AI Governance Explained: Who Decides What AI May Do
AI governance gives people clear responsibility for AI use: who approves it, which boundaries apply, and when those decisions are reviewed.
Updated 5 min read
AI governance is how an organization decides what AI may do, who is responsible for its use, and how those choices are checked. It turns a vague request to “use AI responsibly” into clear boundaries and named people who can approve, change, or stop the work.
You do not need to understand how a model is built to ask who approves its output. Tools such as fenbs keep the team’s agreed rules and decisions available to connected assistants, so those answers can survive beyond one person’s conversation.
What does governance decide in everyday work?
It decides which uses are acceptable and what must happen around them. An assistant drafting an internal meeting summary raises different questions from software recommending who receives a loan. The purpose, people affected, information involved, and possible consequences all matter.
For a simple workplace example, ask whether an assistant may use a document, whether a person must review the result, and who can approve sending it to a customer. A useful answer is specific enough that another teammate would make the same call.
The NIST AI Risk Management Framework (opens in a new tab) is voluntary guidance for addressing risks associated with AI. It is a reference for organizing the questions, rather than a substitute for deciding which obligations apply to your work.
- Purpose: what is the assistant helping accomplish, and what falls outside that purpose?
- Authority: who can approve this use and who can stop it?
- Boundaries: which information, actions, and audiences are allowed?
- Review: what must be checked before someone relies on the result?
- Change: what events require the original decision to be reconsidered?
Who is accountable when an assistant does the work?
Assign a person who owns the business use. That person should understand why the workflow exists, who depends on it, and what a bad result could do. Technical colleagues may manage the connection, and a subject expert may review output, but those jobs do not automatically make either person the owner of the overall decision.
In a small studio, one manager might approve the use while a project lead checks client-facing drafts. In a larger organization, privacy, security, legal, and other specialists may need to advise. Avoid making “everyone” responsible for a decision that nobody has authority to make.
Give the owner a practical stopping path. If a reviewer finds an invented fact or unexpected disclosure, they should know whom to contact and whether to pause publication. Governance is weaker when the only possible response is hoping the person who set up the tool is online.
How is an approved rule different from a helpful instruction?
A helpful instruction shapes the current task: “Make this summary shorter.” An approved rule sets a boundary that continues to apply: “A project lead must approve summaries before they are sent to clients.” The second statement needs an owner, scope, and a practical way to follow it.
Keep suggestions separate from decisions. An assistant can suggest removing review to save time, but that suggestion is not approval. Write down who agreed to any change and what replaced the previous rule. Otherwise, a casual conversation can quietly become the team’s new operating policy.
This is where Decisions and rules in fenbs help: people decide, assistants can record what people chose, and rules appear first in the context connected assistants read. Access controls in the relevant applications still need to enforce the boundaries that require technical enforcement.
Give every session the same approved starting point
Write the person’s decision once, including its scope and reason, and connect related follow-up tasks. fenbs shows the rules first when an assistant reads the project context. History keeps changes to that shared record attributed to the person or assistant that made them.
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What would this look like for a normal team task?
Say a three-person design studio wants help turning meeting notes into client updates. The manager allows drafts from approved notes, the project lead reviews names and commitments, and a person sends the final message. Private budget notes are outside the agreed source material.
Before starting, the project lead checks that the assistant has access only to the intended materials. During review, they compare the draft with the notes and remove any invented commitment. Afterward, they record a recurring problem if the assistant repeatedly confuses a suggestion with an agreed delivery date.
| Question | A usable answer |
|---|---|
| Who owns this use? | The studio manager approves the purpose and boundaries. |
| Who reviews the result? | The project lead compares factual statements with the approved notes. |
| What stays with a person? | Approving commitments and sending the message. |
| When do we reconsider? | When source access, audience, or the assistant’s actions change. |
These are illustrative choices, not a universal policy for design studios. Your own workflow may need different checks. The useful habit is making the choices explicit before someone treats an unreviewed draft as an authorized message.
Why does review continue after the first approval?
A useful workflow can change without anyone formally redesigning it. Someone adds another folder, connects a sending tool, or starts using the output for a different audience. An earlier approval may no longer describe what actually happens.
The NIST governance playbook (opens in a new tab) includes clear responsibilities, monitoring and review processes, change management, and incident response among its suggested policy considerations. Those ideas support a simple habit: revisit a decision when its assumptions stop matching the work.
Review a small sample of completed work, ask whether the checks were performed, and follow up on errors. Record what remains uncertain instead of replacing it with “approved.” The practical guide to using AI at work helps connect those questions to everyday tasks.
How to do this with fenbs?
- Create a free account and add one project where your team already uses an assistant. One person can start free, with no credit card.
- Record the approved use and its boundaries in Decisions and rules. Name the person who made each decision, and leave unresolved questions open.
- Add tasks for access checks, output review, and other agreed follow-up work. Give each task a plan that explains how its result will be checked.
- Connect a supported assistant, ask it to read the project context before working, and save progress before the session ends.
- Use History when reviewing changes, and record a lesson when a mistake should change how the next task is handled.
fenbs is not the right fit for automated monitoring of everything your employees do in other AI tools; it does not provide that service. It keeps the shared work record. Application settings, security tools, professional advice, and people’s review may all still be necessary.
How do you keep governance understandable?
Ask a teammate to explain the next action in their own words. They should know which material is allowed, what requires review, and whom to ask when a request crosses the boundary. Rewrite a rule that only its author understands.
Explore the AI governance hub, use AI governance training for the separate learning question, and see governance for small teams for a smaller operating context. An AI brain preserves the work; people remain responsible for the decisions it records.