TeamStation AI / Executive decisions

AI governance puts responsibility before access

AI governance defines what a system may do, who can authorize it, and how its risks are reviewed. It connects policy to real access controls, accountable decisions, and evidence throughout the system lifecycle.

Review the decision guide

At a glance

Define permitted use
Name the system, purpose, data and accountable owner.
Enforce the limit
Apply access controls and approval requirements to actual actions.
Review what changed
Reassess after new data, tools, scope or incidents.

How it works

Policy needs an enforceable boundary

Scroll sideways to see the whole diagram. The same steps are written out below.

Policy needs an enforceable boundary

Define permitted use
Name the system, purpose, data and accountable owner.
Decision authority
Specify which actions need approval and who may grant it.
Business risk owner
A recorded decision tied to the exact action and approver.

Denied action path

Permission scope plus tests of prohibited access.

Implement access and approval boundaries, then test denied as well as allowed actions.

Allowed action path

Enforce the limit
Apply access controls and approval requirements to actual actions.
Evidence to inspect
Retain decision records and review the controls after meaningful changes or incidents.
Review what changed
Reassess after new data, tools, scope or incidents.

These controls are planning guidance, not a legal determination or certification. Applicable requirements depend on the jurisdiction, data, system use, and agreement.

The problem

A policy document doesn't stop an unapproved tool action. When app permissions are messy, name the owners, set the scope, and test the controls where data and decisions actually move.

Translate policy into an operating control

Data boundary

Identify permitted sources, recipients, retention, and sensitive data restrictions.

Owner: Data owner and security lead

Decision authority

Specify which actions need approval and who may grant it.

Owner: Business risk owner

Oversight

Set review triggers for changed models, workflows, data, and incidents.

Owner: Governance owner

Governance defines the rule; delivery governance checks that the exact release still follows it. Bind controls to release evidence.

Illustrative scenario / not a client result

A sales assistant can read records, but can it change them?

A sales assistant summarizes customer records for an account team. The proposed next step is to update those records automatically. Reading and changing a record carry different consequences, and access to the CRM doesn't itself authorize every action the assistant can perform.

Read-only assistance: Restrict the service to permitted records and keep output separate from system changes.
Approved changes: Require an authorized person to approve the exact record and proposed change.
Bounded automation: Consider limited actions only after denied-action, duplicate and recovery tests support the agreed risk boundary.

The decision: Name who can accept the remaining risk and who can revoke access. Preserve a record of the decision and the tested control. A successful allowed action isn't enough; test the actions the system must refuse as well.

Put the plan into practice

Step 1Inventory the AI systems and intended uses, including third-party services.
Step 2Classify risks with the business, security, privacy, and legal owners where needed.
Step 3Implement access and approval boundaries, then test denied as well as allowed actions.
Step 4Retain decision records and review the controls after meaningful changes or incidents.

Agents need action-specific limits because a tool call can create an external effect. Separate agent capability from authority.

Control evidence register

NIST includes an AI system inventory informed by risk priorities. NIST AI RMF 1.0 (2023), GOVERN 1.6.

TeamStation planning worksheet informed by the cited reference, not a customer result, certification, or completed assessment.

ControlSystem inventory
Evidence to inspectOwner, purpose, data sources, provider, and current operating status.
Review triggerA new use case or provider.
ControlLeast privilege
Evidence to inspectPermission scope plus tests of prohibited access.
Review triggerA role, tool, or data change.
ControlApproval boundary
Evidence to inspectA recorded decision tied to the exact action and approver.
Review triggerExpanded autonomy or scope.
ControlIncident response
Evidence to inspectEscalation record, containment action, and recovery evidence.
Review triggerA harmful or unexpected result.

What this does not establish

These controls are planning guidance, not a legal determination or certification. Applicable requirements depend on the jurisdiction, data, system use, and agreement.

Questions before you commit

Is AI governance only a policy exercise?

No. Policy needs enforceable access controls, approval paths, testing, monitoring, and accountable owners. A written rule alone does not demonstrate that a control works.

Does following this guide establish compliance?

No. Compliance depends on applicable requirements and verified implementation. This guide does not establish certification or legal compliance.

From planning to delivery

What to scope with TeamStation

We can scope governance engineering as implemented access, evaluation and delivery controls. Your security, privacy and business owners define the policy and retain risk acceptance.

A control backlog tied to real systems and accountable owners.
Allowed and denied behavior tests with retained results.
Review triggers and an incident escalation path.

This is engineering guidance, not a legal opinion, certification or assurance that a particular control satisfies every applicable obligation.

Discuss your delivery scope

Your next decision

Set AI data permissions, action limits, risk ownership and review triggers. Connect written policy to controls teams can test.

Turn policy into release and operating checks