Shadow AI: what to do when your teams are already using it
Blocking AI tools does not stop usage — it moves it somewhere you cannot see. Governance works better than prohibition.
In most companies we work with, AI adoption did not start with a strategy. It started with someone pasting a customer email into a chatbot to draft a reply.
Why bans fail
Prohibition creates two problems. Sensitive data still leaves the company, now through personal accounts on personal devices. And the organisation loses the productivity gains its competitors are capturing. The question is not whether teams use AI, but whether that usage is visible.
Start by seeing the surface
Before writing policy, establish the facts: which tools are in use, by which teams, funded from which budgets, touching which categories of data. Expense reports, SSO logs and network telemetry usually reveal more tools than anyone expected.
Then route usage through a gateway
A single access layer for model calls turns an unknown into a managed asset. Through one gateway you get an audit trail of prompts and responses, per-team budgets and rate limits, data redaction before requests leave your perimeter, and the ability to swap models without touching application code.
Write the shortest policy that works
Three categories are usually enough: data that must never be sent to a model, data that may be sent to approved and contracted providers, and data that is unrestricted. Anything longer will not be read.
Make the sanctioned path the easy one
Shadow AI is a user-experience problem before it is a security problem. If the approved route is faster and better than the unapproved one, governance stops needing enforcement.