Control by design
Your team should be able to see what AI can access, approve what it changes, and inspect what happened.
The Brain’s data layer lives in your AWS account. Approved pipelines read connected sources; agents query the governed Brain, without direct source-system credentials in their query path.
Ingestion and external actions have separately configured connections. Refresh schedules and pipeline records make the age of the inputs visible.
People and agents use named identities. Domains define the data they may see; roles define the capabilities they may use. Those boundaries apply to both records and knowledge retrieval.
Access to a finance dataset doesn’t grant permission to send an email or change a record. Sharing information with another person is a separate disclosure to consider.
Connected external writes wait for an authorized reviewer. Your team sees the proposed change and its destination before deciding whether it should run.
Enabled actions, approval rules and failure handling are agreed during implementation. The reviewer’s decision and the action’s outcome become part of the record.
Activity records show who called, which tool they requested, and whether the call was allowed, denied or errored. Source references let reviewers trace the evidence behind an answer.
Knowledge corrections have a review history, too: what changed, who accepted it, and why. Evaluations help your team investigate changes in access behavior.
Bring your reviewers in early
Bring a real workflow and the people accountable for it. We work through the architecture, permissions and approval flow with your IT and security teams.
Identify connected sources, caller identities and the records or knowledge each role should reach.
Exercise permitted and denied requests. Inspect the results and investigate unexpected behavior.
Agree who approves actions, reviews knowledge corrections and operates the environment after launch.
ThinkWork
We’ll walk through the architecture, access model and approval flow.
Talk to ThinkWork