User Entity Behavior Analytics (UEBA) enhances security by detecting anomalies in user and entity behavior. Leveraging machine learning, UEBA solutions analyze data to identify potential threats and mitigate risks effectively.
With UEBA, organizations gain deep visibility into user activities, allowing better detection of insider threats and compromised accounts. These solutions use advanced algorithms to baseline normal behavior and highlight deviations, facilitating prompt and accurate threat responses. Organizations can tailor these systems to their specific requirements, ensuring a high level of security customization.
What are the critical features of UEBA?In finance, UEBA helps detect fraudulent transactions by analyzing user behavior patterns and flagging anomalies. In healthcare, it secures patient data by monitoring access and usage, ensuring compliance with regulations like HIPAA. Government agencies leverage UEBA to protect sensitive information and detect insider threats.
UEBA is crucial for organizations wanting to proactively protect their assets and data by understanding and managing user behaviors and detecting potential threats early.
User entity behavior analytics, otherwise referred to as UEBA, slowly emerged to replace UBA, offering more powerful solutions. As the threat landscape grew, “entities” were added to UBA to monitor malicious behavior beyond the user level. While UBA can detect human behavior within a network, UEBA can model behaviors of humans as well as the machines within networks, including devices, in addition to applications as well as networks, providing complete visibility. When behavioral abnormalities are associated with an entity (i.e. a particular IP address), attacks hardly go unnoticed. By using a baseline of normal user and machine behaviors, UEBA can recognize when a machine is compromised, and thus minimize the amount of damage that can be done.
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.
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.
There are many benefits of using behavior analytics tools. These include:
Below is a list of some key features to consider when choosing a UBA tool:
