Local data control
Workloads can remain on company-controlled infrastructure, reducing reliance on external model providers for sensitive use cases.
Security architecture
Access control before AI.
Private infrastructure helps control where data runs. Secure private AI also requires identity, authorization, retrieval policy, tool boundaries, and auditable operation.
Core rule
The model is not the security boundary.
A user’s identity and permissions are evaluated before company information is retrieved. The model receives only the approved context needed for that request.
The model does not decide what a user is allowed to see.Authorization happens before data reaches the model.
SSO and identity
Connect employees to company identity so access follows managed users, groups, and lifecycle controls.
Role-based access
Apply department, role, source, and document permissions before retrieval rather than filtering a generated answer afterward.
Department isolation
Sales does not automatically receive Finance data. Finance does not automatically receive HR data. Shared access must be intentional.
Audit logging
Record relevant requests, source access, tool actions, and system events to support investigation and governance.
Controlled agents
Limit agents to approved tools and data, require appropriate confirmation, and use sandboxed execution when workflows create risk.
Secure retrieval flow
Policy travels with every request
Authenticate
Confirm the employee through company identity.
Authorize
Resolve groups, role, source rights, and request policy.
Retrieve
Search only the information permitted for that employee.
Generate
Send approved context to the model and record the transaction.
Design security into the pilot
Map identity, data boundaries, and tool permissions before deployment begins.
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