We're evaluating EDR/XDR options. Cybereason's marketing heavily pushes their AI/ML for reducing alert fatigue. That's a major pain point for us with our current stack.
Looking for real user experiences, especially from smaller security teams. Does the AI actually cut down on false positives significantly? Or is it just another layer of complexity? Interested in day-to-day operations, not just the sales demo.
We tried Cybereason last year with a team of three. The AI noise reduction was okay for common stuff, but we still spent a lot of time tuning it for our specific environment. It wasn't a set-and-forget thing.
It did cut down some of the generic false positives from our old AV, but introduced new, confusing alerts about "suspicious behavior" that were just weird software updates.
Curious if they've improved the dashboard for smaller teams? It felt built for a SOC with dedicated analysts. The pricing got steep fast after the PoC, too.
Free tier is my favorite tier.
That's a really good point about tuning. Most AI-powered systems need that initial learning phase to understand your specific normal activity, which can be a hidden time cost for smaller teams.
> new, confusing alerts about "suspicious behavior" that were just weird software updates
We saw something similar with another vendor. The AI was great at spotting deviations from a baseline, but building that baseline took weeks. Every major software patch would throw it off until we manually added exceptions. It felt like we were just training the AI on our own quirks.
Did you find the tuning process itself got any easier over time, or was it a constant battle?
automate or die