What Shadow AI Looks Like on Your Network
AI adoption is greatly outpacing observability, security, and AI governance.
It’s the end of the day. A senior software engineer is staring down a mountain of code reviews and documentation that absolutely must be completed before the next release.
Her coffee is cold and the monitor is starting to blur.
Knowing that the official IT procurement process takes a month of forms and committee reviews, she logs into an unvetted AI tool and pastes a block of proprietary code into the prompt window.
Within seconds, the screen flashes with clean summaries. Smirking, she closes her laptop. When her team’s morning status meeting rolls around, she doesn’t mention the shortcut.
This is how quickly shadow AI can happen in the real world, and it creates huge AI governance gaps.
Three Traffic Patterns Revealing Shadow AI
In a recent IBM Institute for Business Value study of 2,000 technology executives, 70 percent of respondents said teams across their businesses are deploying technology faster than IT can track it. Another 77 percent said AI adoption is already outpacing their governance capabilities.
It isn’t just employees experimenting with stand-alone chatbots, either. AI is finding its way into applications and APIs, development environments, automated workflows, and agents. Cybersecurity Dive reports that 43 percent of security incidents involved shadow AI in 2025, more than double the share from the previous year, while more than two-thirds of organizations also lacked governance processes to limit shadow AI.
A list of approved tools won’t show all of that, but your network can.
Unexpected AI Data Flows
Think back to our engineer. Her interaction with that AI tool has to cross the network. The same is true when an intern uses a chatbot to build a presentation or a CIO runs an urgent pilot program with a new AI vendor. On the wire, that activity can stand out from normal browsing:
- A sudden outbound spike as large volumes of data move from an internal environment to an external AI service
- A new AI destination appearing in traffic from an application that normally communicates with a familiar set of services
- An unusual traffic imbalance with far more data leaving the environment than coming back
None of these proves a shadow AI incident. But each gives IT a reason to ask what changed, who or what is behind the activity, and whether it belongs there.
Persistent Machine-to-Machine AI Traffic
Then there’s the AI activity that doesn’t involve a person at all.
For example, a developer connects an AI API to an application. The experimental integration becomes orphaned when someone leaves the company, but the credentials remain part of a larger service.
Now the traffic looks different. Instead of someone occasionally opening an AI tool, there’s an ongoing machine-to-machine (M2M) connection between an internal workload and an outside service. You may see structured API handshakes or automated pipelines running continuously, independent of a human user session.
Over time, that service can quietly become part of the application’s transaction path. Nobody formally documented it in the architecture. If the service or its credentials are compromised, it can create a new attack path into the environment.
New and Changing AI Dependencies
With AI, dependencies extend into the model layer and the services around it.
Today, an application may talk to one AI provider. Tomorrow, a developer tests another. An agent might introduce its own connections as it moves between services to complete a task.
Those relationships can change without showing up immediately in an inventory or architecture diagram. A slowdown that looks like an application problem may actually sit with an external AI provider. A new automated workflow could be sending traffic somewhere nobody expected. Or a service that seemed unimportant during testing may now sit directly in the path of a business application.
Network traffic provides a way to see those relationships as they’re happening, rather than discovering them after something goes wrong.
See Your Complete AI Footprint
The NETSCOUT data platform translates raw network traffic into highly contextualized, real-time Smart Data, providing the granular observability required to help organizations discover previously unknown AI activity and understand the users, applications, services, and dependencies behind it. This brings hidden data movements into clear daylight, making it easier to control shadow AI.
Visit our Detect Shadow AI use case page to see how NETSCOUT can help you discover hidden AI activity in your environment.