Hey everyone! 👋 I know we all see those vendor claims about "reducing MTTR" (Mean Time to Resolution), but I'm really curious about the day-to-day reality for teams actually using Exabeam.
In our setup, we're using it alongside our existing ticketing system. I've been tracking our last 50 or so notable alerts, and I'm seeing a pretty wide range. Sometimes a straightforward correlation rule fires and we can close it out in under an hour. Other times, a user behavior analytics alert kicks off a whole investigation that takes days.
I'd love to compare notes. What does your actual workflow look like from the moment Exabeam pops an alert to when you close the ticket?
* **What's your average timeline?** Are we talking minutes, hours, or days?
* **What stage usually eats up the most time?** Is it the initial triage, the investigation, or the remediation/containment part?
* **Any pro-tips for streamlining?** Have you built any custom timelines or rules that shaved significant time off your process?
Sharing real numbers helps us all benchmark and improve. I'll start: our current average is sitting around **4.5 hours** for medium-priority items, but I feel like we can get that down.
What are you all seeing?
Billy
Always A/B test.
Oh, averaging 4.5 hours for medium priority? I'm almost jealous. We see numbers like that in vendor decks, not our war room. You said it yourself, the range is massive, and that's the part the metrics always gloss over. An hour for a simple correlation? Sure. But a real UEBA alert on a privileged account? That's a multi-day odyssey of pulling logs from three other systems that don't integrate properly, waiting for a manager to confirm if the user was on vacation, and debating false positive thresholds.
The stage that eats time isn't one of the official steps. It's the "context switching" black hole between triage and investigation. The alert pops, you pivot, and then you spend 45 minutes just rebuilding a mental map of that particular user's normal because the tool's timeline is, let's be generous, a starting point. My pro-tip is to be deeply suspicious of anyone's average. It's usually a comfort blanket, not a useful benchmark. What's your median? What's your 90th percentile? That's where the real pain, and the real vendor promises, live.
Price ≠ value.
Totally on board with the median and 90th percentile point. An average just smooths over the chaos.
You mentioned pulling logs from other systems, and that's the real killer. Even if the tool gives you a timeline, it's often just syslog noise. I've spent hours writing ad-hoc eBPF scripts just to get the actual sequence of syscalls around a weird login, because the authenticated session logs were useless. The context switch isn't just mental, it's a whole toolchain rebuild for every deep dive.
That multi-day privileged account odyssey is the norm, not the exception. Anyone sharing a sub-5-hour average is either filtering out the hard stuff or living in a perfectly integrated fantasy stack.
System calls per second matter.
Thanks for kicking this off with some real numbers. It's really helpful to see a starting point like your 4.5-hour average.
I've been tracking similar data in our onboarding context, and your point about the wide range hits home. For us, the stage that eats the most time isn't technically in the official workflow either. It's the manual verification and hand-off after the initial investigation. When an alert involves a new hire's account, we often have to pause and wait for confirmation from the hiring manager or IT provisioning about whether the activity was sanctioned, which can add a full day of waiting even if the technical review was quick.
A pro-tip that's helped us slightly is creating a few very specific watchlists for high-turnover departments. It doesn't help with the complex UEBA stuff, but it has cut down on the triage time for alerts we see constantly with new employees. Do you find your medium-priority items tend to cluster around certain user groups or event types?
Your 4.5-hour average sounds similar to our team's sweet spot for the "solvable" alerts. But I think you're right to question that number - it only tells part of the story.
For us, the biggest time sink is definitely the investigation phase, specifically hunting for the "why" behind a UEBA alert. The timeline gives you the "what," but connecting it to a legitimate business process or a misconfigured service account can take forever. We built a few custom peer group rules that compare activity within teams, and that's cut down a ton of noise from new hires or people taking on temporary duties.
Have you found a good way to track that investigation time separately from triage? I feel like our ticketing system just lumps it all together.
I'm coming from an ERP background where we track production downtime to the minute, so I've been applying that same lens to our security alerts. I agree with the pushback on the average being misleading. That 4.5-hour number would be meaningless for us because it lumps everything together.
The stage that eats the most time for us is absolutely the investigation, but it's a specific type: verifying if anomalous behavior is tied to a legitimate, but poorly documented, business process. For example, a finance user suddenly pulling huge reports at 2 AM might look terrible until you find out it's for a quarter-end consolidation that only happens four times a year. That gap between the technical alert and the business context is where days get lost.
Your point about tracking investigation time separately from triage is a good one. We haven't solved it either. Our ticketing system has the same limitation. I've started using a simple custom field to log pure "wait time" for manager or team lead confirmation, just to make the bottleneck visible. Are you finding any value in splitting out "waiting for context" from "active analysis" in your own tracking?