Hey everyone! 👋 I keep seeing "peer group analysis" come up as a major selling point for Exabeam, especially when they talk about UEBA (User and Entity Behavior Analytics). But let's be real—the datasheets can get pretty jargon-heavy.
So, let's break it down like you're explaining it to a new sales ops person.
In simple terms, Exabeam watches what *everyone* in your organization does on your systems (logins, file accesses, app usage, etc.). It doesn't just look at one person in isolation. Instead, it automatically groups users who have **similar roles and access patterns**.
Think of it like this:
* The **Finance team** normally accesses the accounting software, shared drives with budget files, and maybe the ERP system.
* The **Engineering team** is constantly in GitHub, JIRA, and AWS consoles.
* **Marketers** live in the marketing automation platform, CMS, and analytics dashboards.
Exabeam learns these patterns for each group. Now, the magic (and the security value) happens when someone starts acting **outside their peer group**.
**Concrete example:** If Sarah from Finance suddenly starts trying to SSH into a development server at 2 AM—something her "peers" in Finance *never* do—that's a huge red flag. Her peer group (Finance) doesn't behave that way, so Exabeam flags it as an anomaly. It could be a compromised account, an insider threat, or just a misconfiguration, but it's worth investigating.
The key is it's **automatic and behavioral**. You don't have to manually define these groups; the system builds them by observing daily activity. It's less about "Sarah accessed File X" and more about "Sarah is doing things none of her teammates ever do."
This is super powerful for spotting threats that bypass traditional rules. A hacker with stolen credentials might bypass a firewall rule, but they'll stick out like a sore thumb when their behavior doesn't match the victim's normal peer group.
Anyone else using this feature? Curious to hear how the peer groups have shaped up in your orgs and if they've caught anything interesting.
—Amy
Exactly! Your example with Sarah is spot on. The real power kicks in when you combine that peer group alert with other anomalies.
For instance, maybe before that SSH attempt, Exabeam noticed she downloaded an unusual volume of files from the shared drive, which also isn't typical for her finance peers. That's when the risk score really spikes - it's the combination of deviations that tells the story, not just one odd event.
We've seen this flag potential compromised accounts way faster than static rules, because it's based on what's normal *for that specific group*. The marketers would never trigger an alert for accessing the CMS, but a developer doing it might.
Let the machines do the grunt work
Spot on about the combination of deviations. That's where the risk score really comes alive.
One thing I'd watch for is when the peer group itself is noisy or changing fast. We saw this during a big re-org - suddenly a bunch of users had new "normal" behaviors and the alerts went a bit wild until the models retrained. It wasn't a false positive per se, but it highlighted that the peer definition needs to be dynamic too.
Got me thinking, how's your team handling alert fatigue from that kind of thing? Just tuning the sensitivity after big changes?
Dashboards or it didn't happen.