UEBA Tools: Key Capabilities and 8 Tools You Should Know

What Are UEBA (User and Entity Behavior Analytics) Tools?

UEBA tools are software systems that use machine learning and statistical methodologies to identify anomalous behavior or instances within a network. These tools are responsible for some of the earliest uses of artificial intelligence (AI) in security operations. They operate by analyzing and learning from historical data to establish a baseline of normal behavior. This baseline is then used to detect deviations or anomalies that could indicate a potential security threat.

These tools do not rely on predefined security rules or signatures. Instead, they use machine learning algorithms to continuously learn and adapt to new patterns and behaviors. This makes them highly effective at detecting unknown threats, such as compromised credentials, zero-day exploits and advanced persistent threats (APTs), which traditional security solutions may miss.

Furthermore, UEBA tools analyze not just user behavior, but also entity behavior. This means they can monitor and analyze the behavior of devices, applications, and network traffic—essentially, any entity that is part of an organization’s digital ecosystem. This holistic approach allows for a much more comprehensive and accurate threat detection and response. 

The leading EUBA tools provide robust detection capabilities, combined with greater insights for investigation and response. They also provide automated incident timelines, which highlight events by risk, along with more dynamic alerting techniques which allow analysts to prioritize the triage of 3rd-party alerts with greater precision.

Editor’s note: Updated the article to cover recent market trends, updated product information to reflect features and capabilities in 2026, and added 1 new tool

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The UEBA Market Trends 

Market Size And Growth Forecast

The User and Entity Behavior Analytics (UEBA) market is expanding rapidly. It is valued at approximately USD 0.41 billion and is projected to grow to USD 14.18 billion by 2035. This represents a compound annual growth rate (CAGR) of 38.0%.

This level of growth reflects increasing demand for advanced security analytics as organizations invest in tools that can detect complex and evolving threats.

Key Market Drivers

Several factors are driving the growth of the UEBA market. A major driver is the rise in cybersecurity threats, which are becoming more frequent and sophisticated. Organizations are adopting UEBA solutions to detect anomalies and respond to threats in real time.

Regulatory requirements are also contributing to demand. Laws related to data protection and privacy require organizations to monitor user activity and ensure compliance. UEBA tools help meet these requirements by providing visibility into user behavior.

The shift to remote work has further increased the need for monitoring across distributed environments. In addition, organizations are focusing more on detecting insider threats, which UEBA tools are designed to identify through behavior analysis.

Role of Generative AI in UEBA

Generative AI is emerging as a significant force shaping the evolution of the UEBA market—both as a driver of new threats and as an enabler of more advanced defense capabilities:

• Generative AI is lowering the barrier to entry for cybercriminals: Attackers can now use AI tools to generate highly convincing phishing emails, polymorphic malware, and adaptive attack strategies in real time. These AI-generated attacks are often novel and do not rely on known indicators of compromise, making them difficult for traditional, rule-based security systems to detect.
• Increasing importance of behavior-based detection models like UEBA: By analyzing patterns of user and entity behavior rather than relying on signatures, UEBA solutions are better equipped to identify previously unseen and evolving threats.
• Generative AI embedded into UEBA platforms: Modern UEBA solutions use AI to automatically transform large volumes of raw security data into structured behavioral insights, helping security teams quickly understand “who did what and why it matters.” In some platforms, generative AI is used to create and scale behavioral models, enrich investigations, and provide explainable, human-readable summaries of security events.

Key Features of UEBA Tools 

Machine Learning (ML) and Analytics

UEBA tools leverage machine learning to analyze vast amounts of data in real-time, enabling them to identify subtle patterns and correlations that may be indicative of a security threat. This represents one of the earliest uses of ML in security operations.

These algorithms can learn from historical data to establish a baseline of normal behavior for users and entities within the network. Any deviations from this baseline are flagged as potential threats. This ability to learn and adapt over time makes UEBA tools highly effective at detecting new and evolving threats.

Moreover, the use of advanced analytics allows these tools to sift through the noise and focus on the most critical threats. They can prioritize alerts based on the severity of the threat and the value of the affected assets.

Data Ingestion and Cross-Vendor Integration

UEBA tools can ingest and analyze vast amounts of data from a variety of sources. These include log files, network traffic data, identity information, and threat intelligence feeds.

Moreover, UEBA tools have robust cross-vendor integration capabilities. This means they can seamlessly integrate with other security tools and systems, creating a unified security infrastructure. Systems typically integrated with UEBA tools are SIEM (Security Information and Event Management), IDS (Intrusion Detection System), and firewalls. The leading UEBA products offer an integrated solution, working together within a SIEM.

Real-Time Monitoring and Alerting

UEBA tools offer real-time monitoring and alerting capabilities. They continuously analyze network activity, allowing them to detect threats as they occur. This is crucial in today’s threat landscape, where threats can proliferate and cause damage in a matter of minutes. Once a threat is detected, UEBA tools can send out alerts in real-time, enabling security teams to respond swiftly and mitigate the threat before it can cause significant damage. In some cases, higher severity alerts can trigger response actions using automation.

Threat Hunting Capabilities

In addition to real-time threat detection, UEBA tools also support threat hunting. This proactive approach to cybersecurity involves searching for threats that may have evaded traditional detection methods. With UEBA tools, security teams can conduct in-depth investigations into suspicious activities earlier in the attack cycle to uncover hidden threats.

These tools provide a wealth of data and analytics that can aid in threat hunting. They can reveal patterns of behavior that may indicate a coordinated attack, identify relationships between different entities that could signify a compromised device, and provide insights into the tactics, techniques, and procedures (TTPs) used by attackers. Some of the leading solutions provide sophisticated threat hunting capabilities, allowing hunters to search across known anomalies uncovered by the UEBA.

Visualization and Reporting Tools

UEBA tools come equipped with visualization and reporting tools. They provide a visual representation of the network and its activity, making it easier for security teams to understand the current threat landscape. They can visualize patterns and trends, identify hotspots of activity, and track changes over time.

Additionally, UEBA tools provide detailed and actionable reporting on the detected threats. They can generate reports on various metrics, such as the number and types of threats detected, the affected assets, the response times, and more. These reports can aid in decision-making and strategic planning, helping organizations enhance their cybersecurity posture.

Automation and Orchestration

Finally, a key feature of modern UEBA tools is their ability to automate and orchestrate various security tasks. Automation allows these tools to execute predefined actions automatically when certain criteria or thresholds are met. For example, if the system detects multiple failed login attempts from a user within a short period, it can automatically lock the account to prevent unauthorized access.

The orchestration capabilities work in tandem with automation to streamline the workflow of security operations. Through orchestration, UEBA tools can interact with other security solutions like firewalls, endpoint protection platforms, and incident response tools. This integration enables a coordinated and rapid response to detected threats. For instance, when a UEBA tool detects unusual data movement that could be indicative of data exfiltration, it can trigger the firewall to block the suspicious IP address involved, all without human intervention.

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Notable UEBA Tools 

There are several UEBA tools available in the market today. Let’s review the key capabilities of six popular options.

Advanced / Enterprise UEBA and SIEM Platforms

1. Exabeam

Exabeam is an AI-driven security operations platform that leverages integrated user and entity behavioral analytics to protect organizations from cyberthreats and advanced attack techniques. This tool uses machine learning and behavioral modeling to identify abnormal activities and alert security teams in real-time based on risk.

Exabeam goes beyond threat visibility, also offering actionable insights that can help you respond effectively to incidents. Its advanced analytics capabilities allow for detailed forensic investigations, making it easier to understand the context of security events. Additionally, Exabeam can integrate with existing security infrastructure, enhancing its efficiency and effectiveness.

The Exabeam platform is cloud-native, designed to be user-friendly, making it suitable for businesses of all sizes and budgets. It offers a streamlined threat detection, investigation, and response (TDIR) workflow, and leverages intelligent automation that enables security teams to respond quickly and effectively to threats.

Learn more about Exabeam Security Analytics

2. Splunk User Behavior Analytics

Splunk User Behavior Analytics (UBA) is a machine learning–driven solution that analyzes behavior across users, devices, and applications to detect insider threats and advanced attacks. It builds behavioral baselines and correlates activity across entities to identify deviations such as credential misuse or lateral movement, while prioritizing risks to support efficient investigation and response.

Key features:

• Behavioral analytics and machine learning: Continuously learns normal behavior patterns to detect subtle anomalies that may indicate insider threats or advanced attacks.
• Entity risk scoring and aggregation: Combines multiple risk signals into a single score per user or entity to prioritize threats.
• Multi-entity correlation: Correlates activity across users, endpoints, and applications to identify complex attack patterns such as privilege abuse and lateral movement.
• Contextual threat insights: Provides enriched metadata, peer comparisons, and historical context to improve investigation accuracy and decision-making.
• Automated threat detection and prioritization: Uses machine learning models to detect and rank threats automatically, reducing alert fatigue and improving SOC efficiency.
• Integration with SIEM workflows: Integrates with Splunk Enterprise Security to unify detection, investigation, and response processes.

3. IBM Security QRadar

IBM Security QRadar SIEM includes user behavior analytics capabilities that provide visibility into user activity and help detect insider threats and anomalous behavior. By correlating data across multiple sources, QRadar enables real-time threat detection, supports threat hunting, and helps security teams prioritize and investigate risks.

Key features:

• User behavior analytics: Monitors user activity to identify anomalous behavior and detect insider threats or compromised accounts.
• Data correlation across sources: Aggregates and analyzes data from multiple systems to provide a unified view of security events.
• Real-time threat detection: Identifies threats as they occur, enabling faster response and mitigation.
• Threat hunting support: Enables analysts to explore datasets and uncover hidden threats using correlated insights.
• Integration and interoperability: Works across a range of security tools and data sources to provide comprehensive visibility.
• Operational efficiency improvements: Reduces manual investigation effort through automation and prioritized insights. 

4. Microsoft Sentinel

Microsoft Sentinel is a cloud-native SIEM platform that integrates UEBA capabilities with AI-driven analytics, automation, and threat intelligence. It collects and correlates data across multicloud and hybrid environments, enabling organizations to detect, investigate, and respond to threats with contextual insights and reduced false positives.

Key features:

• Scalable data collection: Ingests telemetry from users, devices, applications, and cloud environments using built-in connectors and APIs.
• AI-driven detection and correlation: Uses machine learning to identify threats, correlate signals, and reduce false positives.
• Graph-based context and visibility: Provides enriched context through a security graph that maps relationships across entities and activities.
• Integrated UEBA and analytics: Combines behavioral analytics with SIEM and SOAR capabilities for unified detection and response.
• Threat hunting and custom detections: Supports proactive hunting with machine learning–enhanced rules and customizable detection logic.
• AI-assisted investigation: Uses generative AI to summarize incidents, generate queries, and recommend response actions. 

7. Securonix 

Securonix is a cloud-native security analytics platform that integrates UEBA with AI-driven detection, investigation, and response capabilities. It uses behavioral analytics, threat intelligence, and automated workflows to help security teams prioritize risks, reduce alert noise, and accelerate investigations across complex environments.

Key features:

• AI-driven anomaly detection: Identifies abnormal behavior using machine learning with contextual enrichment from identity and threat intelligence.
• Automated alert triage and prioritization: Uses AI to filter noise, rank risks, and surface the most relevant threats.
• Contextual investigation support: Provides enriched insights, summaries, and guided investigation workflows to reduce manual analysis.
• Integrated detection and response: Unifies detection, investigation, and response capabilities within a single platform.
• Threat intelligence integration: Enriches alerts with external intelligence and aligns detections with frameworks like MITRE ATT&CK.
• AI-assisted SOC operations: Uses AI agents to support analysts with decision-making, case preparation, and response recommendations. 

Specialized / Flexible UEBA Solutions

6. Micro Focus Interset UEBA

Micro Focus Interset UEBA is a behavioral analytics solution that uses unsupervised machine learning to detect insider threats and targeted attacks by analyzing user and entity behavior. It combines endpoint data with behavioral intelligence to uncover hidden threats and prioritize high-risk activities for investigation.

Key features:

• Unsupervised machine learning models: Learns normal behavior patterns for users and entities without relying on predefined rules.
• Anomaly detection with behavioral context: Identifies suspicious activities such as unusual logins, impersonation, or abnormal system access.
• Peer group analysis: Compares behavior across similar users or entities to improve anomaly detection accuracy.
• Risk scoring and prioritization: Calculates risk scores to highlight the most suspicious entities and reduce alert fatigue.
• Integration with endpoint data: Leverages endpoint telemetry to enhance visibility and detection capabilities.
• Threat hunting interface: Provides tools to investigate anomalies and track potential attack patterns. 

7. ManageEngine Log360

ManageEngine Log360 is a unified SIEM platform that incorporates UEBA capabilities to detect anomalies, prioritize risks, and automate incident response. It combines log management, AI-driven analytics, and orchestration to improve visibility and simplify security operations across hybrid environments.

Key features:

• AI-driven behavioral analytics: Detects anomalies in user activity using machine learning and adaptive baselines.
• Automated detection, investigation, and response (TDIR): Uses built-in workflows and playbooks to accelerate incident handling.
• Contextual investigation tools: Provides centralized dashboards, timelines, and visualizations for efficient analysis.
• Alert noise reduction: Uses adaptive thresholds and tuning to minimize false positives and improve signal quality.
• Threat intelligence integration: Enriches detections with external threat data and contextual insights.
• SOAR capabilities: Automates workflows and integrates with other security tools for coordinated response. 

Source: ManageEngine

8. Varonis Data Security Platform

Varonis Data Security Platform is a data-centric security solution that incorporates UEBA to monitor and protect sensitive data across cloud, SaaS, and on-premises environments. It focuses on detecting abnormal data access and user behavior while automating responses to reduce risk.

Key features:

• Data-centric UEBA: Monitors user interactions with sensitive data to detect abnormal access patterns and potential threats.
• Real-time detection and response: Provides continuous monitoring and alerts for suspicious activity across data environments.
• Automated risk reduction: Applies policies and remediation actions to limit data exposure and prevent breaches.
• Data discovery and classification: Identifies and classifies sensitive data to improve visibility and protection.
• Cross-environment coverage: Secures data across multi-cloud, SaaS, and hybrid infrastructures.
• Integrated incident response support: Combines UEBA with expert-driven response capabilities for continuous protection. 

Source: Varonis

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The Exabeam Security Operations Platform: The Leader in UEBA

Cloud-scale Security Log Management and SIEM: Securely ingest, parse, and store security data with lightning-fast search, compliance reporting, and dashboards. Combines powerful correlation and threat intelligence with case management. (199/250)

Powerful Behavioral Analytics: Machine learning-based behavioral models increase detection fidelity and automated AI-driven timelines prioritize anomalies based on risk. (138/250)

Automated Threat Detection, Investigation, and Response (TDIR): An automated TDIR workflow uses ML and AI to identify threats, accelerate investigations, and reduce response times with consistent, repeatable results.