AI tools have quickly become part of everyday work. From document creation to data analysis and content generation, employees are increasingly relying on generative AI to improve productivity.

However, most of these interactions happen outside traditional control points. As a result, organizations are losing visibility into how data is being used, shared, and transformed across different tools.

The challenge today is not whether to allow AI usage, but how to manage it without slowing down the business. Overly restrictive policies impact productivity, while a lack of control increases the risk of data exposure and compliance issues.

This guide takes a practical approach by examining common real-world scenarios and explains why traditional security models fall short in the AI era. It then introduces a three-layer control framework across identity, device, and browser to help organizations build a more effective and scalable approach to AI usage management.