Our employees are secretly using public AI tools for their daily work, and we do not have a formal policy. How do we write a practical AI policy that protects our business from security risks without creating a bureaucratic bottleneck that stops our team from experimenting?

Question

Tyler Smith · Accepted Answer

If you do not have an official AI policy, your employees are already using public tools in ways that expose your company to massive risk. Ignoring shadow IT is a dangerous mistake. To protect your business while encouraging productivity, you must write a simple, direct policy that provides clear guardrails without crushing initiative. Your policy must be practical and easy to follow. Start by defining what data is strictly off-limits for public AI models. Any proprietary code, client financial data, and personally identifiable information must never be entered into any public LLM. Make this a non-negotiable rule with clear consequences. Next, set up approved sandbox environments. Provide your team with enterprise accounts where data privacy is contractually guaranteed. This eliminates the temptation to use personal accounts. Ensure your policy requires team members to always verify the accuracy of AI outputs. If an employee uses AI to draft a deliverable, they retain full accountability for its correctness. We suggest using a simple three-question filter. First, is this data public? Second, is this tool enterprise-secure? Third, did a human review the output? If the answer to all three is yes, let your team run. Review this policy every quarter in your leadership team meetings to adapt to new technological developments. This keeps your operating system secure while keeping your team fast and agile.