Private AI deployment data boundaries

Map prompts, files, model endpoints, retention, access, and incident responsibilities for private AI.

On this page

Map the complete data path

A private interface does not prove a private AI service. Map browser, identity, prompt, upload, gateway, model endpoint, logs, vector store, backups, and support access.

Set allowed data and endpoints

State data classes users may submit, approved model endpoints, retention, export, deletion, and incident rules. Treat each endpoint as a separate data boundary.

Control access and secrets

Separate administrators from users, scope backend credentials, restrict egress, test offboarding, and keep prompts out of unnecessary diagnostics.

Verify with safe test data

Use non-sensitive prompts and files to test routing, retention, model selection, excluded endpoints, and administrator visibility. Record any path that stores or forwards data.

Boundary record

Review each path.

PathDecisionEvidence
PromptAllowed classesSafe routing test.
FileRetentionUpload test.
EndpointApprovalNetwork review.
LogsAccessRestricted account test.

Questions

Does local hosting settle residency?

No. Check every connected model and service.

What is retained?

Document prompts, files, outputs, metadata, and backups.

What proves boundary?

Observed path tests with normal and administrator roles.

Control cycle

Inventory paths and owners.

Checks

Keep evidence current.

Endpoint register

Endpoints are approved.

Role test

Access is restricted.

Path test

Routing is observed.

Sources and further reading

Talk to our team.

Tell us what you're working on, whether it's a deployment, an audit, a security test or a cyber range. You'll speak with an engineer who can help you scope it.

  • 30-minute call: free, with no obligation.
  • NDA on request: we can sign before you share details.
  • Clear next steps: a scope and plan after the call.