An AI policy fails when employees need a lawyer to understand it. The useful policy answers everyday questions: Which tools can I use? What information can I put into them? What must a person review? Who owns the result if the system is wrong?
Be specific about tools and data
Identify approved tools and explain what customer, employee, financial, confidential, regulated, credential, and proprietary information may not be submitted without approval.
Define human review
Require review where errors could matter, including external communications, consequential decisions, security changes, regulated work, or material presented as fact.
Give every use case an owner
Someone should be accountable for the purpose, data, permissions, review process, and decision to stop a workflow. NIST’s AI RMF provides a useful structure through Govern, Map, Measure, and Manage.
Write rules employees can use in an ordinary workday
A policy is easier to follow when it describes concrete tasks. A receptionist may want to shorten a customer email. A sales assistant may want help summarizing meeting notes. A manager may want a draft procedure. Each task raises a different question about confidential data, accuracy and authority. Give staff examples of acceptable use, prohibited use and use that requires approval.
For instance, an employee might be allowed to improve the wording of a generic appointment reminder without adding customer details. Uploading a spreadsheet containing names, phone numbers and service histories to an unapproved tool is a different matter. A policy that says only “use AI responsibly” does not explain the distinction.
Define which data may be shared
List the organization’s main data categories: public information, internal operational material, customer and employee details, financial records, credentials and regulated or confidential documents. State which categories may enter approved tools, under what conditions, and who can authorize exceptions. Removing a name does not necessarily anonymize a document if addresses, unique facts or account references remain.
Ask vendors what information they retain, who can access it, whether prompts or outputs can be used for product improvement, and how data can be deleted or exported. Do not assume every tool or pricing tier offers the same terms. Where the answer is uncertain, keep the information out until it has been reviewed.
Keep review proportional to the consequences
Grammar assistance for an internal announcement needs a different review from a draft legal notice, a financial recommendation, a security configuration change or a decision that could affect employment. Make the human reviewer responsible for verifying names, dates, figures, sources and conclusions. A confident response is not evidence that the facts are correct.
Where a tool suggests an action in another system, separate preparation from execution. A draft customer response can be reviewed before sending; a proposed change to access permissions should be checked against an authorization record before it is applied. High-impact decisions should remain with appropriately qualified people.
Create a simple approval record
For each business use case, record its purpose, owner, approved tool, data categories, users, expected output, review steps, supplier, known limitations and a person authorized to stop it. This record does not have to become a large governance project. One page per use case is often enough to make responsibility visible.
Include a route for reporting problems. Staff should know how to flag inaccurate content, accidental disclosure, inappropriate outputs and unexpected automation behavior without first proving that harm occurred. Record what happened, protect any sensitive evidence and decide whether the use case should be paused while it is investigated.
Use a short policy statement employees can test
A workable internal rule might say: “Use only approved AI tools for approved purposes. Do not enter restricted information without authorization. Check factual claims before relying on them. A named employee remains responsible for anything sent or decided. Report mistakes promptly.” The organization should then add the tool list, data classifications and escalation contacts that make those words actionable.
Review the policy when a vendor changes its data terms, a tool gains access to new systems, or staff begin using it for more consequential work. NIST’s AI Risk Management Framework describes a broader approach through Govern, Map, Measure and Manage, but a small company’s first obligation is to make the rules intelligible and enforceable.