Shadow AI Is Already at Work: Build a Policy People Will Follow

A practical guide to finding unmanaged AI use, protecting sensitive information, and creating useful guardrails without stopping experimentation.

The first AI governance problem is rarely choosing a platform. It is understanding which tools people already use, what information enters them, and which business decisions depend on their output.

Discover use before writing rules

Ask teams which AI tools they use, what tasks they accelerate, what data they enter, and whether anyone checks the output. Make discovery nonpunitive so the inventory reflects reality.

Group uses by consequence: low-risk drafting, internal analysis, customer-facing work, regulated or confidential information, and decisions that materially affect people or money.

• Tool and account owner
• Information entered
• Output destination
• Human reviewer
• Business consequence

Write guardrails around information and decisions

A useful policy explains what information may never enter an unapproved tool, which tools are approved for which tasks, when a person must verify output, and how employees request a new use case.

Include retention, intellectual-property, customer confidentiality, vendor terms, and incident reporting. Legal and security review may be needed for the organization’s specific obligations.

Make the approved path easier

Rules fail when the only safe answer is no. Give employees an approved toolset, examples, short training, and a clear owner who can evaluate new workflows.

Review the inventory and policy regularly because tools, features, contracts, and business uses change quickly.

Talk through your situation

Every environment, contract, team, risk profile, and business priority is different. AMD can help clarify requirements, compare options, coordinate providers, and define a practical next step without forcing a predetermined product.