Key takeaway
Treat shadow AI as a visibility and process gap. Record the need, tool, data, access and owner, then approve a suitable use case as a controlled pilot.
Use shadow AI signals to find gaps
Shadow AI means using AI tools for company work without IT knowledge or approval. It includes more than public chat prompts. It can be a department-purchased assistant, an agent connected to a user’s mail or files, company data pasted into a personal account, or an employee-built API workflow. Signals include an incomplete IT inventory, an unfamiliar subscription in expenses, an unreviewed OAuth grant, duplicate bots and AI output sent to a customer without review.
Tools and spending
Look for AI services in personal and department expenses, contracts and renewal notices.
Identity and connections
Review SSO, OAuth and app integrations: which account can reach which mailboxes, files and systems?
Workflow
Ask teams openly about the tool, purpose, data, owner and decision oversight.
Build an inventory from evidence
Invite employees to disclose tools and the work they are trying to complete without fear of punishment. Reconcile the responses with expenses, SSO and OAuth grants, and available network or DNS observations. Record each tool’s purpose, owner, users, data sources, permissions, supplier and pilot or production status. Network visibility describes observed traffic; it does not reveal prompt content or automatically identify a model that runs offline.
Set policy and a fast approval path
Clearly name approved work tools, data that must not be entered into external services and cases where AI output needs review. A short request for a new tool should state its purpose, data, integrations, owner and accountable reviewer. Assign security, data-owner and other relevant reviews with a target decision time. An approval path that takes too long can push the same need back into hidden use.
Name the purpose and owner
Choose one measurable task and name the person accountable for its outcome.
Limit data and permissions
Approve required sources and actions explicitly; grant minimum access.
Define human oversight
Who reviews the output, corrects errors and stops the agent when needed?
Check common stall points before the pilot
AI pilots can stall during extended platform comparisons, unready integrations, limited IT delivery capacity, custom work for every use case, late security review, poor-quality company data or missing ownership. Before starting, create a simple readiness matrix: use case and business outcome; data quality and permission; integration, test environment and available IT capacity; human approval and audit trail; technical and business owners; and stop conditions. This brings security questions into design instead of raising them after a prototype is built.
| Question | Evidence |
|---|---|
| What work will change? | Current workflow, delay and exceptions |
| What can the agent access? | Data-owner approval and test permissions |
| Who catches an incorrect result? | Review point, escalation and stop rule |
| Is the pilot useful? | Usage, quality, correction and time measures |
Test the outcome in a bounded pilot
For example, a sales team wants first drafts for incoming pricing enquiries. Start with a small representative set of approved catalogue material and reply templates; exclude sensitive customer data from the test set and give the agent read access only to those sources. A sales representative checks the price, terms and any promise before sending. Measure eligible enquiries, edits to drafts, time to send and any incorrect or misleading reply. Improve the data or use case, or stop the pilot, if the results do not support it.