Read only authorized sources
Identity, team scope, source permissions and query access need server-side verification. Access needs to be checked against identity and data scope for every request.
Trust is a product requirement
Before AI takes action,
make its boundaries clear.
Set clear, verifiable boundaries for enterprise AI, from data access to action approval.
Separate visibility from action
Seeing an account does not authorize changing its quote. Sending, approving, writing and permission changes need independent access and confirmation policies.
Show evidence and gaps
Show sources, update times and definitions. When data is insufficient, say so instead of generating complete business conclusions.
Document processing locations
Specify storage, model inputs and tool execution locations. Private deployment alone does not imply zero external data transfer.
Trace actions and outcomes
Record the initiator, approver, target and result. Audit scope, retention and export must be verified in the actual system.
Hand off on failure
Stop on missing data, unauthorized requests or tool failures, then provide human handoff rather than endless retries.
Security controls, data-processing scope and service commitments are specified in the assessment and delivery agreement.
Before go-live
The checklist comes
before the launch button.
- Data owner signs off on access and deletion
- Test denied access across teams and roles
- Verify consequential actions cannot bypass approval
- Verify backup, recovery, exceptions and human handoff
- Document data and model processing paths
Start with one team.You don’t need to change everything.
You don’t need to change everything.
Start with work that matters.
Agree on the workflow, data and success criteria before starting.