Can a spreadsheet hold these templates?

Yes. A spreadsheet can hold a small register and review log if ownership, access, and links are clear. Keep detailed evidence in an appropriate location and avoid creating multiple competing versions.

Does completing the toolkit certify our AI use?

No. The templates organize evidence and decisions. They do not establish that all applicable requirements have been met or replace specialist review when the workflow needs it.

Should every employee create a separate register?

Usually one current shared entry per workflow is easier to maintain. Different teams can contribute to that entry while a named owner keeps its scope and status accurate.

Do we need a template for each AI vendor?

Start with the workflow and add vendor-specific evidence when needed. Two uses of the same vendor can have different inputs, action permissions, and approval needs.

AI Governance Toolkit: Registers, Checklists, and Review Notes

Build a practical AI governance toolkit with reusable records for AI use, review evidence, decisions, exceptions, and follow-up work.

September 21, 2026Updated October 21, 20266 min read

An AI governance toolkit is a small collection of records that helps you identify AI uses, review the evidence, record decisions, and follow up on problems. Start with a use register, a review checklist, a decision record, an exception note, and a review log.

Each record should answer a different question and point to the same workflow. Tools like fenbs keep the agreed rules and follow-up tasks in the shared work record, so the answer does not disappear inside one person’s chat. You can keep restricted evidence elsewhere and link to it rather than duplicating sensitive material.

What belongs in a small AI governance toolkit?

Build for the questions your team must answer during ordinary work: What are we using? Who owns it? What did we check? What is allowed now? What needs attention next? A useful toolkit makes those answers easy to find without turning every draft into an approval packet.

Five reusable records and their jobs
Record	Question it answers	Update it when
AI use register	Which workflow uses AI and who owns it?	A use begins, changes, pauses, or ends
Review checklist	What evidence supports this proposed use?	A review is performed or evidence changes
Decision record	What did an authorized person agree?	A decision is made or replaced
Exception note	Which temporary departure was approved?	An exception is requested, reviewed, or expires
Review log	What happened after approval?	A check, incident, or material change occurs

These are suggested templates, not official forms. The NIST AI RMF Playbook (opens in a new tab) offers voluntary actions that organizations can select for their use case. It explicitly says it is not a checklist to follow in its entirety; use the same judgment when adapting this kit.

How should you fill in the AI use register?

Make one entry per workflow, not merely one per vendor. The same assistant used to brainstorm a public headline and summarize employee records creates different questions. Give each use a stable reference so its reviews and decisions remain connected even when you rename the workflow.

Copyable AI use register entry

Use reference and name:
Business purpose:
Workflow owner and backup:
Tool and configuration reference:
People affected:
Input categories and approved sources:
Output and where it goes:
Actions the AI can take:
Human review point:
Current status and decision reference:
Evidence location:
Next review and change triggers:

Write categories of information rather than copying the information itself. “Approved public product descriptions” is enough for a register; a customer’s support message is usually unnecessary. Name the output destination because a private draft and an automatically published answer need different boundaries.

The NIST Govern guidance on inventories (opens in a new tab) includes responsibility for maintaining inventory information. Choose one accountable owner for each entry and ask them to confirm its accuracy when the workflow changes.

A register describes work; it does not make an AI remember it automatically. What an AI brain is explains why connected assistants need a deliberate way to read the relevant record.

What should the review checklist actually check?

Use questions that require evidence, with answers of yes, no, unknown, or not applicable. A checked box without a source can hide an assumption. For every unknown, name the missing evidence and the person who will obtain it. Explain any not-applicable answer so another reviewer can challenge it.

Purpose: Is the intended use specific, and have excluded uses been written down?
Inputs: Are the sources approved for this use, and has someone checked the relevant access and handling settings?
Outputs: Has a reviewer tested representative examples, including incomplete and misleading inputs?
Actions: Can the AI publish, send, change, delete, or spend anything, and are those actions appropriately restricted?
People: Can affected users report a problem, and does someone have authority to pause the workflow?
Evidence: Is the test method, result, limitation, reviewer, and date recorded?

Say a three-person design studio uses AI to draft project status emails from approved internal summaries. A reviewer tests an ordinary summary, a contradictory one, and one with a missing deadline. The checklist should preserve the actual results, including whether the assistant invented a date. “We tested some emails” is not enough to support a decision.

Use the narrower guide to AI agent governance for small teams when the workflow includes assistants taking actions. In fenbs, connect the unresolved checks to tasks and keep the human-approved boundaries in Decisions and rules.

How do decisions and exceptions stay separate?

A decision records the agreed position. An exception records a specific, temporary departure from it. Combining them makes it hard to tell whether a one-off approval silently changed the rule for everybody. Give an exception its own scope, person who approved it, end condition, and fallback.

Copyable decision and exception notes

DECISION
Question and use reference:
Outcome: approved / limited / paused
Boundary and reason:
Person who decided and date:
Evidence considered and remaining limits:
Implementation owner:
Review trigger:

EXCEPTION
Rule affected and use reference:
Requested departure and reason:
People and work covered:
Extra safeguards:
Person authorized to approve:
Start and expiry:
Fallback when it ends:
Final outcome:

In the studio example, the ordinary rule could require a project manager to review every outgoing status email. A request to skip that check for a deadline is an exception request, not a reason to erase the rule. If nobody with authority approves it, the existing review requirement continues.

An expiry needs an owner who will act on it. Ask that person to stop the temporary arrangement, restore the normal process, or seek a fresh decision before it ends. A date in a note does not change a tool’s permissions automatically.

What makes the review log useful later?

Log the event, evidence, outcome, owner, and next step. Include clean results as well as problems so the team can distinguish an unreviewed use from one that was checked and left unchanged. Keep enough detail to reproduce the check without storing confidential inputs in a broadly shared record.

For the studio, a log entry might say that the reviewer found an invented completion date in a fictional test email, paused use for deadline statements, and assigned a revision to the input format. The next entry should explain what was retested and whether that narrower use is now approved.

Avoid a single “compliant” field. It hides the scope, evidence, and limits of a review. A more useful conclusion is “approved for drafting from the named summary source, with human review before sending.” Other uses remain undecided until someone reviews them.

How fenbs helps

Keep the toolkit connected to real work

Use fenbs for the rules people approved, the tasks needed to implement them, and the lessons from reviews. History records who changed the shared work, whether a person or assistant. Keep detailed restricted evidence in its appropriate system and save the useful reference and outcome with the project.

Create a free account

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How to do this with fenbs

Create a free account and add a project for the AI workflow you want to review.
Add a concise AI context note describing its owner, inputs, outputs, and evidence location. Keep confidential source records out of the note.
Record human-approved boundaries in Decisions and rules, and leave unresolved approval questions open.
Create tasks for missing evidence, implementation changes, and the next review. Add test results to the relevant task when a check is complete.
Connect a supported assistant, ask it to read the project context before working, and save progress with the next step before stopping.

fenbs does not discover every AI use across your company or run technical risk tests. Choose dedicated systems for those needs and maintain this work record alongside them. One person can start free, with no credit card; colleagues sharing work need the appropriate team setup.

How do you keep the toolkit from becoming paperwork?

Try the kit on one workflow and remove fields that neither inform a decision nor help someone maintain it. Keep evidence links current, use the same reference throughout, and ask the owner to update the record when the workflow changes. A short complete entry beats a long form filled with guesses.

Use AI governance roles for the broader division of responsibility, and the AI governance hub for adjacent guidance. For the everyday habit of choosing and checking work, share how to use AI at work with the people maintaining the kit.