AI Governance Framework Template: Owners, Rules, and Reviews

An AI governance framework template defines who may approve AI use, what evidence they need, which boundaries apply, and when a decision must be reviewed. Copy the sections below, replace each placeholder with a named person or a clear rule, and test the result on one real workflow.

Keep the approved framework separate from drafts and open questions. Tools like fenbs give those human decisions and implementation tasks a shared home that connected assistants can read. A framework becomes useful when someone can follow its approval route without asking the original author to explain it.

What should the framework cover first?

Begin with scope and authority. State which organization, team, workflows, and AI uses the framework covers, including trials and tools supplied by outside vendors. Name the person who approves the framework itself. A department cannot grant permission on behalf of another department simply by writing a broad policy.

Framework owner:
Approved by and effective date:
Teams and workflows covered:
AI uses included, including trials:
Explicit exclusions:
Related company policies:
Where the approved version lives:
How changes are proposed and approved:
Next review and change triggers:

Keep the first version narrow enough to apply. A small firm might cover AI-assisted drafting of internal project updates while excluding hiring decisions and customer-facing actions. Those exclusions clarify the document’s limits; they do not imply that a different use is automatically acceptable.

The NIST AI Risk Management Framework↗ (opens in a new tab) is intended for voluntary use. Treat this template as an organizational starting point, rather than a claim of formal alignment or proof that a particular workflow meets its requirements.

Who owns the work and who can approve it?

Separate preparing evidence from accepting the decision. Name a workflow owner who maintains the record, a reviewer who examines the evidence, an approver with authority to set the boundary, and an operator who uses the system. A small team may combine some roles, but it should state that clearly.

Give people authority that matches the responsibility. Someone named as the pause contact needs a workable route to stop the relevant action, including outside normal working hours when necessary. If nobody has that authority, record the gap and resolve it before relying on the workflow.

The NIST Playbook Govern section↗ (opens in a new tab) addresses documented responsibilities and communication. Use it to challenge gaps in your proposed structure. For assistants taking actions, AI agent governance for small teams adds practical boundaries to consider.

What approval route should each use follow?

Describe a route based on the action and its possible consequences. Avoid making the tool’s name the only criterion. Drafting an internal outline, sending a customer message, and changing a financial record create different review needs even if the same model handles all three.

Before a new use starts:
1. Owner describes purpose, inputs, outputs, and actions.
2. Reviewer identifies affected people and evidence needed.
3. Approver chooses approved, limited, paused, or rejected.
4. Owner implements the required controls.
5. Reviewer confirms the agreed checks are complete.
6. Operator receives the current boundary.

No response means:
What changes require fresh approval:
Where each decision is recorded:

Specify what “limited” means in plain words. A hypothetical consulting firm might approve AI drafting from approved project summaries, require a manager to check every draft, and prohibit automatic sending. The approval covers that defined use; adding direct access to client files requires another review.

Record people’s decisions with their reasons and limits. In fenbs, Decisions and rules can preserve those choices while tasks track whether the promised controls have actually been put in place.

What evidence should a risk review require?

Set minimum evidence rather than requiring a vague declaration that the use is safe. Describe the intended benefit, foreseeable failures, people affected, input sources, action permissions, and testing approach. Ask what remains uncertain after safeguards are applied, and who may accept that remaining uncertainty.

• Purpose and limits: the workflow’s intended outcome, excluded uses, and whether a simpler approach would do.
• Information handling: approved input categories, access settings, output destinations, and evidence sources.
• Testing: representative examples, difficult cases, expected outcomes, actual results, and known limits.
• Human control: review points, the ability to stop the action, and the fallback process.
• Decision: the authorized person’s outcome, conditions, review trigger, and required follow-up work.

Use the separate AI risk assessment template to work through an individual use in more detail. The framework should say when that assessment is required and who reviews it, rather than attempting to hold every test result itself.

In the consulting example, include a fictional summary with an omitted deadline. If the assistant invents one, record the failure and narrow or pause that part of the use. Approval depends on evidence about this workflow, not a general statement that the vendor’s model is capable.

How should exceptions and urgent problems work?

An exception needs a specific request, an authorized decision, and an end condition. State who can approve it and which boundaries they cannot waive. Do not let urgency convert an operator’s request into permission. Until a person with authority decides, the ordinary boundary still applies.

EXCEPTION
Rule and workflow affected:
Requested departure and reason:
Scope, safeguards, and evidence:
Authorized approver and decision:
Expiry or end condition:
Owner responsible for restoring the normal process:

URGENT PROBLEM
How to report:
Who can pause the workflow:
Fallback while paused:
Evidence location and access:
Who may approve resuming:
Required review and follow-up:

For the consulting firm, a rushed client update should still follow the agreed approval route. If a draft accidentally includes information outside its approved source, the operator reports it, the named person pauses use, and the team follows its existing incident process. Keep sensitive evidence in the appropriate restricted location.

How to do this with fenbs

1. Create a free account and add a project for the workflow your framework will cover first.
2. Save a concise context note describing scope and responsibilities, with a reference to the approved framework.
3. Record the human-approved boundaries in Decisions and rules. Keep approval questions open until the authorized person decides.
4. Create tasks for missing controls, evidence, and follow-up checks. Record what was tested and what remains uncertain on the relevant task.
5. Ask your connected assistant to read the context before work, and save progress with the next step and waiting decisions before stopping.

fenbs does not enforce a restriction in another application or provide certification. It fits the shared decisions and follow-up work; specialist systems still handle technical enforcement and evidence collection. Assistants can record what people decided, but cannot make the approval themselves.

When should the framework change?

Choose a review date and specific events that bring it forward: a new input source, new action permission, affected group, failure, or material vendor change. Name who checks for those events and how they notify the framework owner. Keep earlier decisions available when a new one replaces them.

Test the approved version by asking a colleague to route a new use from request to decision. For authoritative source documents, see AI governance framework PDFs; use the NIST framework guide and AI governance hub for the wider context.