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.
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
UEBA vs. SIEM
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.
UEBA vs. NTA
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.
UEBA vs UBA
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.
Common use cases for UEBA
UEBA empowers organizations to detect and respond to sophisticated threats by analyzing patterns of behavior. Below are several ways UEBA can make a tangible impact:
1. Spotting insider threats
Behavioral analysis for detecting insider threats is a core UEBA use case. It helps uncover unusual activity from trusted insiders—whether intentional or accidental. By monitoring shifts in access patterns or anomalous file usage, UEBA makes it easier to stay ahead of risks like unauthorized data transfers or privilege misuse.
2. Detecting stolen credentials
When accounts are compromised, attackers often behave in ways that stand out. UEBA picks up on these signals—such as logins from unfamiliar locations or systems accessed at unusual hours—so you can intervene quickly.
3. Monitoring privileged accounts
Privileged users have access to sensitive systems, making their actions critical to monitor. UEBA highlights deviations from their typical workflows, providing an added layer of oversight that’s both proactive and non-invasive.
4. Uncovering Advanced Persistent Threats (APTs)
APTs are designed to evade traditional detection methods. UEBA cuts through the noise by tracking subtle behavioral anomalies, like lateral movement or atypical data exfiltration, that signal a deeper threat.
5. Streamlining compliance efforts
Regulations demand proof of secure operations. UEBA supports compliance by monitoring sensitive data access and generating actionable, easy-to-audit records for reporting.
6. Accelerating incident response
When alerts lack context, teams waste time chasing false leads. UEBA enriches incidents with behavioral insights, helping analysts prioritize real threats and respond decisively.
7. Securing IoT ecosystems
IoT devices often operate on the edges of traditional security. UEBA identifies unusual communication patterns or usage spikes that might signal compromise, helping secure these vital endpoints.
8. Identifying ransomware early
Early-stage ransomware activity, such as mass file access or encryption, stands out against typical workflows. UEBA spots these changes, enabling faster containment and reducing potential damage.
By focusing on patterns, not just events, UEBA enables organizations to see the bigger picture, delivering insights that are actionable, relevant, and timely.
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.
FAQs
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.
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