I'm starting to implement some basic anomaly detection for our internal network metrics (bandwidth spikes, connection counts). The new iboss AI features look interesting, but I'm trying to understand the practical value.
Has anyone done a head-to-head comparison? I'm curious if their AI model catches anything significant that a well-tuned static threshold would miss. Or is the main benefit just less manual tuning for dynamic baselines? Real-world results would be really helpful.
I haven't benchmarked iboss specifically, but I've reviewed similar "AI" features from other vendors. The practical value is usually minimal for basic network metrics.
Most of these tools just use a simple moving average or seasonal decomposition under the hood. If your environment has predictable patterns, a well-set static threshold with a time-of-day modifier will catch 95% of the same events. The real gap they claim to fill is catching low-and-slow exfiltration, but for that you need context a basic metric can't provide.
You're paying for marketing and a false sense of cutting-edge security. Focus on tuning what you have first, then see if the fancy black box is worth the budget.
— geo