Shadow AI detection

Shadow AI detection is a way to find the AI tools your team uses that were never approved, without guessing. It reads signals a company already has: expense and subscription records, login logs, browser extensions, and which AI notetakers joined which calls. From those, it builds one inventory of every AI tool actually in use and flags the ones that touch client or confidential data. You end up with a clear approved-or-blocked list instead of a vague sense that "people are probably using ChatGPT." The goal is visibility and safer options, not a hunt.

Asking works until it does not: people forget tools, or leave off the ones they suspect are not allowed, which are exactly the ones that matter. This reads real signals, so the list is closer to reality. At the other end, the heavy enterprise security suites (sometimes sold as DLP, for data loss prevention) sit on all your network traffic and are priced and staffed for large security teams. This is lighter and built for a 10 to 70 person company. It works from records you already have, and it produces a decision list you can act on, not a stream of alerts.

No. It inventories tools and data flows at the business level, not individuals. It looks at what AI tools are in use and what data they can reach, using company records like expenses, logins, and meeting attendance. It is not keylogging, reading private messages, or tracking what any one person types. It works best run in the open: you tell the team you are building an approved AI tool list so people can use AI safely. When staff understand the point is safer options and not punishment, they help you find the tools instead of hiding them, which is the only way this stays accurate over time.

It can, and it is built to be honest about that. No single signal catches everything: a tool paid for personally with no browser extension and no meeting bot can slip past. That is why it combines several sources instead of trusting one, and why a person reviews the flags rather than auto-blocking. Every tool on the list points back to the signal that surfaced it, so you can check any item. The first pass finds the bulk of what is there and the highest-risk uses; the ongoing check keeps the list current as new tools appear.

On the signal side, it reads Google Workspace or Microsoft 365 admin and login data, single sign-on tools like Okta, expense and subscription tools like Ramp, Brex, or QuickBooks, browser and extension inventories, meeting tools like Zoom, Google Meet, and Fireflies, and network or DNS logs where they exist. On the output side, the inventory can land in your CRM such as Attio, a shared doc, or Slack, wherever your team will actually see it. Most tools with an API can be added. The audit maps your exact stack and picks the signals that matter for your setup.

Usually two to three weeks. The first days go to connecting the signal sources you already have and pulling the initial data. Then the tools get matched against the directory of known AI tools, the risky ones get flagged, and a person reviews the edge cases with you so the list reflects your real risk, not a generic score. You have a first full inventory quickly. The rest of the window is deciding your approved-or-blocked calls and setting up the light ongoing check so the list stays current after the first pass.

Two parts. Tooling runs as a modest monthly cost for reading the signals and matching them, which scales with how many sources and people you cover. Implementation is a fixed scope, quoted once the audit maps your signal sources, your risk rules, and where the inventory should live, so you are pricing a defined build rather than an open-ended retainer. The audit itself is where the scope and price get set against every other opportunity in your business, priced as part of the audit, so you are not guessing at effort up front.