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User and Entity Behavioral Analytics (UEBA)

User and Entity Behavior Analytics (UEBA) builds upon User Behavior Analytics (UBA) to provide more powerful threat detection across networks. Coined by analyst firm Gartner in 2015, UEBA stands for User and Entity Behavior Analytics. UEBA extends monitoring beyond just user activity to include other entities like servers, routers, and endpoints.

Whereas UBA utilizes machine learning to model typical user behavior and detect anomalies, UEBA expands this approach to additional network entities that could be compromised in an attack. By monitoring both users and other critical infrastructure for suspicious deviations from baseline activity, UEBA solutions can identify threats that evade traditional security tools.

UEBA - User and Admin at their computers

For example, UEBA can detect Distributed Denial of Service (DDoS) attacks by identifying unusual spikes in traffic to a server. It can also flag unauthorized data access by spotting abnormal download volumes or file transfer patterns. By correlating across multiple data sources, UEBA can uncover threats like compromised credentials and lateral movement that span both users and entities.

UEBA’s comprehensive monitoring and advanced analytics offer security teams an essential source of threat detection and response orchestration. It serves as a core component of modern Security Operations Centers (SOCs) and is often integrated into other tools like Security Information and Event Management (SIEM) and identity management.

How UEBA works

UEBA solutions operate by continuously collecting data and analyzing behavior to identify anomalies across users, devices, and infrastructure. The systems run silently, gathering information to establish baselines without disrupting normal activity.

There are three main components that enable UEBA deployments:

  • Analytics: Collects data from various sources, builds behavior profiles, and applies statistical models and machine learning to detect unusual deviations.
  • Integration: Ingests and correlates information from different security systems to enable analysis across diverse data sets.
  • Presentation: Surfaces findings through alerts and visualization, enabling automated or manual response.

During initial deployment, UEBA solutions ingest logs and other system data to model typical access patterns, communication flows, resource usage, and other behaviors. The systems apply advanced analytics like machine learning to define normal baselines across both users and entities.

Once these profiles are built, UEBA matches current activities against them to calculate risk scores for anomalies and flag threats. This analysis is continuous, allowing the systems to identify insider attacks, compromised credentials, data exfiltration, and other risks as they occur.

By combining data from throughout the infrastructure with adaptive analytics, UEBA solutions deliver robust threat detection and response capabilities not found in traditional tools. They are commonly integrated with SIEM platforms to enhance security operations.

Benefits and drawbacks of UEBA

UEBA solutions offer significant advantages for security teams, including:

Benefits of UEBA

  • Automating threat detection across the infrastructure
  • Reducing risks from compromised credentials and insider threats
  • Condensing incident response timelines
  • Addressing a wide range of cyberattacks beyond the capabilities of traditional tools
  • Enhancing analyst productivity through automation
  • Lowering costs by preventing breaches
  • Improving operational efficiency

However, there are also some potential drawbacks to consider:

Drawbacks of UEBA

  • Limited network visibility since UEBA depends on instrumented systems
  • General indications of threat activity rather than specific attack details
  • Reliance on logs and integrations from third-party data sources
  • Slow and complex deployments requiring approvals and configuration

Organizations must weigh these pros and cons based on their environment, risk tolerance, and security objectives. But for most, the advanced threat detection and response capabilities of UEBA offer considerable value and risk reduction.

UEBA in comparison with other systems


While UEBA and SIEM both utilize user and entity behavior data to define normal patterns, they serve different purposes:

  • SIEM provides visibility into overall IT infrastructure security.
  • UEBA focuses specifically on detecting sophisticated threats through user and entity profiling.
  • Using both expands threat detection across simple and advanced attacks.


Network Traffic Analysis (NTA) monitors all network activity, while UEBA offers more advanced analytics:

  • NTA analyzes traffic flows to identify potential attacks.
  • UEBA specializes in user and entity behavioral analysis to uncover threats.


Gartner introduced the term UEBA to expand upon UBA by including the monitoring of non-human entities like applications, servers, and devices. This provides more context for identifying threats.

Best practices and recommendations

  • UEBA should enhance existing security tools, not replace them. Continue using firewalls, IDS, SIEM, etc. in tandem.
  • Limit access to UEBA systems and alerts to designated security team members.
  • Train staff on general cybersecurity best practices around access, data handling etc.
  • Consider both insider and external threats when establishing UEBA policies and rules.
  • Recognize potential threats from privileged and non-privileged accounts alike.
  • Use UEBA with big data analytics platforms like SIEM for detecting sophisticated threats.

The key is viewing UEBA as an augmentation to security stacks by providing specialized user and entity behavioral analytics. It should complement other controls rather than replace core monitoring and prevention tools.


What does UEBA stand for?

UEBA stands for user and entity behavior analytics. It expands monitoring beyond just users to infrastructure like servers and devices.

How does UEBA detect threats?

UEBA establishes baseline behavior profiles for users and entities. It then uses machine learning to identify anomalies and flag potential attacks.

What information does UEBA analyze?

UEBA evaluates data like system logs, network traffic, and authentication events to uncover unusual activity across users, applications, and infrastructure.