Access only what the role needs
Define the systems, records and actions required for the job. Separate reading from writing, and verify those permissions in each connected system.
AI agent governance. Built into every action.
Your company rules, your department knowledge, and the right permissions for each role. One clear boundary between what an agent can do and what your people decide.
Your organizationRules that travel with the work
Company policySecurity · access · approvals
01Department brainService playbooks · shared knowledge
02Agent permissionsRead shipments · request exceptions
03 Read shipment Credit needs approval
A clear rule behind every action.
Choose an action. See the permission, the responsible person, and the policy behind it.
Example permission checkInteractive
Read shipment
Allowed
Noa can read the connected shipment record for this service request.
Noa requests→Policy checks→Read allowed
Follow the rule to its source.
Credits require a manager’s decision. Payment settings stay outside this service role.
Department and role instructions stay within company policy.Review the evidence.
Inspect a sample activity log: who acted, which system was involved, and why an action was allowed or held for approval.
Read shipment
- Actor
- Noa
- Human owner
- Dana
- System
- Shipment record
- Example time
- 09:41
- Rule
- Service role may read shipment status
- Outcome
- Completed
Carrier confirms a delay; estimated delivery tomorrow.
Give every role a smaller security footprint.
Start with the information a role needs and the actions it should perform. Use these boundaries to scope your implementation with IT and security.
Keep sensitive information in scope
Identify personal, financial and confidential data before connecting a source. Agree what may enter a conversation, what should be excluded, and who may review it.
Treat connections as permissions
Review the account behind each integration, its access scope and its owner. Agree how credentials are protected, replaced and revoked when access is no longer needed.
Plan for the moments that need extra care.
A useful governance plan covers changes, unexpected requests and recovery as well as everyday work.
Check instructions from outside
Include tests for messages and documents that ask an agent to ignore policy, reveal private information or use an unrelated tool. Agree the expected refusal and escalation before launch.
Review changes before rollout
Name an owner for changes to knowledge, tools and permissions. Test representative cases and approval paths before expanding a role, and agree how to restore the previous setup.
Know who handles an incident
Define who can stop affected work, revoke connected access and investigate activity. Agree how the team checks completed actions and authorizes a safe restart.
Security starts with clear boundaries.
Define how information is protected, who can access it, and how your team stays accountable. Agree the controls for your implementation before launch.
Data protection
Set requirements for encryption, sensitive information and model-provider data use, including whether information may be retained or used for training.
Identity & access
Define sign-in requirements, administrator responsibilities and access for each role. Include ownership, rotation and revocation of connected credentials.
Activity & retention
Agree which actions need a record, who can review it and how long information is kept. Include deletion requirements for conversations, knowledge and backups.
Deployment & accountability
Confirm hosting locations, involved providers and incident contacts. Match the proposed setup to your security requirements and supporting documentation.
A little more clarity.
What is AI agent governance?
Governance defines a role’s boundaries, who decides when exceptions arise, and how activity can be reviewed. This walkthrough shows those choices using a delivery-service example.
How are agent permissions defined?
Identify the information and actions a role needs, then verify how that scope can be enforced in the connected systems. The example distinguishes a permitted read, an approval request and an action outside the role.
Can an agent approve its own exception?
Not in this workflow. The agent can prepare a request, but the designated human manager must decide. Preparing a request is not permission to execute it.
What can I review in the activity log?
The example shows the actor, action, source system, owner, applicable rule and outcome. Actual logging, retention and review requirements should be confirmed for your implementation.
Is our data used to train AI models?
Confirm data-use terms for the proposed deployment and each model provider during your security review. Ask which information is sent to providers, how long it is retained and whether it may be used for training.
How should we prepare for unexpected agent behavior?
Agree an incident owner, a way to stop affected work and revoke access, and a process for reviewing completed actions. Validate the available controls and recovery steps for your implementation before launch.
Which certifications and deployment options are available?
Discuss your organization’s requirements with our team and request the relevant verified product documentation. This demonstration is not evidence of a certification or deployment capability.
Put clear boundaries around your digital team.
Plan access, decision ownership and the evidence your team needs to review.
Discuss your requirements