UEBA Use case

Traditional UEBA (User and Entity Behavior Analytics) (Exabeam, Securonix, Gurucul) is a separate product bolted onto your stack. You ship the same logs to a second platform, wait 30-90 days for baselines to build, then get opaque risk scores from a black box ML model that nobody can explain or tune. Most orgs stop trusting it within a year. It becomes shelfware.

How spotr.io does it?

Behavioral detection is built into the streaming engine — not a separate product. Statistical models (zscore, deviation, trend, anomaly, new_term, rarity) run continuously on the stream alongside every other detection type. No 90-day learning period. No duplicate data ingestion. No extra license.

Every behavioral detection is transparent and tuneable — your team can see what triggered, why, and adjust the thresholds. It's one layer in the detection hierarchy, not a standalone oracle.

The AI SOC Analyst correlates behavioral signals with threshold, sequence, and filter detections to build full-context signals. "This user deviated because X, correlated with Y" — not "risk score: 87."

The Conversation

"How long did it take your UEBA to start producing results you trust?" — Most will say months, or admit they never got there.

"Can your analysts explain why a user was flagged?" — If they can't, they can't triage it.

"What if behavioral detection was built in, transparent, and producing results on day 1?" — That's spotr.io.