data?"
What they want: the smallest possible number, with access logging. Good answer: role-based access, logged, with a named process for support access that requires customer approval.
4. "What happens if we leave?"
What they want: to know they're not trapped. Good answer: export formats, deletion timeline, and confirmation that deletion includes derived data — embeddings, caches, logs. Willingness to answer this well is itself a trust signal.
5. "Can you delete a single user's data on request?"
What they want: a real deletion path, not a soft delete flag. Good answer: the actual mechanism and where the data lives that must be purged. If you haven't tested this, test it before the call.
6. "How do we audit what the AI did?"
What they want: logs they can inspect independently. Good answer: per-action logs including inputs, outputs, model version, prompt version, timestamp, and triggering user — exportable to their SIEM.
7. "What if the AI does something wrong?"
What they want: to know there's a brake. Good answer: which actions are gated behind human approval, how to disable AI functions without breaking the product, and what the rollback looks like.
8. "Have you tested for prompt injection?"
What they want: evidence you've thought about adversarial input. Good answer: your input handling approach, what content is treated as untrusted, and what testing you've done. "We haven't looked at that" is a deal-stopper for anyone regulated.
9. "Which subprocessors do you use?"
What they want: the full list, because their compliance obligations flow through to yours. Good answer: a maintained, public subprocessor list with a change notification policy.
10. "What certifications do you hold?"
What they want: SOC 2, ISO 27001, or a credible roadmap toward them. Good answer: what you have, what's in progress with a date, and what compensating controls exist meanwhile. Honesty here is better than implication.
11. "How do you handle model updates?"
What they want: assurance that behavior won't silently change under them. Good answer: version pinning, a change notification process, and regression testing against a maintained evaluation set.
12. "Can we run this in our own environment?"
What they want: an option, especially in regulated sectors. Good answer: whether you support it and what changes. A clear no with reasoning beats a vague maybe.
The pattern
Every one of these questions is really asking: when this goes wrong, will we know, and can we stop it?
Enterprise buyers aren't asking whether your AI is perfect. They're asking whether you've designed for the day it isn't.
Teams that treat the security review as a hurdle write defensive answers and stall. Teams that treat it as a product requirement built the answers into the architecture — and clear the review in a week.
FAQ
What do enterprise buyers ask about AI security? Primarily: where data is stored, whether you train on it, who can access it, how AI actions are audited, what happens on exit, and how the AI can be stopped or rolled back.
What's the fastest way to stall an enterprise AI deal? A vague answer on data residency or model training. Both suggest the vendor hasn't designed for enterprise requirements.
Do we need SOC 2 to sell AI to enterprises? Not always, but you need a credible answer — either the certification, a dated roadmap, or documented compensating controls.
Why does prompt injection come up in procurement? Because any system ingesting user-supplied or third-party content can be manipulated into unintended actions. Regulated buyers expect vendors to have tested for it.
I'm Ravi Jadav, Chief Product Officer and Co-Founder at Sunbots Innovations and Co-Founder at Xwits Developers. If you're preparing an AI product for enterprise buyers, get in touch.