An employee AI use policy should make safe choices straightforward. Employees need to know which tools the business has approved, what information they may enter, and who must check AI-generated work before it is used. Without clear rules, staff may rely on personal accounts, share information with an unreviewed tool, or treat a plausible-sounding answer as verified.
A useful policy is more than a list of forbidden activities. It connects tool approval, data handling, human oversight, and incident reporting. The goal is to let employees use AI for appropriate tasks while protecting customer, employee, and business information.
Start with a clear scope and purpose
Explain which employees, contractors, business systems, and types of AI use the policy covers. Define generative AI in plain language, and state that the rules apply whether a tool is accessed through a website, an application, or a feature embedded in another product.
Set the basic expectation: employees may use only tools and use cases the business has approved. AI output is not automatically accurate, appropriate, or authorized for external use. Employees remain responsible for following the policy and raising questions when a situation is unclear.
Describe acceptable purposes in terms of tasks, not broad promises. For example, a business might approve drafting general internal outlines or summarizing public information, while requiring additional review before using AI for work involving confidential records or consequential decisions.
How to decide which AI tools employees can use
Keep an approved-tool list that identifies the tool, permitted business uses, any restrictions, and the person or role responsible for approval. Review a tool before adoption, and revisit it when its features, terms, or business use change. A familiar brand name or paid subscription alone does not answer whether a tool is suitable for company work.
During review, ask the provider or check its documentation for clear answers to questions such as:
- Data retention: What prompts, files, and outputs are stored, and for how long?
- Model training: Can information submitted by employees be used to train or improve a model? Can that use be controlled?
- Deletion: Can the business request deletion, and what does deletion cover?
- Access controls: Can the business manage accounts, permissions, and access when a worker changes roles or leaves?
- Incident communication: What does the provider commit to do if company information is exposed or a security incident affects the service?
- Contract terms: Do the written terms address confidentiality, data handling, security responsibilities, and changes to the service?
Record the decision and the conditions attached to it. If the answers are incomplete or unclear, do not treat the tool as approved for sensitive company work. The NIST Generative AI Profile provides a risk-management reference that businesses can use when considering how to govern AI use.
Classify information before employees enter it
Employees need examples, not just a warning to “be careful.” A simple classification system can show what may be used with approved tools and what must stay out unless a specific review authorizes it.
- Public: Information already approved for public release, such as published marketing copy. It may be used only in ways allowed by the tool’s approved use rules.
- Internal: Routine work material not intended for public release, such as internal process notes. Use only with a tool approved for that data type and purpose.
- Confidential: Customer or client records, employee information, nonpublic financial details, contracts, and business plans. Do not enter these unless the business has specifically approved the tool, data handling, and use case.
- Restricted: Passwords, access tokens, security details, trade secrets, protected health information, privileged legal material, and other regulated or highly sensitive information. Keep it out of AI tools unless designated compliance or professional reviewers explicitly approve a tightly defined use.
Be cautious with information that has been edited or stripped of names. Removing obvious identifiers does not automatically make a record safe to share; details can still identify a person or reveal confidential context. If employees cannot tell whether information is sensitive, the policy should direct them to pause and ask an assigned manager or security contact.
For a closer look at the risks of entering sensitive information into public tools, see what employees should consider before pasting information into AI. Businesses can also connect these rules with their broader data protection practices.
Set industry-specific boundaries
An approved tool does not make every use of information appropriate. Healthcare, legal, financial, and other regulated work can involve duties under applicable rules, contracts, or professional obligations. The policy should require the relevant compliance lead or qualified professional to review those uses rather than suggesting one general permission covers every situation.
For example, employees should not put protected health information into an AI tool unless the organization has confirmed that the tool and intended use are appropriate for that information. Lawyers should protect client confidentiality and have professional obligations reviewed before using AI with client material. The Florida Bar’s Opinion 24-1 discusses lawyers’ professional responsibilities related to generative AI. A general workplace policy is not a substitute for industry-specific review.
Define when human review is required
Require a qualified person to verify AI output before it is relied on, acted upon, or shared externally. Review should match the risk of the task. A draft of general internal language may need a different check than content that affects a customer, employee, patient, legal matter, or financial decision.
Tell reviewers what to verify: factual accuracy, missing context, confidentiality, tone, and whether the output follows business procedures. For consequential decisions, AI should not be the sole decision-maker. Assign the review to someone with the knowledge and authority to make the final call, and make clear who owns that decision.
For practical guidance on assigning this responsibility, see who should review AI-generated work.
Give employees a simple incident response path
Employees should know what to do if they accidentally enter restricted information, use an unapproved tool, or notice an unexpected disclosure. Tell them to stop using the tool for that information, avoid trying to conceal or independently resolve the incident, and promptly notify the designated manager or security contact.
The policy should ask employees to share the details needed to assess the event, such as what information was entered, which tool was involved, and when it happened. The business can then follow its established security and compliance processes to decide what steps are appropriate. Make clear that prompt reporting is expected, including when the employee is unsure whether an exposure occurred.
Make the policy usable in daily work
Assign ownership for maintaining the approved-tool list, reviewing changes, answering employee questions, and updating the policy as business needs evolve. Provide a clear route for requesting a new tool or use case instead of leaving employees to make their own exceptions.
Keep the core rules easy to find: use approved tools only, follow the data classification rules, obtain the required human review, and report mistakes promptly. Explain the reasons behind the rules so employees can apply them when a scenario is not listed word for word.
A clear AI policy supports practical adoption without treating every tool or task as equally safe. If your business wants to align AI rules with its broader technology planning, cybersecurity, and managed IT practices, contact Zevonix to discuss your needs.