Shadow AI is already in your organisation. People are using consumer chatbots to write emails, summarise documents, and draft policies—often with good intentions and poor risk awareness. The response is rarely effective when it is purely restrictive.
A pragmatic approach treats shadow AI as a governance and enablement problem: provide safe alternatives, make expectations clear, and reduce the risk of sensitive data exposure without blocking legitimate productivity gains.
Start with a realistic policy
Policies work when they are simple and enforceable. Common elements include:
- Data classification rules. What data can and cannot be entered into external tools.
- Approved tool list. A short list of sanctioned tools with known contracts and controls.
- Use-case boundaries. Acceptable uses (drafting, summarisation) vs prohibited uses (decisions, regulated advice).
Pair policy with architecture controls that reduce risk at the source (see data minimisation and data residency).
Provide safer alternatives
People adopt shadow AI when internal options are slow or unavailable. Provide:
- Enterprise-approved assistants. With authentication, logging, and retention controls.
- Templates and training. “How to use AI safely” guides for common tasks.
- Guarded integrations. If tools are enabled, use strong authorisation layers (see tool authorisation).
Do vendor governance properly
If employees use a tool, procurement will eventually need to catch up. Use a lightweight but repeatable review path (see procurement playbook and choosing providers).
Measure and iterate
Shadow AI governance improves with measurement:
- Adoption of approved tools vs unsanctioned tools.
- Reported incidents and near-misses.
- Training completion and comprehension checks.
The aim is not to eliminate shadow AI overnight. The aim is to move it from uncontrolled risk to a managed, enabling capability—without losing the productivity upside that drove adoption in the first place.