Shadow AI describes employees or teams using AI services without formal approval, visibility or agreed controls. It can emerge quickly because consumer AI tools are easy to access and often genuinely useful.
Why it matters
- Sensitive or confidential information may be submitted to services with unsuitable terms.
- Outputs may influence decisions without appropriate checking.
- The organisation may not know which suppliers are processing information.
- Teams can create duplicated or inconsistent workflows outside normal change control.
Why banning everything rarely solves it
If employees can see genuine productivity benefits, prohibition without a usable alternative may simply push the activity out of sight. Governance works better when approved routes are clear and proportionate.
A better starting point
- Ask teams which AI tools they use and for what purpose.
- Classify use cases by data sensitivity and decision impact.
- Define approved tools and prohibited information.
- Create a simple route for new use cases to be assessed.
- Train employees on verification, confidentiality and escalation.
A practical next step
Run a short shadow-AI discovery exercise, then build policy around actual behaviour rather than assumptions.