The tricky part here is not just spotting AI apps running on endpoints, but understanding how those AI tools interact with multiple cloud MCPs in real time - and what risks emerge from those connections. We built LynxTrac's endpoint security to track deep app behavior and config changes, including registry and network activity, but connecting that to AI-driven MCP calls was a missing piece.

Traceforce's approach to building a live connectivity graph between AI apps and MCPs is exactly the kind of visibility traditional EDRs miss. We've seen that standard EDR alerts can't flag API key leakage or risky AI-generated commands without that context. For instance, LynxTrac's file integrity monitoring and CVE scanning catch suspicious changes, but without a way to tie AI actions to those changes, it's hard to tell if an AI assistant just triggered a destructive script.

I'm curious how your pentesting tool handles the frequent updates in AI app behaviors and MCP APIs without overwhelming false positives? We've struggled with tuning automated alerts that map to MITRE ATT&CK tactics in an environment where AI tooling itself evolves weekly. Also, how do you balance local content inspection for privacy while enabling enough control to block risky AI commands before they hit production?

Your "warn and acknowledge" flow sounds smart - it aligns with what we see customers want: guardrails, not blockers, so devs don't feel slowed down. We recently added AI-driven incident summaries to LynxTrac to help teams triage alerts faster without digging through logs. Wondering if you've tried AI-assisted analysis on your MCP pentest results to speed up identifying real risks?

Would be great to hear more about how you maintain coverage on changing AI/MCP combos without constant manual tuning. If you're curious, I can share how we balance automation and manual policy updates to keep noise low while catching real threats.