Best User Behavior Analytics (UEBA) Solutions

While they may seem synonymous, UBA and UEBA are distinctly different. While UBA can detect and track suspicious activities and behaviors, UEBA is able to detect abnormalities that are more complex across multiple users, devices, and IP addresses. Unlike UBA, UEBA tracks user activity and other entities. These entities may or may not include managed and unmanaged endpoints, networks, applications, and external threats.

1. Use cases are one of the three pillars that provide insight into the abnormal behavior of users and entities within a corporate network by monitoring, detecting, and making alerts of anomalies.
2. Data sources are used to collect various types of data from a repository. The repositories often include data from a data lake or warehouse or an external system like SIEM (security information and event management).
3. Analytics are used to detect abnormal behavior, either through supervised or unsupervised machine learning or other methods like rule-based analytics, statistical modeling, or threat signatures. Data analysis allows baseline profiles and patterns to be created so that anomalies can be detected by comparing those profiles to user or device behaviors.

UBA and SIEM (security and information event management) are closely related. UBA tools work in conjunction with SIEM solutions to reveal anomalies in behavioral patterns within a network. To perform analysis, UEBA relies on security data which is collected and stored by a SIEM. UBA works in real time to uncover unknown threats and anomalies, whereas SIEM uses point-in-time analysis, which means that it can only process a limited number of events in a particular time frame. By combining UBA with a SIEM solution, human and machine behavior can both be spotlighted, providing organizations with the benefits of advanced threat detection that traditional security tools often miss.

User behavior can be defined as how users interact with a website. Typically, this can refer to any action a user takes, such as the amount of time they spend on a specific page, how many pages they visit, how long they remain on the clicked pages, which links they click on, how they scroll, when and where they leave the website from, and much more. Tracking user activity can be especially helpful when related to threats or cyberattacks. Detecting potential risks or threats before they escalate can save organizations from experiencing damage to their systems, and can save lots of money and time.

Behavior analytics tools are tools used by an organization for analytics, statistics, data protection, or breach prevention. With the hacking incidents increasing more and more frequently, using behavioral analytic tools has become a crucial element for all businesses. The primary goal of behavior analytics tools is to track a user's behavior and data usage, as well as network events and typical behavior patterns to easily identify potential threats based on detected anomalies.

UEBA enhances security threat detection by analyzing patterns of behavior across users and entities such as devices within your organization. Traditional security solutions often focus on known threats, but UEBA's ability to learn and adapt to behavior anomalies enables you to identify insider threats and new attack vectors before they cause harm. Machine learning algorithms power UEBA systems to understand normal behavioral baselines and flag deviations that may indicate credential compromise, data exfiltration, or other malicious activities.

When selecting a UEBA solution, you should look for features such as real-time monitoring, machine learning capabilities, and integration with existing security information and event management systems. Strong analytic functions are essential, allowing you to spot subtle changes in behavior that could suggest security risks. The solution should easily scale based on the size of your organization and provide detailed reporting and visualization tools to assist in understanding insights and taking prompt actions.

UEBA integrates seamlessly with various cybersecurity solutions like Security Information and Event Management (SIEM), Identity and Access Management (IAM), and Data Loss Prevention (DLP) systems. This integration allows you to leverage existing data and enhance security coverage without disrupting your systems. By interacting with these systems, UEBA aids in contextualizing threats, providing a unified view of network activity, and enhancing overall incident response capabilities.

While all industries can benefit from UEBA, particularly those handling sensitive data like healthcare, finance, and government sectors, these industries see tremendous value. In finance, for instance, UEBA helps identify fraudulent transactions by monitoring user behaviors. In healthcare, it safeguards personal health information by detecting unusual access patterns. Government entities utilize UEBA to protect sensitive operations from being compromised by insider threats.

Machine learning algorithms in UEBA work by establishing a baseline of what constitutes normal behavior for users and entities in your network. These algorithms analyze historical data and continuously update to adapt to new patterns. When deviations from the norm are detected, the algorithms trigger alerts, allowing you to investigate potential threats. By leveraging supervised and unsupervised learning models, UEBA provides a dynamic and robust defense mechanism against sophisticated cyber threats.