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What is User and Entity Behavior Analytics (UEBA)?

User and Entity Behavior Analytics UEBA

Hitesh J UPDATED: May 20, 2024

User and Entity Behavior Analytics (UEBA), sometimes known as User Behavior Analytics (UBA), is a cybersecurity solution or feature that identifies dangers by looking for an activity that differs from the norm. UEBA is most typically used to monitor and detect odd traffic patterns, unauthorized data access and movement, or suspicious or malicious behavior on a computer network or endpoints.

What is UEBA?

UEBA, or user and entity behavior analytics, is a cybersecurity mechanism that monitors users' usual activity. As a result, they notice any unusual activity or situations where the “normal” patterns are broken. For instance, if a person typically downloads 10 MB of data per day but then suddenly downloads terabytes of data, the system would be able to recognize this anomaly and notify them instantly.

UEBA uses machine learning, algorithms, and statistical analytics to determine whether there is a divergence from established trends, indicating which anomalies could pose a significant threat. UEBA can also aggregate data from reports and logs and analyze data from files, flows, and packets.

You don't track security events or monitor devices in UEBA; instead, you follow all of your system's users and entities. As a result, UEBA focuses on insider threats, such as rogue employees, compromised employees, and those who already have access to your system and then carry out targeted assaults and fraud attempts, as well as servers, applications, and devices that are working within your system.

Components of User and Entity Behavior Analytics

The three primary components of UEBA systems are all critical to their operation:

  • Data analytics employs information about users' and entities' “typical” behavior to create a profile of how they behave. The use of statistical models to detect anomalous behavior and inform system administrators is thus possible.
  • Data integration refers to the ability of UEBA systems to compare data from a variety of sources, including logs, and other datasets, with data from existing security systems.
  • The technique through which UEBA systems communicate their findings is known as data presentation. This is usually accomplished by requesting that a security analyst evaluate odd behavior.

How Does UEBA Work?

From system logs, a UEBA system collects data on user and entity activity. It analyses the data using powerful analytical tools and creates a baseline of user behavior patterns. In addition, UEBA continuously analyses entity behavior to detect anomalous behavior and compares it to baseline behavior for the same or comparable entities.

Baselining is essential for a UEBA system since it allows for the detection of potential threats. The UEBA system creates a risk score and assesses if deviations are allowed by comparing the predefined baseline with current user behavior. When the risk score reaches a specific level, the system sends a real-time alert to security analysts.

UEBA's premise is quite essential. You can easily steal an employee's user name and password, but imitating the person's typical behavior while on the network is far more complex.

Take, for example, stealing Jane Doe's password and user name. Even if you were given significant knowledge and preparation, you wouldn't be able to act precisely like Jane Doe once within the system. As a result, UEBA alarms are triggered when Jane Doe's user identity is logged in to the system, and her behavior differs from that of a usual Jane Doe.

Another similar scenario is if your credit card was taken. For example, pickpocketing your wallet allows a thief to go to a high-end store and spend thousands of dollars on your credit card. Suppose your spending history on that card differs from the thief's. In that case, the company's fraud detection department will likely notice the unusual spending and block suspicious transactions, sending you an alert or requesting you to double-check the transaction's legitimacy.

As a result, UEBA is an essential part of IT security, allowing you to:

  1. Keep an eye out for insider threats. It's not impossible to envisage an employee, or perhaps a group of employees, going rogue and stealing data and information using their credentials. Data breaches, sabotage, and policy violations committed by your employees can all be detected with UEBA.
  2. Identify accounts that have been compromised. User accounts are occasionally hacked. It's possible that the user unintentionally placed malware on their computer or that a valid account has been faked. UEBA can assist you in identifying faked and compromised users before they do serious harm.
  3. Detect and prevent brute-force assaults. Hackers are known to attack cloud-based entities and third-party authentication solutions. You can detect brute-force attacks with UEBA, which allows you to limit access to these organizations.
  4. Detect permissions modifications and the formation of super users. The employment of super users is used in some attacks. You can use UEBA to detect when super users are formed or if accounts have been given permissions they don't need.

UEBA tracks the actions of users and entities within a company. It analyses this data and determines whether a specific activity or behavior could lead to a cyberattack. It can distinguish between a threat or attack and regular use because, while a hacker may be able to hack an employee's password to log in, once inside, the hacker will not be able to simulate “regular” activity, which UEBA can identify.

UEBA can analyze data from broad data repositories like a data lake or data warehouse and data from SIEM, aggregating data from multiple sources. It incorporates data from existing security monitoring systems, such as logs, packet capture data, and other datasets. Because UEBA relies on cross-organizational security data, which is often collected and stored by SIEM, the two are frequently utilized together.

A range of analytics approaches, including statistical models, machine learning, rules, and threat signatures, are used by the analytics component to discover anomalies. For example, UEBA employs machine learning to monitor potential insider threats in addition to recording events and devices. This is accomplished by establishing a ‘baseline,' which includes the location from which end-user signs in, the files and servers they regularly access, the privileges they have, the frequency and time of access, and the devices utilized for entry. In addition, standard rule and correlation-based analytics offered in traditional SIEMs should be used in conjunction with advanced analytics.

As a result, unlike specific solutions for staff monitoring, trusted hosts monitoring, and fraud, UEBA can detect a wide range of attack types, from simple to complicated.

Because UEBA can detect abnormal behaviors in real-time, it can send an alarm to security analysts and seek a reaction fast, allowing them to respond to possible threats before they become breaches. Typically, security personnel would have to comb through warnings to determine legitimate threats; however, UEBA automates this process, prioritizing only actual threats.

Because UEBA relies on cross-organizational security data to complete its analysis, it is often collected and stored by a SIEM. Thus, there is a close relationship between UEBA and SIEM systems.

5. Detect a data breach that is protected. It is not enough to just keep data secure if it is secured. You should be aware of when a user accesses this data without having a genuine business justification to do so.

Three Pillars of UEBA

UEBA systems have three major characteristics, according to Gartner:

  • Case studies: The behavior of entities and users in a network is reported by UEBA solutions. Anomalies are detected, monitored, and alerted. Thus, unlike systems that do specialized analyses such as trusted host monitoring, fraud detection, and so on, UEBA solutions must be relevant for numerous use cases.
  • Sources of information: UEBA solutions can ingest data from a general data repository; Data warehouses, data lakes, and Security Information and Event Management are examples of such repositories (SIEM). To collect data, UEBA technologies do not put software agents directly in the IT environment.
  • Analytics:  Machine learning, statistical models, rules, and threat signatures are used in UEBA systems to isolate abnormalities.

Best Practises For UEBA

UEBA originated as a result of malicious activity on the part of users and other entities. Therefore, UEBA technologies and methods should not replace existing monitoring systems but rather supplement them and improve your company's overall security posture.

Another great technique is to utilize machine learning and statistical analysis to harness big data storage and processing capacity to avoid receiving an avalanche of irrelevant alerts and becoming overwhelmed by the vast number of data created.

UEBA strengthens security by monitoring users and other entities and detecting anomalies in behavior patterns that could indicate a threat using machine learning and algorithms. As a result, today’s companies can improve their security posture and more effectively mitigate threats and prevent security breaches by taking a better approach to security and acquiring more visibility into user and entity behavior.

To properly implement a UEBA system, use these recommended practices:

  • When developing new laws and guidelines, keep both internal and external dangers in mind.
  • Ensure that only the members of your team who need to know receive UEBA alerts.
  • Non-privileged user accounts should not be thought of as innocuous. To breach critical systems, attackers frequently take control of regular accounts and elevate access. Unauthorized privilege escalation can be detected using UEBA systems.
  • Do not consider UEBA processes and tools to replace entire monitoring systems like intrusion detection systems (IDS). Instead, they should be used in conjunction with existing monitoring infrastructure.

UEBA systems are more effective when they are integrated into other cybersecurity tools. The user tracking service is an insider threat detection mechanism, so it can heighten the accuracy of SIEM and XDR services. The Logpoint platform provides an example of the combination of UEBA and SIEM.

Logpoint SIEM Reports

The Logpoint UEBA has two threads. The first of these records every action of each user account. This service uses machine learning to register a pattern of behavior. Each new action is compared to the standard and if it is an outlier, Logpoint will raise an alert. The SIEM will subsequently pay more attention to the activities of that user account.

The second service of the Logpoint UEBA extracts data from Active Directory to identify unusual login activity, such as the same user account logging in from different locations in the world. It also examines failed logins, which could indicate a brute-force attack that is trying many possible passwords to crack account credentials.

Logpoint UEBA Access a FREE Demo

Pros of UEBA

  • The main advantage of UEBA is that it can identify a broad spectrum of cyberattacks automatically. Insider threats, compromised accounts, brute-force attacks, the creation of new users, and data breaches are all examples of these.
  • This is advantageous since automated methods can significantly minimize the number of security analysts required. Currently, this will be a significant draw for many businesses since the cybersecurity sector remains strong, but there is a considerable cybersecurity skills shortage. According to the ESG/ISSA research survey, 74 percent of respondents think scarcity affects their businesses. By the way, this percentage has risen from 70% last year.
  • Because UEBA allows fewer security analysts to accomplish more, it can help you save money on cybersecurity. This is another important reason why more and more businesses are adopting UEBA. Between 2010 and 2018, cybersecurity expenses surged by 141 percent, owing in large part to emerging technologies like UEBA.

Cons of UEBA

However, even when used in conjunction with other cybersecurity solutions, UEBA has some downsides.

  • The most significant disadvantage of UEBA is the upfront expense. While investment in UEBA may immediately pay for itself for larger enterprises, smaller businesses may not require such a comprehensive monitoring solution. In addition, small companies deploying UEBA may perceive it as a waste of time and money because most dedicated hosting options already include advanced user access controls for websites and web portals.
  • Second, the data produced by UEBA is more sophisticated than that produced by simpler UBA systems. For analysts who lack the necessary training, this can be difficult to comprehend.
  • Finally, and reiterating some of the concerns made earlier, it's critical to remember that UEBA is a supplement to other cybersecurity solutions, not a replacement. It will alert you to suspicious behavior, but it will not prevent burglars from entering your home.

Final Thoughts

UEBA is a valuable addition to your cybersecurity arsenal. It gives you a new technique to detect threats and can be used in conjunction with intrusion prevention systems and threat detection software. UEBA systems can also train new security engineers because they provide an excellent opportunity to learn what constitutes suspicious activity.

Finally, keep in mind that UEBA systems are not a silver bullet for cybersecurity. They should also be used in conjunction with a comprehensive Threat Detection solution. UEBA systems can substantially shorten the time it takes to identify and respond to cyberattacks when used in this fashion, recognizing threats that standard technologies miss.



A SIEM is a security system that searches through logs and network activity monitoring data for indicators of compromise. This can include UEBA. The UEBA methodology establishes a baseline of regular activity per user account and per device to enable anomaly detection. This method can be used in an intrusion detection system (IDS), endpoint detection and response (EDR), and SIEM tools.

What is the difference between UBA and UEBA?

UBA stands for “user behavior analytics.” It models the activities of each user account on a system. UEBA stands for “user and entity behavior analytics.” This models user activities and also records the typical usage of each device on a network.

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