“Do You Train on My Data?” Answering the #1 AI Procurement Question

Across AI vendor evaluations in 2026, one question comes up more than any other: "Is my data used to train your models?" Buyers ask it because the answer determines whether their confidential data could resurface elsewhere, and because it is a fast proxy for how seriously a vendor takes data governance. How you answer — and how easy you make it to find the answer — has a direct effect on whether the deal moves forward.

Why this question carries so much weight

For a buyer, training is the scariest data-usage pattern. If their prompts, documents, or customer records were used to train a model, that data could in principle influence outputs seen by other customers, including competitors. Even when the real-world risk is low, the perceived risk is high, and procurement teams are trained to treat it as a gating question. A confident, documented "no" removes a major objection; a vague answer creates one.

Finding your real answer

You cannot answer for your buyers until you have answered for yourself, per provider:

• Check the tier you use. Foundation-model providers separate consumer products from business/API tiers. Business and API tiers typically default to no-training and short or zero retention; consumer tiers may not. Confirm which you are on.
• Read the actual terms. Find the specific clause on training and retention in your provider's business terms or DPA, and save a link or screenshot. "We think so" is not an answer you can stand behind.
• Account for every provider. If you use more than one model provider, you need the answer for each. One provider's opt-out does not cover another.
• Check your own pipeline. Make sure you are not separately logging prompts into a dataset that gets used for fine-tuning without the same protections.

Presenting the answer so it helps you

Once you know the answer, make it impossible to miss:

• State it plainly and prominently. "Customer data is never used to train AI models" belongs at the top of your AI governance section, not buried in a policy PDF.
• Break it down per provider. Pair the headline with a per-provider view — which models, what data, what training stance — so a reviewer can verify it.
• Show the mechanism. Note that you use zero-retention/no-training API tiers, and cite the terms. Specifics build credibility.
• Keep it current. If you change providers or tiers, update the statement. An out-of-date "no" is worse than none.

Turn a blocker into a differentiator

The vendors who win on this question are not necessarily the ones with the strictest data handling — they are the ones who make their handling easiest to verify. Publishing a clear, per-provider training stance on your trust center means the reviewer answers their own question before they even send the questionnaire. That is the difference between a deal that stalls for a week of email and one that keeps moving.

An affordable AI trust center makes this a one-time setup: state your global training stance, tag each model provider with its per-provider stance, and let the page do the answering. ShieldPage supports exactly this at SMB pricing — so a small AI company can answer procurement's hardest question as convincingly as a much larger vendor.