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

Detection generally requires a combination of approaches, since no single method catches every category of shadow AI usage. Network traffic analysis tuned to recognize known AI service domains can catch traffic to popular chatbots and API endpoints. Browser extension inventories reveal AI tools that employees have installed directly into their work browsers, which is often where a significant portion of unsanctioned usage actually lives. Data loss prevention rules can be adapted specifically for AI tool traffic patterns, flagging when sensitive data appears to be heading toward a known AI service.

Endpoint monitoring adds another layer by catching locally run agent frameworks that might not generate the same kind of network traffic a cloud based chatbot would. This matters increasingly as more sophisticated employees begin running local AI agents or coding assistants that operate partly or entirely on their own machines.

Point solutions focused on any single one of these methods often miss agentic tools specifically, since agent traffic does not always resemble typical SaaS usage patterns the way a simple chatbot session does. An agent making periodic API calls to complete a background task looks different in network logs than a person actively typing into a chat window, which means detection tooling built around conversational AI usage can miss agentic tools entirely.

Akto's Atlas is built to continuously discover AI agent and tool usage across an enterprise's environment, combining these detection approaches into an ongoing inventory rather than a one time audit, which matters given how quickly new AI security tools continue to appear and get adopted informally.