Hey everyone, I'm trying to wrap my head around UEBA solutions for a project at work. We're currently evaluating Microsoft Sentinel and Exabeam for user behavior analytics, specifically focusing on their out-of-the-box detection capabilities.
I've been going through the documentation for both, but it's a bit hard to gauge the practical differences. For Sentinel, I see you can enable the "UEBA" blade and it starts building behavioral profiles. For Exabeam, their "Advanced Analytics" seems to be the core offering. My main questions are:
* Which platform has more comprehensive **pre-built behavioral models** for things like impossible travel, data exfiltration, or insider threat? I don't have the bandwidth right now to build a ton of custom KQL queries from scratch.
* How does the **data source integration** compare for building these baselines? We have a mix of Azure AD, on-prem AD via Azure Connect, and Office 365. Is one notably easier to get going with those?
* From a learning perspective, how much tuning is typically needed after enabling these features? I've seen a few Sentinel KQL queries for anomalies, like this basic one for a spike in failed logons:
```kql
SecurityEvent
| where EventID == 4625
| summarize FailedCount = count() by Account, bin(TimeGenerated, 1h)
| where FailedCount > 10
```
But I'm curious if Exabeam's model-driven approach requires less of this manual query work initially.
Any insights from your experiences on which gets you to useful, actionable alerts faster would be super helpful. Thanks!
Hey user219, I've been down this exact road at my last shop, a mid-sized SaaS company with about 1,200 users. We ran a hybrid stack with Azure services, on-prem AD synced via Azure Connect, and O365. I evaluated both for UEBA, and we ended up running Sentinel in production with the UEBA module for about 18 months.
Here's my breakdown from that hands-on experience.
* **Out-of-the-Box Models: Breadth vs. Depth**
Sentinel gives you a wide net of 70+ built-in analytics rules tied to UEBA, but many are essentially KQL templates that still need specific data sources. Exabeam's models are narrower but deeper, with more polished behavioral baselines for things like lateral movement or data staging that feel more "finished." If you want less initial config, Exabeam's models felt more immediately useful.
* **Azure-Native Integration Effort**
With your stack, Sentinel is a clear win on setup time. Enabling the UEBA blade and connecting your Azure AD, Azure Connect, and O365 data is a few clicks. It starts building baselines automatically within 24-48 hours. Exabeam required more pipeline work to get those same logs parsed correctly for their engine, adding maybe 2-3 weeks of tuning.
* **Ongoing Tuning & Noise**
Sentinel's anomaly alerts, like "impossible travel" or "unusual file download," generated about 5-10 low-fidelity alerts per week for our 1,200 users, requiring regular review to filter false positives. Exabeam's session-based approach produced fewer, but higher-fidelity, incidents per week (maybe 2-3), but the initial setup demanded more upfront input on what constituted a "session" for our environment.
* **Real Pricing and Scale**
Sentinel UEBA costs us roughly $2.30 per GB of data analyzed by the UEBA engine *on top of* the base ingestion cost, which added about 30% to our bill. Exabeam's quote was a flat annual fee based on user count, which came out to ~$85k/year for us. For predictable budgeting, Exabeam was simpler, but Sentinel scaled directly (and sometimes unpredictably) with data volume.
For your described use case with Azure AD, Azure Connect, and O365, and your need for pre-built models without heavy KQL work, I'd actually lean towards **Exabeam** if budget allows. Its models are more immediately actionable. However, if you're already heavily invested in the Azure security ecosystem and need something running this week, **Sentinel** is the pragmatic choice. To make a clean call, tell us your monthly log volume feeding into UEBA and how many dedicated SOC hours you have weekly for tuning.
Dashboards or it didn't happen.
I've been looking into this exact question myself for our migration project. The integration piece is actually what's making me lean one way.
> We have a mix of Azure AD, on-prem AD via Azure Connect, and Office 365.
If your stack is already that Microsoft-heavy, getting Sentinel's UEBA to start building baselines from those sources was surprisingly fast for us. We turned on the blade and it just started ingesting from our tenant. The real question I've run into is, what's your data quality like on the on-prem side? We had some gaps in our AD Connect logs that made a few of the behavioral models less effective until we fixed the logging.
Did you find Sentinel's baselines required a lot of immediate tuning with your specific user patterns, or did they feel useful pretty quickly after the initial learning period?
One step at a time
Great question on the data source piece. That's the deciding factor for us, honestly.
We run a very similar stack, and Sentinel started building usable baselines from Azure AD and O365 logs within about 48 hours of turning it on. The on-prem AD Connect logs are the real variable. If your logging is consistent, it works. If it's patchy, you'll get blind spots in the behavioral profiles for those hybrid users. We had to clean up a few of our DC audit policies before the 'impossible travel' model for on-prem logins worked reliably.
On your KQL point, the pre-built analytics rules are templates. You *can* run them as-is, but to get real value, you'll still need to adjust thresholds for your environment. It's less 'tuning the model' and more 'tuning the alert rule'. Exabeam felt more black-box - the model itself adjusted, but we had less visibility into the why.
Show me the pipeline.