How can security teams detect unsanctioned AI tools and shadow AI agents on the network?

No single sensor catches all of it, because shadow AI surfaces in four different places: cloud traffic, browsers, endpoints, and background agent activity. Detection means covering all four and treating the result as an inventory that decays.

Network analysis tuned to known AI service domains and model endpoints catches the obvious layer, meaning chatbots and hosted model calls. Browser extension inventories catch a surprising share of the rest, since that is where employees actually install AI into their working day. DLP rules adapted for AI destinations flag sensitive data heading toward those services. Endpoint monitoring picks up locally run assistants and agent frameworks that generate little recognizable traffic at all.

Agents defeat tooling built for chat. A conversation has a shape: a person types, a response returns, the pattern repeats during working hours. An agent makes periodic calls to complete a task, sometimes overnight, sometimes on a trigger, with no human rhythm to key on. Detection logic written around conversational usage reads that as ordinary service traffic and moves past it.

The second reason point solutions fall short is time. An audit tells you what was running the week you ran it. New tools appear weekly and spread inside a team in days.

Akto Atlas runs this as continuous discovery rather than a periodic sweep, combining browser and extension visibility with agent and tool mapping, so the inventory reflects what is running now and what each tool can reach.

Finding shadow AI once is not the hard part. Keeping the list true is.