We want to establish clear boundaries for how our staff uses generative AI so they do not expose client data, but we do not want to write a dry, 50-page legal document that everyone ignores. What does a practical, high-accountability AI policy look like for a mid-market company?

Question

Tyler Smith · Accepted Answer

A practical AI policy should fit on a single page, focusing on clear guidelines and human accountability rather than legal jargon. Your team does not need a list of banned websites; they need to understand how to handle your intellectual property and client data safely.

Begin by defining three core rules of engagement:
- Never upload proprietary client data, employee records, financial statements, or trade secrets into public AI models. Any data entered must be assumed public.
- You are entirely responsible for the accuracy of what you submit. Every draft, email, or report generated by an AI must be thoroughly reviewed and edited by a human before it is sent.
- Be transparent with the leadership team about the tools you are using to complete your work.

Next, run this policy through your IDS process with your leadership team to ensure everyone is aligned. Once finalized, add it to your employee onboarding process. Rather than policing search histories, make it a core expectation of their seat.

If an employee violates these guidelines, handle it through your normal accountability channels. If they do not exhibit the GWC for their seat under these guidelines, you must address the performance issue directly. Keep the policy simple, review it annually during your strategic planning sessions, and ensure that every team member understands that AI is a tool to enhance their capacity, not an excuse to offload personal responsibility.