User and Entity Behavior Analytics Tools 2026
User and Entity Behavior Analytics (UEBA) tools learn a baseline of normal behavior for every user, host, service account, and device, then flag the deviations signature-based controls miss: a credential logging in from two countries within an hour, a service account suddenly touching file shares it never reads, an employee bulk-downloading before resigning. The pitch is risk scoring over binary alerts, so analysts triage by who is acting unusually instead of drowning in raw events. CISOs turn to UEBA when insider threat, account takeover, and compromised credentials are the real exposure and hand-written rules have stopped scaling. Most modern UEBA lives inside a SIEM or XDR, though standalone and DLP-adjacent options persist for specific needs.
We cover 16 User and Entity Behavior Analytics tools, 1 free and 15 commercial.
Accuracy and depth improve over time. Last reviewed Sep 2026. Is something off? Reach out.
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New to this category? What is User and Entity Behavior Analytics (UEBA)?
Passive AI voice biometric authentication for contact center calls, no PINs or questions
Patented ML-based behavioral analytics engine for CI/CD & cloud risk detection.
ML-based SaaS user behavior monitoring and automated threat remediation.
AI-powered workflow monitoring new user creation & privilege escalation.
ML-powered anomaly detection and UEBA for server and container workloads.
Identity risk engine that analyzes user behavior and correlates security data
AI-powered fraud protection platform for banking, payment, and e-commerce
UEBA solution using ML and big data to detect APTs via behavior profiling
UEBA solution detecting anomalous user/entity behavior via ML models & risk scoring
User behavior analytics & audit solution for insider threat detection
ML-based UEBA detecting insider threats via behavioral anomaly detection and risk scoring.
Expert advisory service for interpreting user behavior analytics data
Real-time ransomware detection & blocking for storage systems with recovery
SIEM UEBA platform with AI for threat detection, hunting, XDR and SOAR
User behavior and access analytics platform with AI-powered insights
How to choose User and Entity Behavior Analytics tools
- Map the entities you must baseline first: users, service accounts, hosts, OT devices. Tools differ sharply in how well they model non-human identities, which is where many attacks now live.
- Check data-source coverage against your stack. Strong UEBA needs identity, endpoint, cloud audit, and data-access telemetry, and a tool that ingests only one of these gives you a partial baseline.
- Test false-positive rates on your own data, not the vendor demo. Behavioral models only help if analysts trust the scores, so run a pilot and measure noise before and after the baselining period.
- Demand explainability. A risk score with no narrative of why an entity is anomalous slows triage; look for peer-group context, contributing-factor breakdowns, and a clear timeline.
- Settle standalone versus built-in early. If your SIEM or XDR already ships UEBA, justify a separate product with a concrete coverage gap rather than paying for duplicate ingestion and storage.
- Confirm it closes the loop. Detection without response is half a tool, so verify clean integration with your SOAR, ITDR, or case management for investigation and containment.
User and Entity Behavior Analytics Tools FAQ
Common questions about User and Entity Behavior Analytics tools, selection guides, pricing, and comparisons.
UEBA is a detection approach that models the normal behavior of users and non-human entities like service accounts, hosts, and devices, then scores deviations from that baseline as risk. Rather than matching known attack signatures, it surfaces anomalies such as unusual login locations, abnormal data access, or privilege misuse. It is built to catch insider threats, account takeover, and compromised credentials that rule-based detection tends to miss.
A SIEM collects and correlates logs against rules you write; UEBA layers statistical and machine-learning baselining on top to detect behavior no rule anticipated. The two are complementary, and most UEBA capability now ships inside a SIEM or XDR rather than as a separate product. If you already run a SIEM, evaluate whether its native UEBA module is enough before buying a standalone tool that duplicates ingestion and storage.
Begin with the entities and data sources you actually need to baseline: identity logs, EDR, cloud audit trails, file activity, network flows. Then test how the tool scores and explains risk, how long baselining takes, and how noisy it is out of the box. Press hard on false-positive rates with your own data, integration with your SIEM or SOAR for response, and whether peer-group analytics and explainability are real or marketing.
For most teams, the UEBA already bundled into your SIEM or XDR is the sensible starting point, since it reuses existing telemetry and avoids a second ingestion bill. Standalone UEBA earns its place when you need specialized coverage like deep insider-threat monitoring, endpoint-level user activity, or data-centric anomaly detection your platform handles poorly. Match the tool to the specific entity and data gap rather than buying behavioral analytics as a generic checkbox.
UEBA targets the threats that hide inside legitimate access: an insider exfiltrating data before they leave, a phished credential wielded by an attacker, a privileged or service account behaving abnormally, and lateral movement that reads as normal traffic to a rule engine. By scoring entities on cumulative anomalous behavior, it helps analysts prioritize the few risky actors instead of triaging thousands of low-context alerts.
