Future-Proofing
What Belongs in an AI Use Policy (and What Most Businesses Miss)
What Belongs in an AI Use Policy (and What Most Businesses Miss) by Todd Moss
Artificial intelligence is already part of many workplaces, whether leadership has formally introduced it or not. Someone is using it to draft an email, summarize a meeting, clean up a spreadsheet, write code, compare documents, or get unstuck on a project. The question is usually not whether people will use AI. It is whether they will have clear guidance before they use it with information that should stay protected.
That is where an AI use policy matters. It is not meant to turn every employee into a compliance expert or make useful tools feel off-limits. A good policy creates a shared understanding of what is acceptable, what needs extra care, and when someone should pause and ask a question.
For many business leaders, the challenge is finding the middle ground. They do not want to ignore a technology that could save time and improve work. They also do not want sensitive information, inaccurate outputs, or rushed decisions to create a problem that could have been avoided with a few practical rules.
What should an AI use policy include?
An AI use policy should explain which AI tools employees may use, what business information they may enter into those tools, when human review is required, who owns responsibility for the final work, and how the organization will update its approach as tools and risks change. It should be written in plain language and tied to the actual work people do, not treated as a document employees read once and never revisit.
The most useful policies are specific enough to guide decisions but flexible enough to remain relevant as AI changes. Think of it less like a locked door and more like a set of lane markers. People still have room to move, but they know where the edges are.
Why a simple “do not use AI” rule rarely works
A blanket ban can feel like the safest answer when AI tools are changing quickly. In practice, it often drives use out of sight. Employees who believe a tool can help them work faster may still use it through personal accounts or free versions, especially if they do not understand what makes that use risky.
This is sometimes called shadow AI: the use of AI tools that have not been reviewed, approved, or made visible to the organization. It is not always malicious. More often, it comes from people trying to solve a real work problem with the tools available to them. But when nobody knows which tools are in use, it becomes difficult to protect data, train employees properly, or make informed decisions about what should become part of the company’s standard workflow.
A policy gives people a safer alternative to guessing. It says, in effect, “Here is how we can use this responsibly, and here is who can help when the answer is unclear.” That is a stronger starting point than trying to make AI disappear from a workplace where it is already within reach.
Start with the purpose, not the legal language
Before listing rules, explain why the policy exists. Employees are more likely to follow guidance when they understand the problem it is trying to solve. The purpose should be straightforward: support useful and responsible AI adoption while protecting people, confidential information, customer trust, and the organization’s ability to make sound decisions.
This matters because AI policy is not only about security. It is also about quality, fairness, accountability, and reputation. A generated response can sound polished while being wrong. A draft can save time while still requiring the person using it to check facts, tone, and context. A summary can help someone prepare for a meeting, but it may leave out the nuance that changes the decision.
When the purpose is clear, the rest of the policy makes more sense. It becomes a guide for good judgment, not just another rulebook.
Define what “AI” means in your workplace
Many policies fail because they use broad language without defining the tools or activities they cover. Employees may think of AI only as a chatbot, while the organization is also using AI-powered meeting transcription, writing assistance, customer service platforms, analytics tools, image generators, security products, and software features that work quietly in the background.
Your policy does not need to catalog every possible technology. It does need to define AI in a way that makes the policy usable. For example, it can cover tools that generate text, images, audio, code, analysis, predictions, recommendations, summaries, or other outputs from prompts, uploaded materials, or business data.
It should also make clear that the policy applies to both company-provided tools and outside tools used for work. That one distinction closes a common gap. A team may have secure settings in an approved platform, while an employee unknowingly pastes the same information into a personal account elsewhere.
Identify approved tools and how approval works
Employees need a clear answer to a basic question: which tools can I use? “Use AI responsibly” is too vague to guide a busy person on a deadline. A better policy names approved tools or points employees to a maintained list that is easy to find.
The policy should also explain what happens when someone wants to use a new tool. A simple request and review process is usually enough. The goal is not to create a maze of approvals. It is to understand how the tool handles data, whether its terms fit the organization’s needs, what access it requires, and whether an existing approved tool already does the job.
An approved-tool process should account for how quickly software changes. A tool that was appropriate six months ago may add a new feature, change its data settings, or introduce a new integration. Approval is not a one-time stamp. It is an ongoing decision that needs occasional review.
For teams working on broader AI use policy development, this is often where useful governance begins. A policy creates the standard, while the review process makes that standard workable in day-to-day decisions.
Set clear rules for data, not just tools
The most important part of an AI use policy is usually the rule about what information can and cannot be entered into an AI system. An approved tool is not automatically safe for every type of data. The right answer depends on the tool’s settings, contract terms, access controls, and the sensitivity of the information involved.
Employees should not have to interpret legal language to know what belongs in a prompt. Give them practical categories instead. Explain that they should not enter confidential business information, personal information, credentials, financial records, protected health information, legal materials, customer records, unpublished strategic plans, or other restricted content unless the organization has specifically approved that use case and the necessary safeguards are in place.
A useful policy also recognizes that data can become sensitive through context. A list of names may not look especially risky on its own, but paired with performance notes, contact details, donor history, or account information, it can create a very different problem. People need guidance that helps them recognize the full picture.
This is especially important for mission-driven organizations. Cybersecurity for nonprofits is often discussed in terms of phishing, backups, and access controls, but data handling within new tools deserves the same attention. Nonprofits may hold donor details, beneficiary records, grant information, and sensitive internal communications. A well-intended prompt can expose more than the person entering it realizes.
AI can help with the first draft, but people remain responsible for checking the final work.
Require human review for meaningful work
AI can create a first draft, but it cannot accept responsibility for the result. This should be one of the clearest principles in the policy: a person remains accountable for any work they submit, send, publish, approve, or use to make a decision.
That means employees should review AI-generated content for accuracy, relevance, bias, confidentiality, and fit with the situation. They should verify claims, check calculations, confirm sources when research is involved, and make sure the output does not misrepresent a person, client, employee, or organization.
The level of review should match the stakes. A draft of internal meeting notes may need a quick check for omissions and errors. A customer-facing explanation, legal communication, financial analysis, hiring recommendation, or grant-related document needs a more careful review. The policy should call this out clearly so people do not treat all AI output as equal.
It is also helpful to state that AI should not make final decisions about people. Decisions involving hiring, termination, compensation, credit, eligibility, access to services, discipline, or similar high-impact matters require human judgment and appropriate oversight. AI can sometimes support research or organization, but it should not replace accountable decision-making.
Address accuracy, bias, and invented information
One of the easiest mistakes is assuming that a confident answer is a correct one. AI tools are designed to produce useful language, but they can misunderstand prompts, miss context, reflect bias in their training data, or present made-up information as though it were established fact.
An AI use policy should name this risk without making it sound mysterious. Employees need to know that generated output is a starting point, not proof. If the output includes statistics, sources, legal guidance, technical claims, or information about a person or organization, it should be checked against reliable sources before use.
Bias deserves a direct mention as well. AI can reproduce patterns and assumptions found in the information it was trained on. This may show up in subtle ways, such as stereotypes in a job description, uneven language in a customer response, or flawed recommendations based on incomplete data. Human review is not only about catching typos. It is about checking whether the output is fair, appropriate, and grounded in the real situation.
This is where leaders can model a healthy approach. The goal is not to treat AI as either magic or dangerous by default. It is a tool with strengths and limitations. People should be encouraged to use it thoughtfully, question its output, and bring in another person when the result could materially affect someone else.
Clarify ownership, intellectual property, and attribution
AI raises questions about who owns the material that goes into a tool and what rights may apply to the output. Your policy does not need to answer every legal question, but it should tell employees not to upload material they do not have permission to use.
That includes copyrighted content, client-owned material, licensed data, proprietary code, and third-party documents. Employees should understand that “available online” does not necessarily mean “available for any use.” If they are unsure whether content can be entered into an AI tool or reused in a deliverable, they should ask before proceeding.
The policy should also address internal work product. For example, it can state that business materials, prompts created as part of someone’s job, and approved AI-assisted outputs remain company property where applicable. It can clarify when AI assistance should be disclosed, especially for customer-facing work, regulated activities, academic-style research, or situations where transparency is expected.
These rules help prevent a common problem: treating AI output as though it arrives free of context, ownership questions, or responsibility. It does not. The same standards that apply to other business work still apply when AI was part of the process.
Include role-specific guidance where the risks differ
One page of general rules is useful, but it may not be enough for every team. Finance, HR, legal, operations, marketing, customer support, software development, and leadership can all use AI differently. The policy should provide a common baseline, then allow for role-specific instructions where needed.
For example, a marketing team may need guidance on fact-checking, image rights, and brand voice. An HR team may need more restrictive rules around employee data and employment decisions. A development team may need direction on code review, secrets management, licensing, and what repositories or snippets can be entered into AI tools.
This approach helps the policy feel relevant instead of abstract. It also keeps leaders from trying to write one massive document that covers every edge case. The core policy can remain readable, while short supporting guidance addresses the workflows that require more detail.
Make reporting simple and non-punitive
People will make mistakes. They may enter something they later realize should not have been shared, notice a concerning AI output, or discover that a tool is behaving differently than expected. The policy should tell them exactly what to do next.
The reporting path should be clear, quick, and focused on response rather than blame. If employees expect punishment for every mistake, they may stay quiet while a manageable issue becomes harder to address. If they know the organization wants to understand what happened and protect the affected information, they are more likely to speak up early.
A practical policy can ask employees to report suspected data exposure, unapproved tools, unusual AI behavior, inaccurate high-impact output, or concerns about bias to a named internal contact, IT team, security contact, or manager. It should also explain that they should not try to hide or “fix” an incident on their own by deleting evidence or changing records without guidance.
Give employees a way to ask before they act
AI will continue to present situations that are not neatly covered by a policy. That does not mean the policy has failed. It means the organization needs a simple path for questions.
Employees should know who can help them assess a new tool, a new use case, or a question about sensitive data. For smaller organizations, this may be an owner, operations lead, or IT partner. For larger organizations, it may involve IT, security, legal, compliance, HR, or a cross-functional AI governance group.
The key is making the process approachable. People should not feel that asking a question creates a problem. The earlier they ask, the easier it is to find a safe and useful answer.
Connect the policy to training and real workflows
A policy that lives only in an onboarding folder will not guide daily behavior. Employees need short, practical training that shows what the rules look like in ordinary work. This is especially helpful because AI use can feel informal. Someone may not recognize that asking a tool to rewrite a customer email or summarize notes from a meeting is still a business use case with responsibilities attached.
Training can use familiar examples: drafting an internal announcement, analyzing a spreadsheet, creating a project plan, preparing a customer response, or summarizing research. Each example can show what is acceptable, what information should be removed or anonymized, what needs approval, and what a good review looks like.
The most effective training is repeated lightly over time rather than delivered once as a long presentation. A short reminder when a new tool is introduced, a few examples in team meetings, and an accessible place to ask questions will often do more than a complicated annual session.
A clear rollout can include:
A plain-language policy employees can actually read.
A current list of approved tools and approved use cases.
Brief training tailored to the departments most likely to use AI.
A simple channel for tool requests, questions, and incident reporting.
Periodic review as tools, regulations, and business needs change.
Do not forget vendors, integrations, and access controls
An AI tool rarely stands alone. It may connect to email, file storage, customer relationship management systems, project platforms, calendars, or internal databases. These integrations can be useful, but they can also expand the amount of information a tool can access.
Your policy should not attempt to replace technical security standards, but it should set the expectation that AI integrations require review before they are connected to company systems. The review should consider what information the tool can see, who can grant access, whether access can be limited, how data is retained, and how the connection can be removed if needed.
Access should also follow the same basic principles used elsewhere in the organization. People should receive only the level of access they need for their work. Accounts should be protected with strong authentication. Shared logins should be avoided. When someone leaves or changes roles, access should be reviewed and removed when appropriate.
This connects naturally with Zero Trust onboarding. The principle is simple: do not assume a person, device, or new application should receive broad access just because it is inside the organization’s usual environment. Verify the need, grant the minimum appropriate access, and review it as circumstances change.
Before connecting AI tools to company systems, review what they can access, what data they retain, and who can approve permissions.
Plan for records, retention, and auditability
Many organizations overlook what happens to prompts, inputs, and outputs after an AI task is complete. Depending on the tool and settings, conversations may be stored, used to improve a service, retained for a set period, or available to administrators. Employees need to understand that an AI prompt is not always private simply because it is typed into a browser window.
The policy should point to the organization’s existing recordkeeping and retention rules. If AI-generated work becomes part of a client file, business decision, project record, or official communication, it may need to be saved and managed like other work product. If it contains sensitive material, it may require additional safeguards.
This is also useful for accountability. When an important decision involved AI-assisted analysis, leaders should be able to understand what information was used, what human review occurred, and how the final decision was made. The purpose is not to document every low-risk prompt. It is to preserve enough context when the work has meaningful consequences.
Build in review dates and policy ownership
An AI use policy should name the person or group responsible for maintaining it. Without ownership, a document can quietly become outdated while the technology around it changes every few months.
Set a regular review schedule, such as every six or twelve months, with additional review when the organization adopts a significant tool, changes how it handles sensitive information, enters a new regulatory environment, or experiences an incident. The policy should also be updated if employees repeatedly ask the same question. Repeated uncertainty is often a sign that the current guidance needs to be clearer.
This is part of future-proofing IT. The goal is not to predict every new AI capability. It is to build a repeatable habit of evaluating tools, protecting information, and adjusting guidance before confusion turns into risk.
What most businesses miss in their first AI policy
The biggest gap is often not a missing rule. It is the assumption that publishing a policy solves the problem. A useful policy needs practical support around it: approved tools, clear data rules, a human review standard, training, access controls, and an easy way to ask questions.
Another common mistake is focusing only on data exposure. Data protection is essential, but AI policy should also cover accuracy, fairness, intellectual property, customer communication, and responsibility for final work. A policy that only says “do not enter confidential information” may prevent one kind of problem while leaving employees unclear about everything else.
Businesses also miss the importance of making the policy proportional. A small organization does not need to copy a 40-page enterprise framework to act responsibly. It needs guidance that matches its tools, data, people, and actual workflows. A simple, well-used policy is more valuable than a complicated one that nobody can apply.
A practical way to get started
An AI use policy is not a statement that your organization distrusts its employees or fears new technology. It is a practical agreement about how to use a powerful tool with care. It gives employees permission to explore useful ideas while making the boundaries clear enough to protect people, information, and the organization’s judgment.
The best policies are readable, specific, and connected to real work. They name approved tools, protect sensitive information, require human review, clarify responsibility, and leave room for questions. Most importantly, they are kept alive through training, review, and conversations rather than left untouched in a folder.
About 24hourtek
24hourtek, Inc is a forward thinking managed service provider that offers ongoing IT support and strategic guidance to businesses. We meet with our clients at least once a month to review strategy, security posture, and provide guidance on future-proofing your IT.