AI access governance defines who may use which company information through AI, enforces those boundaries and retains evidence for review. Evaluate identity, source and workspace permissions, retrieval paths, cached copies and available records separately. Permission checks and logs can support an investigation; neither establishes that every access was recorded or that nothing leaked.
Why it matters now
The moment you point AI at your company's knowledge, a quiet risk appears: the AI can surface anything the person asking can technically reach. Years of over-permissioned folders, inherited access, and widely shared files become instantly answerable. An intern can ask for the layoff plan or the CEO's salary, and a helpful assistant may oblige.
Security review should establish which people, agents and retrieval paths the controls cover. Test intended and restricted readers, including direct reads and cached results, rather than treating a governance label as a guarantee that every disclosure path is covered.
What it actually controls
Identity: it confirms who, or what, is behind a request, a person or an autonomous agent, using your existing login. No identity, no answer.
Permission-aware retrieval: check the requester's effective access before returning information. Establish which source and workspace rules apply, how cached copies are scoped and how permission changes propagate.
Field-level restrictions: where supported, withhold restricted sections within an allowed document. If that boundary cannot be enforced, exclude the document or supply a separately approved version.
Evidence: inspect the available access records, their fields and retention. Verify coverage for the operations you depend on; best-effort logging and an intact record do not establish what happened on unrecorded paths.
How it differs from DLP and classic security
Data loss prevention and sensitivity labeling are about classifying and restricting files. They help, but they are a separate program you configure and maintain, and they were not built for the way AI fans out across everything at once.
AI access governance concerns identity and resource checks around retrieval and returned information. DLP and labeling can support that boundary, but neither establishes connector, copied-file or cache behavior on its own. Test the combined controls and available evidence for the intended workflow.
What good governance looks like
Good governance makes its boundaries testable. Distinguish a connected source from a manually uploaded copy, test access and revocation across source and cache paths, and establish whether field-level restrictions are supported. Check record coverage and record access separately. Agent approvals and runtime stop controls require their own evidence; a cryptographic seal can show record integrity without proving that the record is complete.
Where HeyBrain fits
Evaluate HeyBrain with approved material and the sources available in your account. Supported knowledge-access metadata recording is best-effort and varies by path and integration. A manual upload is a separate copy, so do not assume that it inherits later source permission changes. Test allowed and denied reads, cache behavior, revocation and available record coverage for your intended client. Anchoring proof on-chain is on the roadmap. A 14-day Personal Pro trial is available with a card required.