Understanding UEBA: The Key to Strengthening Your Cybersecurity Strategy

In recent years, user and entity behavior analytics (UEBA) has emerged as a vital component of modern cybersecurity strategy. UEBA helps organizations detect, investigate, and respond to threats that traditional security tools often fail to identify. This blog series aims to help you better understand UEBA by discussing its definition, purpose, and distinguishing factors from other security tools. In this first post, we will cover the basics of UEBA, why the market exists, and why behavioral analytics are needed.

What is UEBA?

UEBA is a cybersecurity solution that analyzes user and entity behavior on networks and other systems to detect anomalies and malicious behavior. By ingesting operational data from multiple sources, UEBA solutions use machine learning (ML) and behavior analysis to establish standard profiles of behavior for users and entities operating within an enterprise network. When anomalous activity is detected, the solution assigns a risk score, and if the score exceeds a predefined threshold, an alert is sent to security operations center (SOC) analysts for further investigation.

The birth of the UEBA market

The concept of monitoring unusual behavior to identify potential threats can be traced back to credit card fraud detection techniques. However, early attempts to apply these techniques to cybersecurity were limited by the reliance on expert systems that couldn’t adapt to the constantly evolving tactics of cybercriminals. Two key developments changed the landscape: the advancement of ML techniques and the significant reduction in data storage costs. These factors enabled the emergence of UEBA as a viable cybersecurity solution, with Exabeam being one of the pioneering companies in the field.

Why are behavioral analytics needed?

Traditional rule-based security solutions often struggle to detect sophisticated and stealthy cyberattacks, both “low and slow” techniques as well as credential-based movement and well-disguised new process introduction from malware. Consequently, security analysts are inundated with alerts that provide little context, making it difficult to quickly detect and remediate compromised credentials and lateral movement of attackers. 

UEBA solutions employ advanced ML and data science techniques to combat these advanced threats effectively. By integrating and analyzing data from multiple security log sources, UEBA solutions provide fewer but more actionable alerts, ultimately bolstering an organization’s security posture.

Conclusion

Understanding the fundamentals of UEBA is crucial for organizations looking to strengthen their cybersecurity posture. By leveraging advanced ML algorithms, UEBA can analyze and detect anomalies in user behavior, providing valuable insights and timely alerts to thwart potential threats. Stay tuned for part two of this blog series, where we will explore how UEBA differs from other security tools and the various ways it is utilized to enhance an organization’s security posture.