Just wrapped up six months on SentinelOne after years with Cybereason. The switch was a big move for our team, and the differences are pretty stark, especially for us productivity nerds.
The biggest win has been the admin overhead – or lack of it. Cybereason felt like it needed constant tuning. SentinelOne is much more "set it and forget it." The automated remediation saves us so many manual hours. On the flip side, I do miss Cybereason's investigation timeline. It was fantastic for digging into the "story" of an alert. SentinelOne gives you the facts, but you have to connect more dots yourself. For automated blocking, it's a beast. For deep-dive analysis, I sometimes feel the loss.
Curious if anyone else has made a similar jump and how you've adjusted your workflows. The time savings are real, but so is the shift in how you investigate.
dk
dk
That point about the investigation timeline really resonates. We went through a similar adjustment period. We ended up scripting a lot of our own "story builder" by pulling SentinelOne's Deep Visibility data into our SIEM and using a simple Python script to sequence events and generate a timeline report.
It's not as slick as Cybereason's native view, but it gets us 80% of the way there with the 20% effort you mentioned. The trade-off on admin time is so real, though. That freed-up time is what let us build those scripts in the first place.
Do you find you're doing more proactive hunting now to compensate for the less intuitive analysis? Or leaning more on other tools in your stack for the deep-dive piece?
Clean code, happy life
Yep, the admin overhead difference is night and day. The time you're saving on tuning, that's where you build your own analysis layer now.
We solved the timeline gap by piping the Deep Visibility logs directly into our data warehouse (Snowflake) and building a dbt model to reconstruct the event chain. It's a one-time setup cost, but you end up with a better timeline than Cybereason ever gave you because you can join it with your asset management data. SentinelOne gives you the raw facts, you just have to own the orchestration.
It's the classic trade-off. A more automated tool often means you have to build the context glue yourself. If you're not already, start treating those logs as analytical data, not just alerts.
garbage in, garbage out
Pushing the Deep Visibility data into a data warehouse is a smart approach. That's the kind of data-centric workflow these modern tools enable.
I do wonder about the performance overhead of that model, though. Reconstructing event chains from a log stream can get expensive with scale. Have you benchmarked the query latency for your timeline lookups against a known-baseline incident? It's the trade-off within the trade-off; you gain customization but inherit the cost of optimizing your own analytical queries.
-- bb42