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Two-thirds of personal-account AI is business work. Nearly half of personal use flows through enterprise accounts. And token counts tell employers nothing about whether any of it is generating value.

A global mobile payments business with two distinct AI risk surfaces: customer operations handling sensitive financial data, and engineers working with source code and internal systems. A two-week proof of value found material exposure on both sides.

A global automotive manufacturer and race team with some of the most valuable intellectual property in engineering. A workforce that needed AI to stay competitive. A security team with no visibility into what was leaving through it.

A global investment group had deployed Microsoft Copilot across the business, written an AI policy, and still had no visibility into how AI was actually being used. Because Copilot was not the only tool in use.

A major international law firm with an AI usage policy, a security stack including Zscaler and a browser security tool, and a growing problem: the policy existed, but there was no way to prove it was being followed.

A large US healthcare services organization, a Microsoft E5 deployment with DLP rules in place, and a category of sensitive data flowing into AI tools that the existing controls had no way to detect. The data was personal. The use case was entirely legitimate. And none of it looked like anything a pattern-matching rule was built to find.

A large UK passenger rail operator with thousands of employees, a permissive approach to AI, and no visibility into how it was being used. A three-week proof of value that changed the picture entirely.

For security teams deploying ChatGPT Enterprise, Business, and Edu plans