Shadow AI
Shadow AI appears when people need outcomes now and approved channels are too slow, too limited, or too hard to use. The result is unmanaged data exposure and fragmented decision quality.
Lesson: demand will not disappear, so channel it into safer defaults.Why this matters in practice
Consumer AI tools can receive sensitive prompts, files, and strategic context outside enterprise controls. Pure prohibition usually fails. Better outcomes come from sanctioned tools, simple policy language, and visible support for real work use cases.
What to check in your organization
1. Do users have approved AI tools that are easier than unsanctioned ones?
2. Are prompt/file upload controls in place for sensitive data classes?
3. Does policy distinguish experimentation, internal use, and customer-facing use?
4. Can teams request new AI use cases with a fast governance path?
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Next actions
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