UEBA, or User Entity Behavior Analytics, is a type of security technology that uses machine learning algorithms to identify unusual or suspicious user behavior within an organization’s network. This can include unusual login activity, unauthorized access to sensitive data and unfamiliar communication patterns.
UEBA is an essential tool to detect and prevent cyberattacks and insider threats. It can help organizations to identify potentially malicious behavior that may not be detected by traditional security measures such as firewalls and antivirus software.
UEBA analyzes the behavior of users and entities (such as devices and applications) within an organization’s network. It uses machine learning algorithms to build a baseline of normal behavior and then continuously monitors activity to identify deviations from this baseline.
For example, UEBA will flag a user who typically logs in from a specific location and accesses a small number of files, but suddenly begins logging in from a different location and accessing many sensitive files. From a security perspective, this could indicate that the user’s account has been compromised or that they are attempting to access sensitive information for which they do not have authorization.
UEBA can also detect unfamiliar communication patterns, such as a user who regularly communicates with a few individuals but suddenly begins communicating with many external parties. This could indicate that the user is attempting to exfiltrate data or engage in other malicious activity.
The UEBA tool provides a proactive mechanism to continuously monitor activity within an organization’s network, identify unusual or suspicious user behavior and prevent cyberattacks through machine learning algorithms. To leverage the benefits of UBEA, both business and technology functions should decide on goals, expectations, and put together a team that can fully utilize the tool for maximum results.