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.
Oh man, you are singing my song with that re-org scenario. We got absolutely slammed with alerts during a major shift to hybrid work last year. The "new normal" for peer groups was just chaos for a solid week.
It's not just about model retraining cadence, though that's huge. We learned the hard way that you need a proactive change management feed into Exabeam, if you can swing it. When HR's identity system updates a user's department or title, we try to get that into the system *before* the user starts their new role. It gives the peer groups a head start on regrouping.
But honestly, sometimes you just have to bite the bullet and temporarily whitelist certain expected anomalies during a known transition. It's better than burning out your SOC team. Has your team looked at integrating with an HR feed, or is that still a pipe dream?
Backup first.
Love that finance example - it really makes it click. Your SSH scenario at 2 AM is perfect, but the cool part is how it catches subtle things too.
Like, what if Sarah from Finance *does* access the dev server, but at 10 AM on a Tuesday? Still weird for her peer group, just a lower risk score than the 2 AM attempt. The system weights timing, frequency, and sequence all together.
It's that baseline of "normal for this exact bunch of people" that makes outliers so clear. A marketer logging into the CMS is invisible, but a single finance person doing it? That's a story.
That weighting of timing and frequency is key. In my old role, we saw a similar thing with invoice approvals. A manager approving a batch at month-end was normal. That same person approving a single, large invoice on a weekend stood out, even though the system access was legitimate. It wasn't about the action itself, but the context of their peer group's rhythm.
How granular does the weighting get? Like, does it consider that "normal work hours" might be different for a finance team closing the books versus a support team on shift work?
The proactive HR feed integration is a fantastic goal, but in practice, it's rarely enough on its own. Even with perfect department data flowing in, the behavioral model for a new peer group has to be built from observed activity. You've given the system a better label, but the baseline of "normal" for that newly-labeled group is still zero.
What we found more effective was creating temporary, broader peer groups during known transitions. Instead of forcing the model to immediately distinguish between, say, "Finance, Accounts Payable" and "Finance, Financial Planning," we'd let it learn under a wider umbrella like "Finance - All" for a defined period. This reduces the noise because the acceptable behavior variance is larger initially. You can then gradually narrow the peer group definition as activity stabilizes.
Your point about whitelisting expected anomalies is crucial, though. It's a pragmatic admission that even advanced UEBA can't model intent. If you know a re-org will cause a wave of legitimate but unusual file transfers between new team members, temporarily allowing that pattern is responsible alert management. The alternative is training your team to ignore alerts, which defeats the entire purpose.