AI and Customer Data

We do not train on your data

QNSI does not use Customer Data — content, prompts, keys, secrets, stored objects, search indexes, or audit records — to train, fine-tune, or evaluate any model that QNSI operates or distributes.

When you run an AI workload on QNSI, you are running YOUR model on YOUR data. QNSI orchestrates and isolates that workload; it does not learn from it.

Isolation of AI workloads

• AI inference and fine-tuning run inside hardware-isolated enclaves (Intel SGX, AMD SEV, or AWS Nitro, depending on deployment), with attestation recorded before a workload is admitted.
• Enclave workloads are tenant-scoped. Cross-tenant access is denied at the edge gateway, at the service layer, and by the tenant claim in the token — a defence-in-depth boundary, not a single check.
• Model artefacts and prompts are encrypted at rest under the tenant's own key material.

What is recorded

Metadata about an AI operation — that it happened, when, by which identity, against which key or model reference, and whether attestation passed — is written to the tamper-evident audit chain. The content of the prompt or the model output is not written to the audit chain.

Third-party model providers

If you configure QNSI to call an external model provider, that provider becomes a recipient of whatever you send it, under that provider's terms — not ours. QNSI will tell you when a configuration does this. QNSI does not silently route Customer Data to a third-party model.