Understanding User and Entity Behavior Analytics (UEBA): A Complete Guide

 As cyber threats continue to surge and insider threats become more common, user and entity behavior analytics (UEBA) tools have become an essential component of a comprehensive security strategy, helping organizations to detect anomalous behavior and hidden threats. 

These advanced UEBA technologies utilize machine learning, data science, and pattern recognition to evaluate user and entity activity, establish baselines, and identify deviations that may indicate security events, enabling rapid risk mitigation measures.

In this blog, we will examine the top 5 User and Entity Behavior Analytics (UEBA)  solutions that can help businesses enhance their cybersecurity posture and stay ahead of the evolving threat landscape. The table below outlines all the tools.

How Does UEBA Work?

UEBA gathers, processes, and analyzes network traffic from users and entities to build a behavioral baseline. After the baseline behavior is established, the algorithm detects user and entity behaviors that exceed or fall below the criteria. These anomalous actions trigger real-time alerts to system administrators and security teams, instilling trust in the system's capabilities.

Detecting an advanced attack that uses an employee's compromised credentials is a practical application of UEBA. Suppose a threat actor leverages the employee's credentials to access the network from a different IP address or starts transmitting massive data packets that are atypical for employee transfers. A UEBA solution can notify, block, lock out, or report false positives depending on its capabilities.

Top User and Entity Behaviour Analytics (UEBA) Vendors

Exabeam

Exabeam's UEBA solution creates baselines of typical activity to detect anomalies that standard technologies overlook, such as lateral movement and credential misuse. Its Advanced Analytics feature includes more than 1,800 detection rules and 750 behavioral models for detecting risks such as compromised credentials, zero-day attacks, and advanced persistent threats.

Gurucul

Gurucul is a wide security analytics platform that includes SIEM, UEBA, and XDR components. It claims that customers may employ over 1000 machine learning models out of the box to search for common threat management use cases. The technology may also evaluate a user's social media and website visits to determine user sentiment, which might increase their risk.

LogRhythm

LogRhythm is primarily a logging and SIEM solution. LogRhythm UEBA interfaces with the LogRhythm product and adds "Cloud AI" features to the SIEM. Cloud AI enables artificial intelligence by introducing a new log source for observing and managing user activity. This log source organizes information by type of anomaly, identification of source origin, and other criteria. Cloud AI data, similar to data from other log sources, can be combined with modular graphical widgets to help visualize individual risks.

Securonix

Securonix positions itself as a security operations and analytics platform that integrates SIEM and SOAR capabilities with threat management features designed to meet UEBA requirements. Securonix offers ready-made threat models and machine learning detection that assist in automating data exfiltration events and enhancing data protection. Thanks to its SOAR capabilities, it includes connectors that allow it to connect to various systems and easily gather data from any log source.

Splunk

Splunk User Behavior Analytics (UBA) is an add-on tool for SIEM customers who wish to detect risks and events based on explicit end-user behavior. Splunk uses machine learning algorithms to analyze user behavior and identify suspicious activities. Behavior is assigned a risk score based on baseline behavior patterns, peer group analytics, and ongoing user and group profiling. Because Splunk UBA needs a Splunk license, it's best suited for teams who currently use Splunk as an SIEM and have the resources to manage the high volume of activity going through an SIEM platform.

According to QKS Group, a UEBA Solution has Essential Attributes: 

Use cases: A UEBA solution should be capable of analyzing, detecting, reporting, and monitoring user and entity behavior patterns. Furthermore, as opposed to earlier point solutions, UEBA ought to concentrate on a variety of use cases as opposed to just one analysis, like fraud detection or trusted host monitoring. 

Analytics: A UEBA system should have sophisticated analytics tools that allow it to use many analytics techniques in one package to find anomalies in behavior patterns. These consist of rules and signatures, statistical models, and machine learning (ML). 

Data sources: Both directly from the data sources and via an existing data repository, such as a data warehouse or Security Information and Event Management (SIEM), a UEBA system should be able to ingest data from user and entity activities. 

Market Insights: Do not underestimate the essence of market data when choosing a UEBA tool. Resources such as “User and Entity Behavior Analytics Market Share, 2023, Worldwide” and “Market Forecast: User and Entity Behavior Analytics, 2024-2028, Worldwide”, would be invaluable resources in guiding your vendor selection process. 

Conclusion

user and entity behavior analytics (UEBA) technologies help firms analyze user and application activity across their tech infrastructures. As network traffic and enterprise software create more data, IT and security professionals will have more information about people and assets to evaluate and distill. UEBA does part of that work for them, shifting their workload from manual to more strategic tasks.

UEBA tools do not completely remove manual IT effort, nor are they one-and-done solutions. However, configuring UEBA to closely match your infrastructure pays off: alarms make more sense, and you'll start to grasp behavioral patterns in databases, networks, and apps. UEBA is a long-term investment for enterprises looking to strengthen their security posture by knowing exactly what their users are doing.