Expose the AI Traffic Your Security Stack Never Logged
Your CASB, DLP, and SIEM govern traffic that agrees to be governed. Everything else reaches model providers unrecorded.
Key Takeaways:
- Every layer in your stack assumes AI cooperates. CASB, DLP, SIEM, browser extensions, IdP logs, and network tooling all assume traffic crosses a browser, an identity provider, or a monitored path. A direct terminal call to a model provider crosses none of them.
- Shadow AI is already showing up in breach data. 43 percent of AI related security incidents involved shadow AI, more than double the year before, and 68 percent of those breached organizations had no governance process to limit it.
- The gap will not close with configuration. It lives in the architecture. Closing it needs observation at a layer that works whether or not the traffic cooperates, such as eBPF at the kernel.
- Discovery has to run across three surfaces. Your workforce brings AI in, your infrastructure carries it, and your code and applications embed it. Nine signal sources cover all three.
- You cannot govern, classify, or investigate an asset you never found. Every other AI security capability depends on discovery running first.
If a developer sent customer data to an unapproved model provider this morning, would anything in your stack have recorded it?
Most security tooling was built for a traffic model AI usage increasingly skips. Direct API calls, local model runtimes, agents embedded inside SaaS platforms, and unregistered MCP servers never cross a browser, never authenticate through your IdP, and often never touch your network path. Calling this a tooling failure misreads it. These layers were built for cooperating traffic, and the riskiest AI usage in your environment was never going to cooperate.
Download the report for the nine discovery signals across your workforce, infrastructure, and code, a self-audit to find your own blind spots, and six requirements to look for in a discovery solution.