Use case / AI Governance

Govern data access for AI agents.

AI's access to data needs the same governance as your employees. Enforce what files your AI agents can access, what they can do, when they can do it, and whether or not the action needs human approval first.

STELLARBRIDGE POLICY PLANELIVE DECISION
REQUEST / 8F24 Share invoice externally

A consequential action is evaluated before the file moves.

ActorInvoice review agent
Resourceinvoice-1038.pdf
Policy outcomeHUMAN APPROVAL
Decision recordedPOLICY → EVIDENCE
Dedicated agent identitiesAction-level policyHuman approval gatesSeparate audit trails

The operating reality

An agent acting under a human credential turns automation into a gap in who did what.

01

Agents inherit human authority

Broad integration credentials give automation access it was never explicitly granted.

02

Actions happen before review

A mistaken instruction or tool call can move data before a person sees the decision.

03

Activity loses attribution

Human and agent events collapse into one identity when incident response needs the opposite.

What changes with Stellarbridge

Govern the workflow, not just the destination.

01 / 03

Identity

Give automation a first-class identity.

Every agent receives its own owner, Credentials, and policy bindings instead of borrowing a user account, so AI access governance stays attributable.

  • Accountable human owner
  • Scoped credential
  • Agent events remain distinct
02 / 03

Authority

Constrain what the agent can do.

Grant named actions against named resources. Sensitive operations, including external data movement, can require a human decision before execution.

  • Least-privilege resource scope
  • Allow, deny, or approval
  • External sharing can be gated
03 / 03

Audit

See the decision, not just the event.

The record captures actor, requested action, resource, policy outcome, and any human approval in one trail.

  • Human and agent activity separated
  • Denied attempts retained
  • Approval chain preserved

Control plane

The evidence is part of the action.

Every request resolves to an attributable identity, an explicit policy decision, and a durable record.

01Owner

Every agent maps to an accountable person or team.

02Credential

Automation uses its own scoped identity.

03Action

Policy resolves each requested operation explicitly.

04Audit

Agent behavior remains separable from human activity.

From our field notes

Go deeper on the architecture.

View all writing

Questions

Direct answers.

01Does an agent get a separate identity from its owner?

Yes. The agent has a distinct credential and audit identity while retaining an accountable human or team owner.

02Can risky actions require human approval?

Yes. Policies can require approval for operations such as external sharing while allowing lower-risk actions like metadata reads.

03Can we distinguish agent activity during an incident?

Yes. Agent and human events remain separate, including requested actions, decisions, and approval history.

04How do you prevent AI agents from exposing sensitive customer data?

Give each agent its own limited identity and allow only the files and actions that job needs. Stellarbridge checks every request against that policy before the file moves, so a person's broad login is not reused as open permission for the agent. Sensitive operations such as external sharing can require human approval (human-in-the-loop) first, and the audit trail keeps agent activity separate from people.

05How do you govern privileged access for AI agents?

An agent's high-level access should be narrower than the human who owns it, not the same. Stellarbridge limits each agent to specific files and specific actions, and can require human approval for high-risk moves. That keeps powerful automation trackable without handing it a user's full access.

06Should AI agents be modeled as service accounts or user identities in enterprise IAM?

Treat them as their own automation accounts with a named human or team owner, not as shared user logins and not as invisible add-ons to a person. Stellarbridge gives each agent its own login and rules so identity systems and audit logs stay clear about who or what acted. Generic service accounts without file-level rules still make it hard to tell what the agent did.

07How do you stop AI agents from exposing secrets?

Don't give agents blanket access to secrets or regulated files, and check policy before an agent reads, shares, or transfers anything. Stellarbridge limits which files an agent can open and can require approval before anything leaves your company. Blocked attempts are logged too, so security can see what an agent tried, not just what it did.

08Can a platform govern AI agent actions before they access company data?

Yes, at the point where the agent reaches your files. Stellarbridge checks who the agent is, which files it wants, what it's trying to do, and where the data is going, then records the result. It doesn't install software on employee devices, so use it alongside your device management and sign-in tools to make sure agents can only reach company data through Stellarbridge.

09How should IT manage access control when deploying AI agent builders?

Create agent identities first and start with no access to production files until IT grants it. Stellarbridge lets IT allow specific actions on specific files, keep human approval on risky moves, and keep builder or agent activity separate from employee accounts in the audit record. That way every new builder is not an accidental superuser.

See it in your workflow

Bring the agent, its current credentials, and the files it needs. We’ll map a least-privilege path together.

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