Writing an Employee AI Use Policy: George Dagliyan Research Guide

What should an employee AI use policy actually decide?

George Dagliyan’s research themes on AI adoption and technology acceptance fit a very practical question: what should employees be allowed to do with AI, and where should the line be drawn? This article’s answer is simple: a usable employee AI use policy should separate low-risk public content from higher-risk confidential content, define approval paths, require disclosure when AI materially shapes work, and create an escalation route for edge cases.

A policy that only says “use AI responsibly” is too vague to guide daily behavior. A better policy gives employees permission for routine tasks, blocks unsafe inputs, and tells managers what to do when a request falls outside the normal rule set. The goal is not to eliminate judgment; it is to make judgment repeatable and reviewable.

What uses should be allowed by default?

The safest starting point is to allow AI for tasks that use public, non-sensitive information and do not make decisions on their own. Typical allowed uses can include brainstorming, outlining, summarizing public articles, editing tone, translating public-facing text, drafting meeting agendas from non-confidential notes, and creating first-pass test cases or code comments from generic examples.

A policy can state that employees may use approved AI tools for low-risk assistance when they remain responsible for the final output. The employee should verify accuracy, remove anything that sounds like a factual claim unless it is checked, and ensure the tool is not being asked to perform a decision reserved for a person. This keeps AI in the role of helper, not authority.

Which inputs should be prohibited without exception?

A practical acceptable-use policy should prohibit entering confidential, regulated, or private information into a public or unapproved AI tool unless a formal exception has been granted. That category usually includes customer records, personal data, payroll details, nonpublic financial data, legal strategy, source code with security-sensitive sections, internal incident reports, credentials, and proprietary business plans. It also includes any material covered by confidentiality obligations that the organization has already defined elsewhere.

The rule should be written plainly: if the content would not be sent to an unknown external vendor, it should not be pasted into a general AI chat tool. Public content is different; for example, a marketing team may ask AI to rewrite a public website paragraph, but it may not feed in a draft acquisition memo or a spreadsheet containing customer names. This is a content-based gate, not a tool-based guess.

How should approvals and exceptions work?

An acceptable-use policy becomes usable when it states who can approve higher-risk use. A simple structure is three tiers: routine use needs no special approval, internal confidential use requires manager or data-owner approval plus tool approval, and high-risk use requires privacy, security, or legal review depending on the content. The policy should also say that approval is task-specific and does not create a blanket permission for all AI use.

A clear exception process reduces shadow AI use, where employees improvise because the policy feels impossible to follow. The exception request should capture the task, the data type, the tool, the reason the normal rule does not fit, the controls in place, and an expiration date. NIST’s AI Risk Management Framework is voluntary and emphasizes managing AI risk through governance, mapping, measuring, and managing; this article uses that spirit as editorial guidance, not as an official approval template.

What disclosure rule should employees follow?

Disclosure should answer two different questions: whether the user needs to reveal AI assistance internally, and whether a customer or outside audience should be told. Internally, a good rule is to disclose when AI materially drafts, rewrites, classifies, summarizes, or recommends content that another person would reasonably assume was written or checked by a human alone. Externally, the policy should require disclosure whenever a business process, client deliverable, or public statement uses AI in a way that the organization has decided is material to trust or accuracy.

A policy can require a simple label in the workflow rather than a public announcement every time AI is used. For example, a report might carry an internal note saying “AI-assisted draft reviewed by employee,” while a public customer email may require human-authored final text if it references facts, commitments, or pricing. The point is transparency proportional to risk, not symbolic disclosure.

How should monitoring and privacy be handled?

Any AI use policy should acknowledge that monitoring may occur, because organizations often need logs to investigate misuse, data leakage, or security events. At the same time, employees should be told what is monitored, who can review it, and why monitoring exists. This matters because privacy expectations shape whether people adopt the policy honestly or route around it.

NIST’s Privacy Framework is a voluntary tool intended to help organizations identify and manage privacy risk while building innovative products and services. Using that idea in policy design means collecting only the logs needed for governance, limiting access to those logs, and avoiding vague promises that “nothing is ever recorded.” A clear policy is more trustworthy than a hidden one, especially when AI tools may retain prompts or generate audit trails.

What does a usable policy worksheet look like?

The policy itself can be short if it is paired with a one-page worksheet for employees and managers. This article’s proposed worksheet is not an official standard; it is a practical decision aid. It asks five questions: What is the task? Is the input public, internal, confidential, or regulated? Which tool will be used? Who reviews the output? Is disclosure or approval needed before use?

A simple decision rule can sit at the bottom of the worksheet: if the input is public and the output is low consequence, use is allowed with normal review; if the input is internal but not sensitive, use is allowed only with employee verification and no copying of restricted data; if the input is confidential, personal, or regulated, stop and route for approval; if the output will make or support a business decision, human review is mandatory before use. The rule is easy to remember and easy to audit.

How does this work in practice?

Hypothetical scenario, for illustration only: a marketing coordinator wants AI to improve a public blog draft about an upcoming webinar. The draft contains no client names, no private numbers, and no internal strategy. Under the worksheet, this is allowed because the content is public-facing, the task is editing rather than decision-making, and the employee remains responsible for checking facts and tone before publication.

Hypothetical scenario, for illustration only: an operations manager wants AI to summarize a spreadsheet of employee attendance and notes. Even if the summary feels routine, the spreadsheet contains personal and sensitive information. Under the same policy, that request is blocked until an approved environment and the right review path are in place. This contrast shows why a policy should be content-aware, not merely tool-aware.

Frequently Asked Questions

What is the core difference between allowed and prohibited AI use in an employee policy?

The main difference is the sensitivity of the input and the consequence of the output. Public information used for drafting, summarizing, or brainstorming is often suitable for ordinary AI assistance, while confidential, personal, regulated, or security-sensitive information should be blocked or routed through approval. A useful policy also asks whether a human will verify the result before it affects a customer, employee, or business decision.

Should a company ban all employee AI use to stay safe?

A total ban is usually too blunt to be practical because it can push people into unofficial use and reduce adoption of legitimate productivity tools. A better policy sets boundaries, gives examples, and requires escalation for higher-risk tasks. The aim is controlled use, not zero use. That approach is more consistent with enterprise risk management than a one-size-fits-all prohibition.

How can a policy distinguish public content from confidential content?

A simple rule is to ask whether the material could be shared outside the organization without harm or breach of obligation. Public content includes website copy, press releases, and publicly available research summaries. Confidential content includes internal memos, customer records, pricing, source code, security details, and any information already subject to confidentiality commitments. When in doubt, treat the content as sensitive and escalate.

What should employees disclose when they use AI?

Employees should disclose AI use whenever the tool materially drafts, rewrites, summarizes, classifies, or recommends content that a reviewer, customer, or regulator would reasonably expect to be human-authored or manually checked. Internal workflows may use a short label or note, while external disclosures should depend on business risk and the organization’s communication rules. The policy should define disclosure in plain language, not leave it to guesswork.

How should monitoring and privacy be explained without overpromising?

The policy should say what is monitored, who can see the logs, and why the logs exist. It should not promise that nothing is recorded if the organization needs audit trails for security or compliance purposes. At the same time, the policy should limit monitoring to what is necessary and describe access controls. That balance supports accountability while respecting privacy concerns.

Sources and further reading

• NIST — AI Risk Management Framework
• NIST — Privacy Framework