What measures prevent AI from leaking data between customers?

How to answer this security questionnaire question, with an expert response your security or GRC team can adapt.

Expert answer

Enforce tenant isolation on every AI request so prompts and retrieved context are scoped to a single customer, and never share caches, embeddings, or fine-tuned weights across tenants. Combine per-request authorization with provider zero-retention terms to prevent cross-customer leakage.

What the security reviewer is checking

The reviewer’s underlying fear is that another customer’s confidential material could surface in their users’ AI results — either through a model trained on pooled data or through a retrieval pipeline that searches across tenants. They want two distinct assurances. First, a training assurance: that customer data is not used to train or fine-tune models shared across customers. Second, a retrieval assurance: that every AI request is scoped to the requesting tenant at the query layer, enforced in code rather than left to the model. Answers that describe testing for cross-tenant leakage, not just designing against it, score highest.

Example response you can adapt

This is an illustrative template, not a real vendor's security posture. Replace every claim with what is actually true for your organization before submitting it.

Cross-customer isolation for AI features is enforced at the data layer, before any model is involved. We use foundation models accessed via API under enterprise terms; we do not train or fine-tune shared models on customer data, so no customer’s content can influence another’s outputs through model weights. When an AI feature retrieves context, the retrieval query is executed inside the requesting organization’s isolated data partition — tenant scoping is applied by the data access layer on every query as a structural filter, not a parameter the application can forget to pass. The model can therefore only ever see content belonging to the requesting tenant and permitted for the requesting user. Prompts and completions are processed transiently by providers under zero-retention agreements. Our test suite includes cross-tenant isolation tests that attempt to retrieve another organization’s content through the AI pipeline, and these run on every release.

Evidence reviewers expect you to attach

AI architecture diagram showing tenant-scoped retrieval and the model boundary
Statement or policy confirming no shared-model training on customer data
Provider agreements showing zero-retention and no-training terms
Description of automated cross-tenant isolation tests in CI
Penetration test summary covering multi-tenant and AI-feature scope

Follow-up questions reviewers ask next

Is tenant filtering applied in the retrieval layer or in the prompt instructions?
Have you ever observed cross-tenant contamination in AI outputs?
Do you fine-tune any model on customer data, even per-tenant?
How are embeddings or vector indexes segregated between customers?
Was AI cross-tenant isolation in scope for your last penetration test?

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