AI governance is how a business decides where AI may be used, who is accountable, and what controls protect customers and the company. A small team does not necessarily need a large committee. It does need clear rules that match the consequences of the work.
Start with the tools people actually use—not an idealized list of approved software that ignores day-to-day behavior.
Use a risk framework as a guide
NIST's AI Risk Management Framework is voluntary guidance for incorporating trustworthiness into AI work. It organizes activity around Govern, Map, Measure, and Manage. Its generative-AI profile adds guidance for risks associated with generative systems.
For a small business, that can become four practical questions: Who owns the use case? What can go wrong? How will we test it? What will we do about the risks?
Following a checklist is not a certification or a guarantee of compliance. Legal obligations depend on where you operate, the data involved, and how the system is used.
1. Inventory the tools and tasks
Record the product, business purpose, owner, users, information supplied, connected systems, and outputs. Include informal uses, such as staff drafting customer messages in a personal account.
Prioritize the workflows that expose sensitive information or affect people. An internal brainstorming assistant has different consequences from a system used in hiring, credit, health, or customer eligibility decisions.
2. Set data boundaries
Write down what may be entered, what requires approval, and what is prohibited. Check provider terms for training, retention, deletion, and access rather than assuming every AI service has the same policy.
Use the least access a workflow needs. Keep a writing assistant read-only when it does not need to change company records. Review connected tools and revoke access when a project ends or an employee changes roles.
3. Define human review
Specify which outputs can remain internal drafts and which require approval before reaching a customer. Name the reviewer instead of writing a vague requirement that someone should check the answer.
For higher-impact work, establish stronger checks and obtain appropriate professional advice. Do not assume that adding a disclaimer makes an unsuitable decision process safe.
4. Test behavior and keep evidence
Use representative inputs, including missing information, conflicting documents, and requests outside the intended purpose. Check accuracy, source use, refusals, and whether the system stays within its permission boundary.
Document the model or product used, test date, important failures, and changes made. Repeat relevant checks when the product, prompts, data sources, or business rules change.
5. Create an incident route
Make it easy for employees to report an incorrect customer answer, information exposure, or unintended action. Decide who can pause the workflow and who assesses the impact.
Preserve relevant records, limit further harm, and follow applicable notification obligations with appropriate advice. A manual fallback helps the business continue working while the issue is reviewed.
Turn this into a usable one-page policy
A starter policy can name approved tools, allowed purposes, restricted information, review requirements, the business owner, and the reporting contact. Link to the current rules and training examples rather than expecting employees to remember a long document.
Review the policy as actual use changes. Ask staff where it is unclear or makes work difficult; practical feedback helps prevent unofficial workarounds.
Governance should enable useful work
Good controls help a team move from uncertainty to a safe, measurable pilot. They are not a reason to avoid every AI use case, nor a substitute for testing the specific workflow.
Read AI agents for business for permission boundaries and ChatGPT for business for buying questions. Start with the AI Readiness Scorecard, or contact Bellena to scope a governance-aware adoption project.