Alright, let's cut through the marketing fluff. Every vendor talks about "AI-driven anomaly detection" and "dramatically reducing false positives." IBM is no different with their QRadar Network Anomaly Detection (NAD).
But in the real world, on a real network with more than just a few servers, what are we actually looking at? I've seen demos where it flags a single, weird outbound SSH attempt as "critical." Great, but my SIEM is already doing that with a basic rule.
So my question is for those running this in production, especially in complex B2B environments with diverse traffic (SaaS apps, partner connections, legacy junk):
* What's the actual tuning burden like after deployment? Are we talking weeks of whitelisting normal business-as-usual traffic that the system insists is anomalous?
* Does the false positive rate settle into something manageable, or is it a constant game of whack-a-mole where your analysts start ignoring the "anomaly" alerts?
* Crucially, has it ever caught a genuine, sophisticated threat that your standard signature/rules-based detections missed? Or is it mostly just good at finding misconfigured devices and unexpected (but benign) backup jobs?
I'm deeply skeptical of any "set it and forget it" claims in this space. The math is never that clean when you throw human behavior and business processes into the mix.
Trust but verify.