After running QRadar's SIEM for about two years, we finally got budget approval to pilot the User Behavior Analytics (UBA) module. I'll be honest, my initial thought was, "Isn't this just fancy correlation rules I could build myself?" 🤔
I set up a 90-day proof-of-concept, meticulously mapping the data flows from our AD, VPN, and critical apps. The goal was to see if UBA would surface genuine insider threats or compromised accounts that our standard SIEM alerts missed.
Here's what I found it actually caught:
* **Lateral movement by a service account:** Our SIEM saw successful logins from a jump box, but UBA flagged the timing, source, and accessed resources as a significant deviation from the account's 3-year baseline. It was a pentest we authorized, but the detection was valid.
* **Data exfiltration pattern:** An engineer downloading massive volumes of source code to a personal cloud drive. Individual events were logged, but URadar didn't connect them into a "hoarding" pattern. UBA built a risk score over a week and alerted.
* **Geographically impossible travel:** A legit user account showing logins from two countries within an hour. Our SIEM rules had a static, overly permissive threshold (e.g., "different countries within 6 hours"). UBA's machine learning model considered the user's typical locations, travel schedule, and the specific endpoint—fewer false positives.
The module isn't a silver bullet. It requires clean, normalized identity data to work well, and the initial learning period needs careful monitoring. But for us, it moved the needle from "alerting on known-bad signatures" to "highlighting risky behavioral anomalies." It caught subtle, multi-stage activities that our team's custom rules simply weren't sophisticated enough to model.
Has anyone else done a similar comparison? I'm particularly curious about its effectiveness with cloud identity providers (like Azure AD) versus traditional on-prem AD.