What is User and Entity Behavior Analytics (UEBA)?

Venu Shastri - April 25, 2024

User and entity behavior analytics (UEBA) systems monitor an organization’s network, using AI and machine learning (ML) to analyze suspicious activity related to user and endpoint behavior that could indicate a security threat. As modern cyberattacks grow in sophistication, UEBA has become critical for catching the threats that traditional cybersecurity measures miss.

In this article, we’ll explore the key concepts and components of UEBA and the benefits and challenges it brings. Let’s begin by examining the core concepts of UEBA more deeply.

UEBA core concepts

UEBA employs ML and data analytics techniques to establish the typical behavior patterns of users and entities in an organization. By establishing this behavioral baseline, UEBA can then detect abnormal patterns of activity in an organization’s systems and networks. These anomalous behaviors might signify threats such as stolen credentials, compromised entities, or insider attacks.

More generally, behavioral analytics refers to the study of repetitive or significant activities. Within the context of cybersecurity, behavioral analytics focuses on understanding how users and entities normally interact with an organization’s systems. The “users” in UEBA may include an organization’s employees, contractors, and even customers. Meanwhile, the “entities” may be anything else that communicates in the network; this could be servers, devices, applications, or more. The inclusion of entities in UEBA is important because cyber threats are not always tied to humans; automated bots or compromised devices can be just as damaging as human attackers.

Practical applications

UEBA has many practical applications within the modern enterprise:

  • Network security: Monitoring network activity and resource access by both users and entities to detect vulnerabilities or breaches.
  • Detecting insider threats: Identifying abnormal activity from authorized users, indicating the possibility of harmful actions from within your organization.
  • Adversary intrusion: Detecting and alerting your team to unusual data access patterns that might indicate adversary intrusion.

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UEBA components and processes

Effective UEBA requires the coordination of different components and processes. Together, these components form a system to monitor, analyze, and alert you to potential security threats. Let’s look at each of these components in more detail.

Data collection

ML model effectiveness depends on the amount of data available. Naturally, UEBA requires data from various sources across your organization’s systems. These data sources may include:

  • System logs
  • Network traffic data
  • Application usage metrics
  • User activity logs

This data is gathered in real time and will help establish the baseline for UEBA to understand normal and abnormal behavior.

Analytics engines and ML

An analytics engine ingests the collected raw data, applying ML algorithms for training. Based on this training, a UEBA system can understand what qualifies as normal behavior within your systems. Establishing this baseline is key to identifying anomalous patterns of activity and potential security threats.

Continuous monitoring to detect anomalies

With the behavioral baseline established, the UEBA system continuously monitors your network. If it detects any deviations from the norm, it flags the anomaly for further investigation.

Alert and response

Once an anomaly is flagged, the system alerts your security team to provide expert, human-led validation. Immediate notification enables a swift response. An expert investigates the anomaly and takes the appropriate steps to mitigate the risk. Mitigation actions can range from changing user permissions to isolating compromised systems.

Now that it’s clear how UEBA works, we’ll consider its associated benefits and challenges.

Benefits and challenges of using UEBA

Though UEBA is becoming increasingly essential for modern cybersecurity, it does bring challenges alongside its benefits. As your organization considers the implementation of UEBA, understanding both of these is vital.


Through continuous monitoring and analysis of behavior patterns, UEBA offers enhanced security by detecting anomalies that traditional cybersecurity measures might miss. This real-time analysis ensures a strong security posture in the face of an evolving threat landscape and enables your team to take swift action against threats as soon as they emerge.

In addition to the above benefits, UEBA also aids in regulatory compliance. By keeping detailed logs and performing regular analysis, UEBA helps you meet the compliance requirements that may be mandated by your specific industry or region.


Of course, leveraging UEBA is not without its challenges. One of the most significant issues in using UEBA is the risk of false positives. When normal behavior is flagged as anomalous, time and resources are dedicated to (and wasted by) its investigation.

In addition, some organizations are turned off by the perceived implementation complexity of adopting UEBA. UEBA may often require deeper familiarity with ML concepts and the ability to fine-tune ML systems.

Finally, UEBA’s effectiveness depends on its extensive data collection methods. For some organizations, this raises the question of data privacy concerns, as the level of data collection needed may be deemed invasive.

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Introducing AI-powered behavioral analysis from CrowdStrike

In summary, UEBA leverages ML and data analytics to offer a robust and real-time mechanism to detect and mitigate security threats within your organization.

CrowdStrike Falcon® Identity Protection leverages behavioral analysis to detect anomalous actions. For example, in Active Directory (AD), Falcon Identity Protection baselines normal behavior for every user based on authentication and historical data. By using advanced algorithms and ML technologies, it auto-classifies accounts and correlates them with possible AD attack paths or privilege escalations — threat vectors that are commonly hidden from the AD operator.

Falcon Identity Protection builds detailed behavioral profiles for every entity, establishing the baseline for what is — and isn’t — considered normal behavior. Any deviation from baseline behavior is flagged as a threat detection, triggering automated responses based on predefined policies. Because Falcon Identity Protection provides continuous monitoring in real time, it can immediately stop lateral movement as soon as increased risk is detected. This is in contrast to other systems — such as legacy security information and event management (SIEM) solutions — that depend on log ingestion and analysis to detect abnormal behavior.

With AI-powered UEBA systems, organizations can bolster their cybersecurity efforts to detect security threats that traditional cybersecurity measures might not spot. CrowdStrike Falcon® Adversary Intelligence, part of the CrowdStrike Falcon® platform, uses AI/ML in its threat intelligence to determine indicators of attack. For more information, contact us today.


Venu Shastri, a seasoned Identity and cybersecurity product marketeer, serves as Director, Product Marketing at CrowdStrike for Unified Endpoint & Identity Protection. With over a decade of experience in identity, driving product marketing and management functions at Okta and Oracle , Venu has a US patent on passwordless authentication. Prior to his identity experience, Venu had co-founded and drove product management for an enterprise social software start-up. Based out of Raleigh, NC, Venu holds an MBA from the University of Santa Clara and Executive Certification from MIT Sloan.