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User Entity and
Behavior Analytics

Leverage Machine learning-based threat detection and user behavior analytics to protect and bolster your enterprise security posture.

What is UEBA?

UEBA (User and Entity Behavior Analytics), is the process of analyzing the behaviors of an organization’s insiders (employees, staff), outsiders (third-party vendors, contractors), entities (endpoints, servers, accounts, laptops) and applications. It combines the data collected from users and entities to identify any unusual behavior coming from them. It is a process that observes the normal conduct of users and entities and detects any anomalous behavior where a user deviates from these “normal” patterns.
UEBA learns from what users and entities do on a regular basis, for instance, 
  • where do users log in from?
  • what devices do they log in from?
  • what file servers and applications do they access?
  • what privileges do they have?
It establishes a baseline from this data of what is usual or normal behavior. By understanding what is normal for each user and entity, UEBA can easily detect when something unusual occurs. For example, if a user suddenly accesses a server they don’t usually access and they are doing it from a foreign location.
A simple and more relatable example would be if your credit card was stolen. A thief can steal your wallet and spend thousands of dollars using your credit card. If this spending pattern does not match yours, the company’s fraud detection department will often recognize this type of suspicious behavior and contact you to verify the purchases and block the card in question.

How UEBA Works

UEBA solutions work on the premise of establishing baselines by learning the behavior of users, groups and devices and applying risk scoring that adapts over time based on activity. The risk score increases or changes every time a security incident occurs based on the type and priority of the threat.
For example, the system monitors a user’s behavior on a daily basis, when do they arrive at work, what devices do they log into, what other devices such as printers and removable drives do they use, and many other similar data points to determine the user’s normal behavior. The same process is followed for entities such as servers, databases and other significant endpoints. 
If and when there is a deviation from the established baseline, the system adds to the risk score of that particular user or entity. The more unusual the activity, the higher is the risk score added. This continues for each suspicious behavior until the risk score reaches a predefined threshold and an alert is raised. This is helpful not only in identifying threats but also in keeping track of their insecure and privileged users and more.

Why you need UEBA?


Policy or rule based approaches are not able to detect subtle, risky behaviors. Behavior analysis based on raw activity data, helps in identifying users based on rogue intent.


Historical analysis provides deeper insights into usage and trends based on statistical analysis and pin points threats that may occur over a much more extended period of time.


Dynamic baseline for user and device behavior based upon normal behavior. A behavioral profile for each applicable attribute of a user or entity is created using data science. 

Ready to See UEBA in Action?

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Determine users exhibiting risky behavior such as privileged access and atypical data movement.


Analyze and detect patterns of human behavior in big data to reduce the attack surface.


DNIF learns from the anomalies you find most valuable, and then screens out irrelevant detections.


Data models informed by both structured data, such as SIEM logs and unstructured data.


Historical analysis allows you to quickly learn and profile user / entity / parameter behavior.


Leverage ML models to detect and make high-level decisions around your security posture.

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