The issue is not adoption. It is visibility

AI usage inside enterprises is accelerating faster than the security controls designed to manage it.

Across teams we work with, tools like ChatGPT, Copilot, and Claude are already embedded in day-to-day workflows.

Engineers are using them to:

• Debug code
• Explore solutions
• Summarise internal documentation

Product and operations teams are using them for:

• Drafting communications
• Analysing data
• Accelerating decision-making

The issue is not adoption. It is visibility.

Most organisations do not actually know:

• What data is being entered into AI systems
• How often it is happening
• Where sensitive information may be exposed

Policies and training do not solve this on their own. Blocking tools is not practical either: people find workarounds, or the work simply slows down.

AI usage is already part of how teams work, and it is not going away. The real challenge is how you introduce controls without disrupting productivity.

We are interested in how others are approaching this: baseline visibility first, graduated enforcement, or something else entirely. If you are wrestling with the same questions, get in touch.