Best UEBA Software: Top 5 Options in 2026

What Is UEBA Software? 

UEBA (User and Entity Behavior Analytics) software is a cybersecurity solution that uses machine learning and behavioral analytics to detect threats, including insider threats and account takeovers, by establishing a baseline of normal user and entity behavior and then flagging deviations that indicate risk. 

Key benefits include advanced threat detection, reduced alert fatigue through risk-based prioritization, and lower organizational risk by enabling early detection of both known and unknown threats. 

How UEBA software works: 

• Context and storytelling: UEBA platforms often provide context and build “timelines” of user activities to help security teams understand and articulate threats, improving investigative efficiency.
• Baseline establishment: The software learns and creates a profile of normal behavior for individual users and system entities (e.g., devices, servers). 
• Data ingestion and analysis: It collects and analyzes vast amounts of data from various security sources, including logs and events. 
• Anomaly detection: It continuously monitors current activities, comparing them against established baselines to identify suspicious or anomalous behaviors. 
• Threat prioritization: Alerts are triggered for high-risk activities, reducing false positives and helping security teams focus on critical threats. 

How UEBA Software Works 

Establishing Baselines

The UEBA solution must first establish behavioral baselines for users and entities. These baselines are derived by continuously monitoring activities such as login times, file accesses, resource consumption, and communication patterns over an initial learning period. By analyzing these actions, the system constructs a profile of what is considered typical behavior for each user and device in the environment.

Once these baselines are in place, the software regularly updates them to account for normal changes in behavior, such as job role modifications or seasonal business fluctuations. By keeping the baselines current, UEBA increases detection accuracy and minimizes false positives, ensuring that only genuinely unusual and risky activities trigger investigation or intervention from the security team.

Data Ingestion and Analysis

UEBA relies on ingesting data from a vast range of sources across the organization, including system logs, network traffic, authentication records, and cloud application activity. This data input feeds into the core analytics engine, providing the foundation for comprehensive behavior analysis. Modern UEBA solutions integrate with security information and event management (SIEM) platforms or directly with endpoints and network devices to maximize visibility.

Once the data is collected, the analysis component applies algorithms that sort, correlate, and prepare the information for behavioral analytics. Sophisticated data analysis allows the solution to compare events across different times, departments, and device types, uncovering subtle patterns or interactions that might not be evident from a single dataset.

Anomaly Detection

By referencing established baselines, the system continuously evaluates live data for unusual activity patterns such as off-hour logins, sudden permission changes, or unexpected data movement. These deviations are flagged as anomalies, prompting further investigation by security teams.

Not all anomalies are malicious, so UEBA systems use statistical modeling and machine learning to classify and score the detected anomalies. Contextual factors, such as recent role changes or maintenance activity, are considered to reduce noise and improve detection relevance. Over time, the software refines its anomaly detection models to better distinguish genuine threats from benign outliers, resulting in more actionable alerts.

Threat Prioritization

After anomalies are detected, UEBA assigns risk scores to each suspicious event, prioritizing those that pose the greatest threat. This scoring is based on several factors: the type of anomaly, the criticality of the affected assets, and the potential business impact. The result is a list of prioritized threats, allowing security operations centers (SOCs) to focus resources on the most significant risks rather than sifting through a high volume of alerts.

By correlating incidents and analyzing historical context, UEBA systems can also track the progression of advanced attacks. Prioritization mechanisms often rely on artificial intelligence to recognize escalation patterns, such as lateral movement and privilege escalation. This approach ensures critical events are addressed promptly.

Context and Storytelling

UEBA enhances traditional alerting by providing context and building incident narratives, often referred to as “storytelling.” Rather than sending isolated alerts, the software correlates multiple events over time, such as a series of unusual logins, followed by data exfiltration attempts, to craft a timeline of threat actor behavior. This contextual approach provides security teams with actionable intelligence, making investigations quicker and more efficient.

These incident narratives help differentiate between isolated anomalies and larger coordinated attacks. With a clear story that ties actions together, responders gain a broader view of the attack chain, improving both their understanding and response to threats. Effective storytelling allows analysts to reduce time spent on manual data correlation and prioritize actions based on the complete threat picture.

Notable UEBA Software

1. Exabeam

Exabeam is a behavioral analytics–driven SIEM platform that delivers advanced UEBA capabilities to detect insider threats, credential misuse, and lateral movement across hybrid and cloud environments. It combines scalable data collection, risk-based analytics, and automation to enhance visibility and speed across detection and investigation workflows.

General features:

•  Data ingestion and normalization: Collects and standardizes logs from identity systems, endpoints, cloud applications, and network devices for unified visibility and correlation.
•  Automated investigation timelines: Builds contextual, time-ordered narratives of user and entity activity to accelerate investigations and reduce manual triage.
• Integrated automation: Uses SOAR-driven playbooks to orchestrate containment and remediation actions, reducing analyst workload and response time.

UEBA features:

• Peer group analysis: Compares user activity against department- or role-based peer groups to surface subtle deviations that indicate compromised or malicious insiders.
• Behavioral analytics engine: Establishes dynamic baselines for users, devices, and service accounts to detect anomalies such as privilege escalation, data exfiltration, or unusual access patterns.
• Risk-based scoring and prioritization: Assigns weighted risk scores to anomalies using contextual signals, enabling analysts to focus on the most relevant and high-impact threats.
• Entity correlation and context building: Links related user, endpoint, and network activities into a single behavioral storyline for more accurate detection of complex threats.

2. Microsoft Sentinel

Microsoft Sentinel is a cloud-native SIEM that scales across multicloud and multiplatform environments. It combines analytics, automation, and threat intelligence for data collection, detection, investigation, hunting, and response through the Microsoft Defender portal.

General features:

• Data collection at scale: Ingest data from users, devices, apps, and infrastructure using built-in connectors, CEF, Syslog, or REST API integrations.
• Normalization and workbooks: Normalize disparate logs via ASIM and use built-in workbook templates to visualize insights immediately after connecting data sources.
• Analytics and incident grouping: Reduce alert noise using analytic rules that correlate signals and group alerts into higher-fidelity incidents for focused triage.

UEBA features:

• Proactive hunting capabilities: Hunt across data with MITRE-aligned queries; promote findings into custom detections that generate alerts tied to observed behaviors.
• Anomaly-focused analytics: Use analytics that map behavior and detect anomalies across resources to surface previously undetected threats with fewer false positives.
• Entity-centric investigations: Investigate incidents with an interactive entity graph to trace relationships, scope impact, and drill into connected activity efficiently.

Source: Microsoft

3. Splunk

Splunk User Behavior Analytics (UBA) is a machine learning-driven solution to detect insider threats and advanced attacks by analyzing behavior across users, devices, and systems. It builds multi-dimensional baselines and peer group profiles to identify deviations that signal compromised accounts, lateral movement, or data exfiltration. 

General features:

• Noise reduction: Automatically distills massive event volumes into a focused set of threats, minimizing false positives and alert fatigue.
• Integrated investigations: Pushes detected threats into Splunk Enterprise Security for centralized incident management and simplified workflows.
• Minimal manual tuning: Requires low administrative overhead by using dynamic behavior models instead of relying solely on manually created rules.

UEBA features:

• Unsupervised machine learning: Learns normal behavior patterns for users, devices, and applications, then identifies anomalies like account misuse, data theft, or lateral movement.
• Threat context and visualization: Provides graph-based analysis and kill chain mapping to show root cause, scope, and timeline of attacks, enabling faster, informed decisions.

Source: Splunk 

4. Rapid7

Rapid7 Incident Command improves threat detection and response by combining user behavior analytics with attacker-focused detections and deception techniques. Unlike traditional alert systems that focus only on IP addresses, Rapid7 ties activity back to users and assets, making it easier to validate and investigate threats. 

General features include:

• Integrated intruder traps: Uses deception technologies (e.g., honeypots, honey credentials) to detect malicious activity and generate unique threat signals.
• Ecosystem visibility: Provides coverage from endpoint to cloud, reducing the need to switch between tools or manually stitch together logs.
• Cloud-native architecture: Handles large volumes of machine data without requiring teams to manage infrastructure or storage growth.

UEBA features include:

• Context-rich alerts: Automatically correlates user behavior with asset activity to give analysts clear insight into what happened, where, and who was involved.
• Attacker-centric detection: Goes beyond basic anomaly detection by focusing on known attacker behaviors like phishing, credential theft, and lateral movement.

Source: Rapid7 

5. ManageEngine Log360

ManageEngine Log360 is a unified SIEM solution that strengthens security operations with integrated user behavior analytics, automated response, and visibility across hybrid environments. It combines AI-driven detection, correlation engines, and contextual investigation tools to help security teams to detect insider threats, account compromise, and attack patterns.

General features include:

• Contextual investigation tools: Centralized Incident Workbench consolidates telemetry from sources like Active Directory, providing visual timelines and guided analysis.
• Dark web monitoring: Detects leaked credentials and exposed data in dark web sources, enabling proactive breach prevention.
• SOAR capabilities: Automates incident response workflows through pre-defined playbooks, freeing up analysts and reducing response time.

UEBA features include:

• Automated TDIR: Vigil IQ automates detection, investigation, and response using AI, correlation rules, and incident timelines to accelerate threat resolution.
• AI-driven behavioral analytics: Uses UEBA with dynamic peer grouping and user identity mapping to detect anomalies indicating insider threats or credential compromise.

Source: ManageEngine 

Related content: Read our guide to UEBA tools

Conclusion

UEBA software strengthens security operations by focusing on behavior rather than static rules or signatures. By continuously learning what is normal and flagging deviations, UEBA detects insider threats, account compromises, and advanced attacks that bypass traditional defenses. Its ability to prioritize risks and provide contextual timelines allows security teams to respond faster and more accurately, reducing organizational exposure to both known and unknown threats.