TL;DR
A generative AI policy is the rulebook that tells employees which AI tools they may use, what data they may share, and who checks the output. To write one, name an owner and find out how staff already use AI. Then sort every use into allowed, restricted, or prohibited. Tie the data rules to the classification labels your company already has. Require a named human reviewer before AI output reaches a customer, a court, or a decision about a person. Finally, train people, collect signed acknowledgments, and review the policy on a fixed schedule.
Key Takeaways A generative AI policy answers four everyday questions about tools, data, review, and approvals. Find out how people already use AI before you write a single clause. Approved tools with clear data limits work better than blanket bans. Data rules should map your existing data classes to specific tool tiers. Every restricted use needs a named approver, a request form, and a response time. Test the draft against real employee scenarios, then train, sign, and review it on a schedule. Watch on YouTube
How KANGuard Secures Your Data | Prevent Leaks & Unauthorized Access with DLP Policies
A short Kanerika explainer on how KANGuard works with Microsoft Purview to enforce DLP policies, apply labels and classify data across Microsoft 365. It shows the enforcement layer that sits behind the data rules in any GenAI usage policy.
The Customer File in the Chat Window Take a customer success manager on a Thursday afternoon. Her renewal call starts in twenty minutes, and the account history sits in a 40-page export from the CRM. She pastes the file into a free chatbot on her personal account and asks for a summary of open complaints.
The summary is good. She now also has customer names, contract values, and support notes sitting with a vendor her company has never assessed. Nothing told her not to, because her employer’s only guidance was one line in the code of conduct asking staff to use AI responsibly.
A clear generative AI policy would have given her a two-second answer and an approved tool that does the same job safely.
What Is a Generative AI Policy? It is an employee-facing document that sets the rules for tools that write, summarize, code, or create images. It answers ordinary questions in plain words.
Which tools can I use, and what can I paste in? Do I have to say AI helped, and who do I ask when I am unsure?
Companies publish it as an acceptable use policy for generative AI, a standalone intranet page, or a section of the employee handbook. Still, the format matters less than the reach, which should cover employees, contractors, and anyone else who handles company data on the company’s behalf.
How It Differs From a Governance Framework and an IT Acceptable Use Policy Three documents get mixed up here, and keeping them apart makes each one shorter. An AI governance framework describes how the company decides on, oversees, and audits AI across its lifecycle. Kanerika’s guide to building an AI governance framework covers that operating model in depth, while a responsible AI program supplies the principles behind it.
The usage policy sits one level down. It then turns those decisions into instructions a sales rep or a developer can follow on an ordinary Tuesday. Your IT acceptable use policy still applies, but it rarely says anything about prompts, model training, or AI output.
Table 1: Three documents that get confused, and what each one answers
Document Who reads it What it answers AI governance framework Leadership, risk, and audit teams Who decides on AI, and how systems are approved and monitored Generative AI usage policy Every employee and contractor Which tools, what data, what review, and who to ask IT acceptable use policy Every employee How company devices, networks, and accounts may be used AI security controls IT and security teams How the rules are enforced in systems
Security controls such as data loss prevention enforce the policy, but they are not the policy. The generative AI security guide covers those controls layer by layer. This article stays with the words employees actually read.
Why “Use AI Responsibly” Is Not a Policy Employees are not waiting for permission.
Microsoft and LinkedIn’s 2024 Work Trend Index found that 75% of knowledge workers use generative AI at work. Of those users, 78% also bring their own tools. That pattern is known as shadow AI , and it grows fastest where the rules are vague.
The data side tells the same story. For example, in Cisco’s 2024 Data Privacy Benchmark Study , 48% of privacy and security professionals admitted entering non-public company information into GenAI tools. The same study found that 27% of organizations had banned GenAI, at least temporarily.
Bans are a blunt fix, though. In May 2023, Samsung told staff to stop using tools like ChatGPT for work after an employee reportedly uploaded confidential source code, as Fortune reported . The company later described the restriction as temporary.
A well-written policy gets to the useful middle faster, because it says yes to most work and no to a short list of dangerous moves. The wider catalogue of generative AI risks explains what those dangerous moves protect against.
What Should a Generative AI Policy Include? A complete policy answers twelve questions. Each clause below maps to one of them, and each has a natural owner who keeps it current. When a clause has no owner, it tends to go out of date within a quarter.
Table 2: The twelve clauses of a complete generative AI policy
Clause Question it answers Typical owner 1. Purpose and effective date Why does this policy exist, and since when? Policy owner 2. Scope and definitions Who and which tools does it cover? Policy owner, Legal 3. Approved tools and accounts Which tools may I use, and on which account? IT 4. Allowed, restricted, and prohibited uses What can I do without asking? Business leads, Legal 5. Data rules What may I paste, upload, or connect? Privacy, Security 6. Human review Who checks output before it is used? Business leads 7. Disclosure When must I say AI helped? Legal, Communications 8. Intellectual property Who owns AI output, and what must I avoid copying? Legal 9. Vendor terms What must a tool’s terms guarantee? Procurement, Legal 10. Approvals and exceptions How do I ask for something new? Policy owner 11. Training and acknowledgment What must I learn and sign? HR 12. Enforcement and review cadence What happens if I break it, and when does it change? HR, Policy owner
The table also doubles as a gap check for a draft you already have. Read each question aloud, then see whether your document answers it in one or two sentences. If the honest answer is “it depends,” that clause needs an example.
Checklist
Generative AI Checklist for Secure AI Adoption and Governance
Use Kanerika’s checklist to confirm your data, tools, and governance are covered before the policy goes out for sign-off.
Get the Checklist → How to Write a Generative AI Policy in 8 Steps Drafting goes faster when the order is right. So decide who owns the work, and learn what people already do, before anyone writes a clause. Each step below produces something the next one needs.
Step 1: Name an Owner and a Small Drafting Group Give the policy one accountable owner, often the CIO’s office, the chief data officer, or the general counsel. Pair that owner with reviewers from legal, privacy, security, and HR, plus one or two heavy AI users from the business. Keep the group small, because large committees tend to produce long documents that nobody reads.
The heavy users matter more than they look, since they know which tasks people actually hand to AI. They will also spot rules that block real work before launch.
Step 2: Find Out How People Already Use AI Run a short anonymous survey that asks which tools people use, for which tasks, and on which accounts. Pair it with what IT can already see, such as traffic to AI sites and AI features switched on inside existing software. Keep the goal in view, because you want a list of real use cases rather than a disciplinary file.
That list then becomes the test set for every later step. If the draft cannot give a clear answer for a use case on the list, it is not finished.
Step 3: Decide Your Default Posture Every policy leans one of three ways. Specifically, it can ban by default and approve exceptions, allow by default and list prohibitions, or allow approved tools with firm data limits. The third posture is usually the workable one, because a ban pushes work onto personal accounts where nobody can see it.
Cisco’s study also points that way, with 63% of organizations limiting what data can be entered and 61% limiting which tools employees can use. Whichever you choose, write your posture down in one sentence at the top of the policy so every later clause follows from it.
Step 4: Set the Scope Scope covers people, tools, and outputs. First, people means employees, contractors, interns, and vendors working on company data. Second, tools means public chatbots, enterprise assistants, coding assistants, internal apps built on models, and AI features embedded in software you already pay for.
Finally, outputs means text, code, images, audio, video, and any action an AI agent takes on someone’s behalf. That last item is new for many policies, and it changes fastest. Kanerika’s guide to the enterprise LLM explains how deployment choices change what an internal tool can see and do.
Step 5: Draft the Clauses in Plain Language Write every clause as an instruction with an example. A clause such as “Do not enter customer personal data into any unapproved AI tool ” works. Compare that with “employees must exercise appropriate care with sensitive information,” which tells nobody what to do.
Keep sentences short, and then use an ordinary word wherever a legal term is not required. The same plain style that makes prompt engineering guidance easy to follow works for policy text too.
Aim for a core policy of two to four pages. Then put the approved tools list and the data matrix in appendices, so they can change without a full re-approval.
Step 6: Test the Draft Against Real Scenarios Take the use cases from Step 2 and run each one through the draft, as if you were the employee asking. Two reviewers should then reach the same answer within a minute. When they disagree, the wording is the problem rather than the reviewers.
The scenario table later in this guide gives five tests to start with. Also add your own from the survey, especially anything involving customers, hiring, or code.
Step 7: Get Sign-Off From Legal, Privacy, Security, and HR Each reviewer checks something different, since each owns a different risk. For example, legal checks contracts, IP, and disclosure duties, while privacy checks the data rules against your privacy notices. Security checks the approved tools list, while HR checks that enforcement matches existing discipline policies.
Also record who approved which version and when. That revision history is often the first thing an auditor or a customer’s security questionnaire asks to see.
Step 8: Publish, Train, and Collect Acknowledgments Once the draft is signed, publish it where people already look, such as the intranet, the handbook, and the onboarding checklist. At the same time, pair the launch with short training and a signed acknowledgment. Then announce the approved tools as loudly as the restrictions, so that people know where the yes lives.
How to Sort Uses Into Allowed, Restricted, and Prohibited The three-tier sort is the part of the policy employees consult most. Allowed uses need no extra approval. By contrast, restricted uses need written approval or a specific tool, while prohibited uses stay off the table whoever asks.
Sample Rules by Activity Describe each tier with workplace examples instead of abstract categories, because examples stick. People remember “summarizing a public analyst report” far better than “low-risk informational processing.”
Table 3: Sample usage rules by activity (adapt to your contracts and data classes)
Activity Tier Condition Drafting an internal email or outline Allowed Approved tool, sender reviews before sending Summarizing a public report or web page Allowed Verify any fact that will be quoted Brainstorming campaign ideas Allowed No customer data in the prompt Summarizing a customer contract Restricted Enterprise tool approved for confidential data Generating production code Restricted Approved coding assistant, code review, tests, and a license check Recording and transcribing meetings with an AI assistant Restricted Participant notice, approved tool, retention period set Publishing AI-generated images or copy externally Restricted Brand and legal review, disclosure where required Entering trade secrets or credentials into an unapproved tool Prohibited No exceptions Letting AI make a final hiring, firing, or credit decision Prohibited A named person decides and records why Creating content that impersonates a real person Prohibited No exceptions
Treat the table as a starting sample rather than a standard. After all, your contracts, industry rules, and data classes decide where each row lands. For example, a bank and a marketing agency will sort “summarize a client document” very differently.
Why Hiring Gets Its Own Line Hiring deserves extra care in the US, for a simple reason. New York City’s Local Law 144 bars employers from using an automated employment decision tool without a bias audit in the past year. Candidates must also receive notice before it is used.
A policy that keeps a named person accountable for every hiring decision avoids drifting into that territory by accident. It also answers the fairness questions raised in our look at AI ethical concerns .
Data Rules: What Employees May Put Into an AI Tool Data rules are where drafts are vaguest, even though the stakes are highest there. “Protect sensitive data,” for instance, tells nobody anything. Instead, a useful clause names the data classes, names the tools, and states which combinations are allowed.
Map Your Data Classes to Tool Tiers Start from the classification scheme you already use, such as public, internal, confidential, and restricted. If you have none, start with the data classification best practices guide. A GenAI data clause is only as strong as the labels under it, so this work comes first.
Once the classes exist, map each one to the tools allowed to process it. The table below shows a common starting point.
Table 4: Mapping data classes to tool tiers
Data class Examples Public or consumer AI tools Approved enterprise AI tools Public Press releases, published documentation Allowed Allowed Internal Process documents, internal memos Not allowed Allowed Confidential Customer contracts, financials, source code Not allowed Allowed only in tools approved for this class Restricted Personal data, health data, credentials, trade secrets Not allowed Only with written approval in a named, approved environment
Name the restricted data types explicitly. Customer personal data, employee records, and health information should each appear by name.
So should payment data, passwords and API keys, unreleased financials, and licensed source code. State privacy laws, covered in our GDPR and CCPA compliance guide, make that list non-negotiable for many US firms. Our AI privacy guide also adds the model-specific angle.
Cover Files, Connectors, and Outputs, Not Just Prompts Employees think of a prompt as the thing they type. But the data rule has to cover uploads, screenshots, and meeting recordings. It also has to cover browser extensions that read the page and connectors that reach email, drives, or code repositories.
Outputs need a rule too. For example, an AI summary of a confidential contract is still confidential, so it inherits the source’s label and storage rules.
Tools such as Microsoft Purview Information Protection can carry labels into AI workflows, but the policy is what tells people the label matters. Data loss prevention rules then catch what slips through.
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Watch the Webinar → Set a Default for Unlabeled Data Much of what people paste carries no label at all, so give them one default. If you do not know the class, treat the data as confidential and use only an approved enterprise tool, or ask the data owner first.
That single sentence also prevents the most common mistake, which is assuming anything unlabeled must be harmless. It also gives managers a simple line to repeat when teams meet.
Approved Tools and Vendor Terms The approved tools clause can stay short, since the list itself lives in an appendix or an intranet page that IT updates. In practice, the clause says three things. Use only approved tools for company work, use them through company accounts, and ask before trying anything new.
Company accounts matter as much as the tool. That is because enterprise and consumer versions of one assistant often carry different terms on training and retention. Microsoft, for example, states on its Copilot privacy documentation that prompts, responses, and data accessed through Microsoft Graph are not used to train foundation models.
The same page says Copilot only surfaces data a user already has permission to view. That cuts both ways, though.
Loose file-sharing permissions can let an assistant summarize documents people should never have seen. So tighten those permissions before rollout.
Kanerika’s Microsoft 365 Copilot guide covers the pre-implementation planning, including user permissions and access. For restricted data, some firms go further and run private LLMs inside their own environment.
What to Check Before a Tool Joins the List Procurement and legal should run every candidate tool through the same short checklist.
Training. Does the vendor use your prompts or files to train its models, and is that off by default? Retention. How long are prompts and outputs kept, and can admins shorten that period? Admin control. Can IT manage users, see usage, and switch features off? Data location. Where is data processed and stored, and which subprocessors touch it? Output rights. Who owns the output, and does the vendor defend you against IP claims? Read the current terms every time, because vendors revise them often. Routing approved traffic through an LLM gateway also gives IT one place to log and switch off access. Contract clauses for AI vendors are a governance topic of their own, and the AI governance best practices guide covers what to negotiate.
Human Review, Disclosure, and Intellectual Property Generative AI produces fluent text that can still be wrong. In fact, Microsoft’s own Copilot documentation says AI responses aren’t guaranteed to be 100% factual and asks users to review output before sending it. So your policy should turn that advice into a rule with a named reviewer.
Who Reviews What Scale review to the stakes. For instance, a first draft of an internal note needs a careful read by the person sending it. Anything reaching a customer, a regulator, a court, or a decision about a person needs a second reviewer with subject knowledge.
The legal profession learned this in public.
Back in June 2023, in Mata v. Avianca, a federal judge fined two lawyers and their firm $5,000 over a brief citing cases ChatGPT had invented, as LawNext reported . The judge wrote that using AI is not inherently improper, but lawyers carry a gatekeeping role to ensure accuracy.
Customer-facing bots carry the same duty.
In Moffatt v. Air Canada, a Canadian tribunal held the airline liable for wrong fare advice from its website chatbot. As McCarthy Tétrault summarizes , the tribunal said the company is responsible for all the information on its website. Our LLM hallucination explainer covers why these errors happen.
When to Disclose AI Use Disclosure rules work best when they are specific. Require disclosure when customers would reasonably expect a person wrote the content. Also require it when a contract or regulator demands it, and when synthetic images or voices depict people.
Internal drafting help, on the other hand, usually needs no label.
Write the disclosure wording into the policy, so that teams do not invent their own. After all, one short, consistent line is far easier to audit than ten creative variations.
Ownership and Copyright AI output may not be protectable the way human work is.
The US Copyright Office’s report on copyrightability came out in January 2025. It concluded that AI output can be protected only where a human author has determined sufficient expressive elements. A person’s creative arrangement or modification of the output can count, but prompts alone do not meet that bar.
Two clauses follow from that finding. First, employees should keep records of meaningful human edits on work the company needs to own. Second, they should never prompt tools to reproduce named copyrighted works, logos, or a living artist’s style for commercial use.
Approvals, Exceptions, and Incident Reporting Every restricted use needs a route to yes, or else people will route around the policy. So name one intake point, such as a form or a ticket queue, and promise a response time. The right approver depends on the request, but the employee should never have to work that out alone.
Who sits on the approval board is an operating-model question for your governance framework rather than the usage policy. In practice, the policy only has to tell employees where to send a request and what to include. A request template such as the one below keeps every submission complete.
AI USAGE REQUEST
Requester and team [Name, department]
Tool or feature [Product, version, account type]
Business purpose [Task, frequency, expected benefit]
Highest data class [Public | Internal | Confidential | Restricted]
Users [Who will use it, how many people]
Where output goes [Internal | Customers | Public | System of record]
Human review step [Who checks output before use]
Duration [Ongoing | Pilot ending on DATE]
Approver(s) [IT | Privacy | Legal | Business owner]
Decision [Approved | Approved with conditions | Declined]
Exception expiry [DATE, if this is an exception]Exceptions should be written, conditional, and dated, because an exception with no expiry date quietly becomes a second, unofficial policy.
Incident reporting then closes the loop. Tell people exactly how to report a mistake, such as pasting the wrong file, and promise that prompt self-reporting is treated differently from concealment.
Fast reports give security teams time to contain a small slip before it becomes a breach. They also leave the record that an AI auditing framework depends on.
Training, Acknowledgment, and Enforcement A policy nobody understands is a liability with a signature on it. So keep training short and practical, built around the tool list, the data matrix, and a few local scenarios. Developers, recruiters, and marketers also need role-specific examples on top.
Regulation is moving the same way.
In July 2026, the Digital Omnibus on AI , Regulation (EU) 2026/1744, reworded Article 4 of the EU AI Act . It asks AI providers and deployers to take measures supporting AI literacy among their staff. US companies with EU operations or customers can treat policy training as part of that evidence.
After that, collect a signed acknowledgment at launch, at each major revision, and during onboarding. Kanerika’s guide to AI change management covers how to bring skeptical teams along, while broader data literacy work makes the data rules easier to grasp.
Enforcement That People Accept Tie consequences to your existing disciplinary policy rather than inventing new ones. Also separate honest mistakes reported quickly from deliberate misuse. If every slip leads to a warning letter, people stop reporting slips, and the company loses the early warning it needs.
Finally, review the policy on a fixed schedule, such as every six months, and sooner when tools, contracts, or laws change. Put the next review date on the cover page, so that the document cannot quietly go stale.
Watch on YouTube
5 AI Governance Rules Every Enterprise Needs
A one-minute Kanerika short on the governance rules enterprises should set before AI use spreads. It works well as a primer to share with teams ahead of policy training.
Generative AI Policy Template: A Copy-Ready Skeleton The skeleton below compresses the twelve clauses into ten numbered sections and three appendices, so the core stays short. Replace the bracketed placeholders, then delete what does not apply, and keep the core under four pages.
[COMPANY NAME] GENERATIVE AI USAGE POLICY
Version [X.X] | Owner [ROLE] | Effective [DATE] | Next review [DATE]
1) PURPOSE
Why the company allows generative AI and what this policy protects
2) SCOPE AND DEFINITIONS
Covers employees, contractors, and vendors working on company data
Covers public chatbots, enterprise assistants, coding assistants,
internal AI apps, AI features inside existing software, and AI agents
3) APPROVED TOOLS AND ACCOUNTS
Use only tools on [APPROVED AI TOOLS LIST], through company accounts
Personal accounts may not be used for company work
4) ALLOWED, RESTRICTED, AND PROHIBITED USES
Allowed [examples]
Restricted [examples + who approves]
Prohibited [examples, no exceptions]
5) DATA RULES
Follow [DATA CLASSIFICATION POLICY] and the data-to-tool matrix in
Appendix B, covering prompts, uploads, recordings, connectors,
and outputs (unlabeled data counts as Confidential)
6) HUMAN REVIEW, DISCLOSURE, AND IP
A named person reviews output before it reaches customers, regulators,
courts, or decisions about people
Disclose AI use where [RULES] apply
Do not prompt tools to reproduce copyrighted works or real likenesses
7) VENDOR TERMS
New tools pass the vendor checklist in Appendix C before approval
8) REQUESTS, EXCEPTIONS, AND INCIDENTS
Requests go to [INTAKE FORM / QUEUE]
Exceptions are written, conditional, and dated
Report mistakes to [CONTACT] within [TIME]; prompt reports are protected
9) TRAINING, ACKNOWLEDGMENT, AND ENFORCEMENT
Complete [TRAINING] before access
Sign an acknowledgment at launch and at each revision
Breaches follow [DISCIPLINARY POLICY]
10) REVIEW AND REVISION HISTORY
Reviewed every [6] months or when tools, contracts, or laws change
Appendix A Approved tools list (owned by IT, updated monthly)
Appendix B Data-to-tool matrix (owned by Privacy and Security)
Appendix C Vendor terms checklist (owned by Procurement and Legal)Keep the appendices alive, because the tools list and data matrix will change monthly. The policy body, by contrast, should change only at scheduled reviews.
Frameworks to Name in the Policy Appendix A usage policy does not need a legal treatise. Still, naming a few frameworks shows auditors and customers what you built on, and one short appendix is enough.
NIST AI 600-1. The Generative AI Profile of NIST’s AI Risk Management Framework, published July 26, 2024, is a voluntary US reference for generative AI risks and actions. ISO/IEC 42001:2023. The international standard for an AI management system helps if you plan to certify how AI is managed. EU AI Act, Article 4. The AI literacy duty for providers and deployers has applied since February 2, 2025. The Digital Omnibus on AI reworded it in July 2026. NYC Local Law 144. It sets bias audit and notice rules for automated employment decision tools. US Copyright Office, Part 2 report. It explains how the human authorship requirement applies to AI-assisted work. Industry rules such as HIPAA or financial recordkeeping duties sit on top of this list. So ask counsel which ones apply and link them from the data rules rather than restating them. Kanerika’s AI compliance guide maps the wider regulatory picture.
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AI Governance Consulting Services
Kanerika helps enterprises turn AI policies into working controls, from acceptable use policy drafting to technical guardrails and escalation procedures with periodic review cadences.
Explore AI Governance Services Five Scenarios to Pressure-Test Your Policy Run these five cases through your draft before anyone signs it. When two reviewers reach different answers, rewrite the clause until they agree.
Table 5: Five scenarios and what a clear policy answers
Scenario What a clear policy says A sales rep wants a summary of a customer contract before a call Restricted. Use the enterprise assistant approved for confidential data, never a personal account. A manager turns on an AI note-taker for a client meeting Restricted. Tell participants, use the approved tool, and apply the standard retention period. A developer pastes a stack trace into a coding assistant to fix a failing build Allowed in the approved coding tool if the trace holds no secrets. Code review and tests before merge. A recruiter wants AI to rank 300 applicants Prohibited as a final decision. AI may help summarize, and a named person decides and records why. A marketer generates an image of a “customer” for an ad Restricted. Brand and legal review, no likeness of a real person, and disclosure if required.
Notice that each answer names a tool, a condition, and a person, rather than a vague value. If your draft can only answer “be careful,” it needs another pass.
Security teams test the technology in the same way that this table tests the wording. Our guide to LLM red teaming shows how they probe an AI app for the data leaks a usage policy is trying to prevent.
Common Mistakes When Writing a Generative AI Policy The same drafting errors show up across industries, but each one has a simple fix.
Vague verbs. “Use AI responsibly” answers nothing, so replace it with named tools, data classes, and examples. Blanket bans. A ban pushes work onto personal accounts, so allow approved tools with data limits instead. Data rules without examples. “Protect sensitive data” needs a named list of restricted data types. Approval with no address. Requiring permission without naming who grants it stalls every request. Ignoring embedded AI. AI features inside email, CRM, and design tools slip through when only chatbots are in scope. No human owner for output. Every AI-assisted deliverable needs a person who answers for it. A frozen tool list. Keep the list in an appendix with a monthly update and a named maintainer. Most of these errors come from writing the policy alone inside a legal or security team. That is why the drafting group from Step 1 is the cheapest fix for all seven.
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Start Your AI Assessment → How Kanerika Helps Enterprises Put a Generative AI Policy to Work Kanerika treats a usage policy as the front page of a larger system. The words tell people what to do, while the data labels, tool settings, and review steps behind them make the words true.
Our AI governance services include acceptable use policy drafting for AI systems, technical guardrails, and escalation procedures with periodic review cadences. A usage-policy rollout of this kind usually covers five pieces of work.
Assess. Inventory current AI use, data classes, and the AI features already switched on in SaaS products. Design. Draft the policy, the data-to-tool matrix, and the request process with the client’s legal, privacy, and HR leads. Enable. Configure approved tools so the policy is the easy path, with sensitivity labels and access settings on Microsoft Purview . Train. Run role-based sessions built on the client’s own scenarios and collect acknowledgments. Review. Set a cadence for the tool list and the policy body, and track requests and incidents. In addition, Kanerika’s kanSuite governance services, kanGovern, kanComply, and kanGuard, are delivered on Microsoft Purview. They cover the data governance , compliance, and access controls that a GenAI data clause depends on. For teams building their own assistants, our generative AI services group designs internal tools meant for the approved list from day one.
Where the Groundwork Starts The groundwork is often data classification, and one Kanerika project shows what that looks like.
For a large North American healthcare organization, Kanerika used Microsoft Purview to build a central data catalog. The team also designed a classification framework with steward roles and usage policies, backed by user guides and training. The client recorded a 57% reduction in data discovery time and a 90% increase in compliance adherence, as the Purview case study details.
Pitfalls Kanerika Teams Watch For Three pitfalls come up again and again in this work, and each one is avoidable. They look like the drafting mistakes above, but they surface later, once the policy meets real systems.
First, AI features switch on inside software the company already licenses, sometimes after the inventory, so the tool list is stale on launch day. Second, the data rules point to sensitivity labels most files do not carry yet, so labeling has to run alongside drafting. Third, the request queue opens without a response target, and people quietly drift back to personal accounts.
Case Study
90% Compliance Adherence for Healthcare with Purview
A North American healthcare network centralized data governance on Microsoft Purview, cutting data discovery time by 57%.
Read the Case Study → Wrapping Up A generative AI policy works when any employee can answer three questions in under a minute. Which tool can I use, what data can I put in it, and who checks the result before it matters? So build every clause around those questions, test the draft against real scenarios, and keep the tool list current.
Start with the inventory, because nobody can write rules for use they have not seen. Then draft, test, sign, and train, and put the next review date on the calendar before launch day ends.
Frequently Asked Questions
What is a generative AI policy? A generative AI policy is an employee-facing document that sets the rules for using AI tools that write, summarize, code, or create images. It names the approved tools, the data people may share, the review required before output is used, and who approves new uses. It usually sits in the handbook or intranet.
What should a generative AI policy include? A complete policy covers purpose, scope, approved tools and accounts, allowed, restricted, and prohibited uses, data rules, human review, disclosure, intellectual property, vendor terms, approvals and exceptions, training and acknowledgment, and enforcement with a review cadence. Each clause should answer one plain question and carry a workplace example employees recognize.
How do you write a generative AI policy for employees? Name one owner and a small drafting group, then survey how people already use AI. Decide your default posture, set the scope, and draft each clause as an instruction with an example. Test the draft against real scenarios, get sign-off from legal, privacy, security, and HR, then publish, train, and collect acknowledgments.
Why is it important to establish generative AI usage policies? Employees already use AI, often on personal accounts. Microsoft’s 2024 Work Trend Index found 78% of AI users bring their own tools, and Cisco found 48% of surveyed professionals admitted entering non-public company information into GenAI tools. A policy gives people clear limits and an approved alternative before confidential data leaves the company.
Should a company ban ChatGPT and other public AI tools? A blanket ban rarely holds, because it pushes work onto personal devices and accounts where nobody can see it. Most organizations do better by approving enterprise tools with company accounts, limiting which data classes each tool may process, and banning only specific uses such as entering trade secrets into unapproved tools.
Can employees use ChatGPT or Microsoft Copilot under a generative AI policy? That depends on the approved tools list, the account type, and the data involved. Many policies allow an enterprise version of an assistant through a company account for internal and confidential data, while banning personal or free accounts for company work. Check each vendor’s current business terms on training and retention first.
What are examples of prohibited uses in a generative AI policy? Common prohibitions include entering trade secrets, credentials, or customer personal data into unapproved tools, letting AI make final hiring, firing, or credit decisions, creating content that impersonates a real person, and publishing AI output externally without the required review. Each prohibition should be stated with a concrete workplace example employees recognize.
Who should approve a generative AI policy? A single accountable owner, often the CIO’s office, the chief data officer, or general counsel, should sponsor the policy. Legal, privacy, security, and HR each review the parts they own, and business representatives confirm the rules fit real work. Record every approver and version date for audits and customer questionnaires.
How often should a generative AI policy be reviewed? Set a fixed review date, such as every six months, and print it on the cover page. Review sooner when the company adds or drops an AI tool, a vendor changes its terms, a contract imposes new limits, or a law changes. The approved tools list can update monthly without a full re-approval.
Is a generative AI policy the same as an AI governance framework? No. An AI governance framework sets how leadership decides on, oversees, and audits AI systems across their lifecycle. The usage policy turns those decisions into plain rules for employees, covering tools, data, review, and approvals. Most enterprises need both, with the policy pointing to the framework for escalation and oversight.
Do we need a generative AI policy if we already have an IT acceptable use policy? Usually yes. A standard IT acceptable use policy covers devices, networks, and accounts, but it rarely addresses prompts, uploads to AI tools, model training on company data, AI output review, or disclosure. Many companies add a generative AI section to the existing policy or publish a short standalone document that references it.
