How to find shadow AI in your organisation

Every organisation using software is using more AI than it knows. Teams subscribe to tools on corporate cards, staff sign into assistants with their work accounts and vendors switch on AI features inside products you bought for something else. That is shadow AI, the AI in use without approval or a record. None of it announces itself to whoever keeps the AI register, and all of it carries the risks the register exists to govern. The good news is that shadow AI leaves traces, and the traces live in systems you already run.

Why surveys don’t work

The instinctive first move is to ask. A survey goes out, teams respond with what they remember and consider worth mentioning, and the result understates reality for reasons that have nothing to do with bad faith. People forget the tool they trialled in March, do not think of the AI feature inside their CRM as “using AI” and hesitate to volunteer the subscription nobody approved. Surveys are worth running once for the signal they give about awareness, but a register built on self-reporting alone is a register of what people admit to.

Where the traces are

The productive sources, roughly in order of how much they usually yield, are these.

Your identity provider sees sign-ons. When staff use their work accounts with AI services, the sign-on events are already in your identity platform, and they are the single richest source because they capture usage rather than intent.

Your finance system sees subscriptions. AI tools cost money, and the charges land in your accounting platform with merchant names that identify them. Finance data catches the paid tools that identity data alone can miss, and it names the team that pays.

Your device management sees installations. Managed devices report their installed applications, which catches desktop AI tools and the browsers’ AI extensions, especially when browser management is in place.

Your cloud inventories see what your own builders stand up. AI services in your own cloud accounts, from hosted models to AI-enabled data services, appear in the cloud provider’s own inventory, and internal builds are the shadow AI with the deepest data access.

Your network logs see traffic. Requests to AI services from inside your network are visible in the logs of traffic leaving it, which catches usage that touches nothing else you manage.

Your IT service management and workspace tools see the rest. That means the software recorded in your IT asset database whose AI features nobody flagged, and the AI apps installed into your messaging and collaboration platforms.

Correlation is the actual work

Any one source produces hits. The work is resolving them, because the same tool appears as a sign-on event, a card charge and an installed application, and treated separately those are three mysteries instead of one finding. Cross-referencing collapses the noise, because the sign-on names the users, the charge names the paying team and the device data names the machines, and together they describe one system with enough context to make a decision about. Do this in a spreadsheet if your footprint is small. It stops being manageable by hand quickly.

From finding to governance

A discovery finding is not yet a governed system. It is a question. Someone has to decide whether the thing found becomes a governed system with a named owner, gets retired with its users pointed at a sanctioned alternative, or needs investigating first. The decision should be recorded whichever way it goes, because a documented “we found it, assessed it and retired it” is governance working, and an unexplained distance between what discovery found and what the register holds is the opposite.

Run it as hygiene, not as a hunt. The point of discovery is a true register, not catching people, and the organisations that treat found users as offenders teach everyone to hide better. Most shadow AI is people trying to do their jobs with the best tool they could find, which is information about unmet demand as much as it is a governance finding.

Where Aicura fits

Aicura’s discovery does the reading and the correlating described above. Connectors cover identity providers, device management, cloud inventories, IT service management, code hosting, browsers and workspace tools, and related signals resolve into one suggestion in a queue, with every place it was seen and any possible matches attached. A person adds each one to the register, links it to a system already there or ignores it, and the History tab keeps who decided what and when. An ignored item stays ignored on later scans.

Discovery scans, the queue and the decisions are on the free Register tier, as are most connectors. The finance and network traffic sources start at Enterprise.

Aicura provides guidance, not legal advice, and does not certify or audit. What your organisation does about each finding stays its call.

A note on this page

This guide describes an approach to finding ungoverned AI, not a legal procedure, and reading source systems like identity logs and device inventories should be done within your organisation’s own privacy, employment and monitoring policies. Take your own advice on those before you start.