Shadow AI: solve the work problem behind the unapproved tool

An unapproved AI service can reveal a product gap in the organisation’s own AI programme. The person using it may need a document format, an integration or a turnaround time that the approved route cannot deliver. For an enterprise AI lead, stopping the connection and fixing that unmet need are separate pieces of work.

Shadow AI means AI use outside the organisation’s approved scope or visibility. It can involve an unfamiliar service, a personal account used for work or a newly enabled feature inside an established application. Review the specific use, including the information involved, before deciding what should happen next.

What the discovery signal actually establishes

A visit to an AI service establishes much less than a confirmed upload of a confidential document. Keep the observation and the conclusion separate. Start with the service, available time and identity information, the source of the evidence and what remains unknown. Give the team involved a straightforward way to correct a mistaken interpretation.

Microsoft Defender for Cloud Apps’ discovery documentation makes the visibility boundary tangible: cloud discovery analyses traffic logs against an app catalogue, and apps absent from that catalogue are not discovered by default. Available attributes also differ between log sources. Integrating Defender for Endpoint can extend discovery beyond the corporate network, but it does not turn a traffic observation into proof of the contents of every AI interaction.

Microsoft separately documents AI-management capabilities in Defender for Cloud Apps and Microsoft Purview. Defender includes a Generative AI category for app discovery. Configured Purview policies can provide events such as AI interactions, DLP matches and sensitive-information findings. Those are different kinds of evidence, with different setup requirements. Establish which capability actually produced a finding before using it to describe what an employee submitted.

Use discovery tools to open a well-founded enquiry. Do not label a person’s intent from a domain name, or treat an empty report as proof that no unapproved AI use exists. Before broadening collection, agree the purpose, access and retention of the monitoring with the responsible privacy and security teams.

Ask about the work before proposing the replacement

“Summarising documents” is too vague for a useful decision. Ask which documents, what output is needed, who receives it and what fails in the approved process. Public marketing material and an unannounced acquisition contract present very different questions even when the user selects the same summarisation feature.

Establish the service and account actually used. A company name alone will not identify the relevant settings, purchased terms or integration. Ask whether files were uploaded, text was pasted, or a connector was granted access to a wider repository. Collect enough information to establish the exposure without duplicating sensitive material in unrestricted tickets or chat.

Where the evidence indicates a possible security incident, bring it into the existing incident process promptly. That investigation can run alongside the work to provide a supported alternative. A useful business task does not justify ignoring an exposure, and investigating the exposure does not remove the need to complete the task.

Put the alternative through the same job

Consider a hypothetical team that uses an unapproved summariser because the approved service rejects scanned supplier documents. Simply issuing more licences for the approved product leaves the obstacle in place.

Test a permitted document-conversion and summarisation route using suitable sample material. The comparison should include the important tables, small-print conditions and scanned pages, not just a clean paragraph that every tool can handle. Ask the actual users whether the resulting summary supports their decision and how much manual correction remains.

The replacement might combine an approved conversion service, a conventional search tool and human review. It might justify enabling an additional feature in the approved AI service. It might also expose a task that should remain manual until a dependable route is available. The acceptance criterion is that the work can be completed within the agreed data boundary at a tolerable cost.

Include access and support in that test. An alternative that works only for the pilot owner, requires an unavailable administrator or takes days to provision will not resolve the operational problem for the rest of the team.

Close with a usable decision

A review should end with approval for a defined use, migration to a supported route, a time-limited exception or a decision to stop the activity. Make the boundary clear enough for the team to apply: which information, which account or feature, and which outputs need checking.

If migration is the outcome, confirm that the team can complete the work through the replacement and address any access or data left in the former service through the appropriate owners. A block rule can enforce one part of the decision; it cannot establish that the workflow has moved successfully.

Look at reopened cases and recurring causes of unapproved use. Missing formats, unclear account access and slow approval routes can become a practical improvement backlog. A falling number of discovery alerts is not sufficient evidence of success if visibility has narrowed or the underlying work has moved elsewhere.

Use the lessons to improve both the approved service and the AI acceptable-use guidance that answers employees’ next questions. The programme becomes more useful when people know how to get necessary work done before they have to improvise.