In many companies, employees turned to AI chatbots before management made any decision: a salesperson writes emails with one, an accountant pastes in a spreadsheet to get a summary, and a developer drops in a piece of code to fix a bug. This hidden use, sometimes called Shadow AI, brings both productivity and risk, because nobody knows what data went where. An AI usage policy is not meant to block these tools; it is meant to make their use trustworthy.
Why a total ban does not work
A complete ban usually just drives usage underground: employees do the same work on a personal phone and a personal account, and the company loses all control. A better approach is to name the approved tools, draw clear data boundaries and keep responsibility for the results with people.
Your policy should answer these questions
- Which tools are allowed? A list of approved services, and whether they are used with company or personal accounts. Company accounts allow central management and revoking access when an employee leaves, and sometimes come with better data retention terms; these terms differ between services and plans, and they change.
- What data must never be entered? For example passwords and access keys, personal data of customers and employees, contracts, confidential financial information, network diagrams and configurations, and proprietary source code.
- Which uses are open, conditional or prohibited? Drafting a general email might be open; analyzing customer data only with an approved tool; and decisions about hiring or customer credit only with human review.
- Who is responsible for the output? Always the person who used the tool. AI output may be wrong, incomplete or similar to someone else’s content, and must be checked before use.
- Where must we be transparent? For example, customers should know when they are talking to an automated assistant, and it should be clear which published texts were prepared with AI help.
- What do we do when something goes wrong? If someone accidentally enters confidential information, they need to know whom to report it to and how quickly.
Divide data into a few simple levels (public, internal, confidential, highly confidential) and state which tools are allowed for each level. This works better than a long list of prohibitions.
How to put it into practice
- Write it with departments, not for them: Ask sales, finance, technical and HR what they use AI for. A policy that does not fit real work will be bypassed.
- Keep it short: Two or three pages with clear examples beat a twenty-page document nobody reads.
- Short internal training: One session with real examples of “write this” and “don’t write this.”
- Technical controls: Access to approved services through company accounts, with single sign-on (SSO) where possible; categorizing these services on the firewall or proxy; and data loss prevention (DLP) tools if needed.
- A clear owner: One person or a small committee that reviews requests for new tools.
Common mistakes
- Writing the policy and forgetting it. Tools and their terms change constantly, so review the policy at least once a year and whenever a new tool is introduced.
- Ignoring browser extensions and AI features that get switched on inside existing software.
- Relying on vendors’ marketing claims instead of reading the terms of use.
- Writing definitive legal clauses without consulting the company’s legal advisor.
Checklist
- The list of approved tools and account types is defined.
- Data classification levels and the rule for each level are written down.
- Responsibility for reviewing output lies with the user, and the policy says so.
- A path for reporting mistakes and data leaks is defined.
- Staff have received short training and have acknowledged the policy.
- The policy owner and next review date are set.
A good policy on paper is not enough; it also needs technical controls on the network and user accounts. ITPlus724 engineers can work alongside your team on the technical side, such as firewall configuration, account management in Active Directory and data protection.