What is UEBA (user and entity behavior analytics)?

User and entity behavior analytics (UEBA) is a security approach that analyzes user and entity behavioral patterns to detect potential threats.

Key Takeaways

• UEBA tracks subtle changes in current behavior from established baselines to identify anomalies and uncover suspicious activity.
• It monitors both human users and non-human entities like devices and applications to provide comprehensive security coverage.
• UEBA security uses advanced analytics and machine learning (ML) to identify threats that traditional security tools might miss.
• Organizations employ UEBA to reduce false positives and strengthen threat detection across their network environments.

UEBA fundamentals

Expanded from user behavior analytics (UBA) focused on human users (employees, contractors, customers), UEBA includes entities (devices, applications, networks) that enable threat detection across the entire digital ecosystem. Gartner coined the term in 2015.

While conventional security tools rely on predefined rules and signatures, UEBA establishes behavioral baselines using ML, statistical analysis, and behavioral modeling. Foundational baselines allow continuous monitoring that identifies deviations from normal activity that can indicate security threats, so when a person or device strays from established behavior, it is immediately flagged. 

In remote workflows, UEBA solutions ingest data from endpoints, including those used in remote environments (corporate laptops, mobile devices), by integrating with endpoint detection and response (EDR) and network monitoring tools.

UEBA detects a wide array of digital threats, including distributed denial-of-service (DDoS) attacks, brute force attacks, and insider threats. It can also help quickly mitigate phishing and social engineering scams by detecting slight differences in behavior should human users mistakenly share credentials, click on malicious links, or download infected software.

UEBA aligns with the National Institute of Standards and Technology (NIST) Cybersecurity Framework’s "detect" function by continuously verifying security measures through behavioral monitoring.

How UEBA works

Starting with data collection and analysis, UEBA transforms raw data into actionable security insights. UEBA solutions collect data across the IT infrastructure. Data sources include authentication systems and Identity management platforms, network traffic flows, application and access logs, endpoint activity, cloud service logs, and email metadata and logs.

Baseline pattern structure

UEBA systems use collected data to designate behavioral baselines that define “normal” patterns for individuals (human, entity), peer groups (job roles, functions), and overall organizational norms.

Factors used to set baselines include access patterns (when, where, and how frequently resources are accessed), transaction volumes and types, communication patterns, and resource usage (applications, data, systems).

• Anomaly detection

UEBA uses advanced analytics while continuously monitoring for pattern deviations leveraging statistical analysis, ML algorithms, behavioral modeling, and peer group analysis.

UEBA detects variations from the norm across access patterns or login times, data access or exfiltration, logins from unexpected geographic locations, communication patterns, application use, and lateral movement within networks.

• Risk scoring

Risk scores are dynamic and context-aware. They often combine multiple anomalies into a single risk event to reduce alert fatigue. 

UEBA allows security teams to prioritize threats with contextual risk analysis that measures pattern deviation severity, resource sensitivity, incident history, and activity context.

• Alert generation and response

UEBA can be integrated with security orchestration, automation, and response (SOAR) platforms to streamline investigation and response. When significant anomalies are detected, alerts provide detailed context and forensic evidence and can trigger automated response actions.

UEBA helps organizations strengthen their security posture by evolving toward a more adaptive, intelligence-driven approach to security.

Common UEBA use cases

Insider threat detection: UEBA can detect malicious insiders and compromised credentials through data access or data exfiltration attempts, including resources accessed outside typical job functions, dormant account activation, behavior changes preceding employee departure, and privilege escalation or abuse.

Account compromise detection: Even when an attacker has valid credentials, UEBA can identify compromised accounts. UEBA systems are attuned to logins from unusual locations or devices, abnormal access times or durations, changes in typing patterns or command usage, unusual navigation through applications or systems, and resource access not typically used by the account owner.

Advanced persistent threat (APT) detection: UEBA helps detect APTs by identifying unusual lateral movement within networks, reconnaissance activities, data staging before exfiltration, command and control communications, and persistence mechanisms.

Fraud detection: UEBA identifies fraudulent activities by detecting unusual financial transaction patterns, suspicious account modifications, abnormal customer service interactions, policy violations in financial systems, and changes in transaction behavior.

Compliance and audit support: UEBA creates audit trails that map user and entity activities, documenting access to regulated data, supporting forensic investigations, demonstrating security control efficacy, and proving regulatory compliance.

UEBA benefits and challenges

Key benefits

• Enhanced threat detection

UEBA improves the ability to detect sophisticated threats that sidestep traditional security controls, such as zero-day attacks, threats without predefined signatures or rules, and attacks that develop slowly over time by providing visibility into the entire attack lifecycle.

• Reduced false positives

UEBA reduces false positives by correlating multiple behavioral signals over time, focusing on patterns rather than isolated anomalies. It applies contextual risk scoring to prioritize significant threats and distinguishes between malicious activity and legitimate but unusual behavior.

• Improved investigative capabilities

UEBA provides security analysts with detailed context for more effective investigations by providing comprehensive timelines of user and entity activities, correlating related events across multiple systems, and conveying graphic representations of behavioral anomalies.

• Adaptability to changing environments

Unlike rule-based systems that require constant updates, UEBA adapts to evolving environments by continuously refining behavioral baselines, adjusting to organizational changes and new systems, accommodating user role and function updates, and learning from investigation outcomes to improve future detection.

Implementation challenges

• Data quality and availability

UEBA effectiveness goes hand-in-hand with data quality, requiring comprehensive logging across multiple systems, consistent data formats, and accurate user and entity identification.

• Baseline establishment period

Developing reliable behavioral baselines can take weeks or months of accurate data collection and can be affected by factors like seasonal variations.

• Expertise requirements

Implementing and managing UEBA requires professionals trained in data analysis, security, integration, maintenance, and optimization, with additional expertise in tuning UEBA tools.

• Integration complexity

Integrating UEBA with existing security infrastructure can be challenging. It may require new data collection methods, must align with security operations workflows, and needs to integrate with other tools.

UEBA implementation best practices

1. Define clear objectives: Identify specific security challenges, define success criteria and metrics, and establish realistic expectations for implementation timeframes.
    
2. Engage cross-functional teams: Plan for cross-functional alignment between security, IT, and compliance teams to ensure seamless data access, integration, and governance.
    
3. Ensure comprehensive data collection: Identify relevant data sources, provide complete and consistent logging, validate data quality and completeness, and address gaps in existing data collection.
    
4. Start with high-value use cases: Begin with the most critical security use cases, focus on areas with the highest potential risk, and demonstrate value through early successes.
    
5. Integrate with existing security operation workflows: Align alert routing with response procedures, educate and train security teams, and establish clear escalation paths for detected anomalies.
    
6. Continuously optimize and refine: Review and adjust detection thresholds, analyze false positives, update baselines as organizational changes occur, and incorporate feedback from security analysts.

UEBA vs. Identity and access management (IAM)

UEBA vs. endpoint detection and response (EDR)

UEBA vs security information and event management (SIEM)

User and entity behavior analytics FAQs

• How is UEBA different from traditional security tools?

Traditional security tools rely on known signatures, predefined rules, or pattern matching. UEBA establishes baselines of normal behavior and identifies anomalies that deviate from these patterns, allowing UEBA to detect unknown threats and sophisticated attacks missed by conventional controls.

• What’s the difference between UEBA and ITDR?

UEBA focuses on monitoring how identities are being used (and potentially misused) across the environment, while Identity threat detection and response (ITDR) specifically watches for direct attacks on the identity systems that manage those identities. They are highly complementary, with insights from one often enriching the analysis of the other, which contributes to a stronger overall Identity security posture.

• How does UEBA reduce false positives?

UEBA can distinguish between genuine security threats and unusual but legitimate activities by understanding normal behavior patterns and applying risk scoring. This contextual awareness significantly reduces false positives compared to rule-based security tools.

• Does UEBA replace other security technologies?

UEBA complements but does not replace other security technologies. It works most effectively as part of a comprehensive security strategy, including preventative controls, network security, endpoint protection, and other detection capabilities.

• How long does it take to implement UEBA?

Establishing reliable behavioral baselines requires several weeks to months of data collection, depending on the complexity of the environment. Some detection capabilities are realized more quickly than others. The benefits of UEBA materialize as the system learns normal behavior patterns over time.

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