What Is User Entity Behavior Analytics (UEBA)? Complete Guide

How Do UEBA Tools Work?

UEBA solutions monitor the behavior of all users and entities in an organization’s network. They process the behavioral data to determine if a specific activity or behavioral trend may indicate or enable an attack. UEBA technology can identify what constitutes a normal activity and what might be a threat.

Attackers can infiltrate the network using stolen employee credentials, but the attacker’s malicious activity stands out from normal behavioral patterns, allowing UEBA to detect it as an anomaly.

The data processed by UEBA can come from a data repository (i.e., a data lake) or a SIEM solution that collects data from multiple sources. UEBA integrates varying information types, including packet capture data, logs, and other data from security monitoring platforms. Many organizations combine UEBA with SIEM to aggregate and process data.

The analytics component of UEBA can detect anomalies with various analytic techniques, including rule-based, signature-based, statistical, and machine learning models. In addition to tracking devices and events, UEBA monitors potential insider threats using machine learning. It works by establishing a baseline of normal activity to compare suspicious behavior.

If a user logs in from an unusual location, accesses unusual files or tries to access files too frequently or at unusual times, this might indicate improper use of access privileges. Advanced analytics capabilities should complement the traditional rule-based analytics of a SIEM, allowing UEBA to detect many complex and simple attack types, as opposed to a specialized tool that focuses on monitoring one thing (i.e., employees).

UEBA’s ability to detect anomalous behavior in real time allows it to send alerts immediately. Security analysts can thus respond quickly to threats and prevent serious breaches. Without UEBA, security teams need to filter alerts to identify which indications pose a real risk. UEBA automates this analytical process to prioritize actual threats.

Learn more in our detailed guide to UEBA tools (coming soon)

UEBA Security Use Cases

UEBA solutions and behavior-based security are useful for various scenarios. Several advanced threats can circumvent and evade detection by conventional security tools, but a UEBA system will detect and block them. These include:

• Stolen credentials—attackers can obtain a legitimate user’s credentials. Any malicious activity performed under legitimate credentials may appear normal to a traditional monitoring tool, but UEBA identifies atypical activity in the user’s account.
• Targeted devices and accounts—advanced attackers may directly target an endpoint device or account used by executives like the CEO or CFO. UEBA helps identify unusual behavior on privileged assets.
• Compromised hosts—attackers may go undetected for many months or years after taking control of a system or server in the corporate network. UEBA helps identify changes in system behavior and investigate whether malicious activity is occurring.
• Insider threats—malicious insiders are a major security problem given their ability to avoid detection by traditional security solutions. UEBA can see when a user performs risky or suspicious actions, including transferring large amounts of data, escalating privileges, and accessing an unexpected application or system that may indicate malicious behavior.
• Lateral movement—an attacker can compromise an endpoint or system and use it as a base to gain access to other user accounts and systems. UEBA provides a comprehensive view of multiple systems to identify anomalous activity across the network.
• Data theft—if someone transfers an organization’s data to an external source, it could indicate an exfiltration attack. In some cases, data transfers are legitimate, with users leveraging an external service, but in others, attackers set up malware to exfiltrate sensitive information. UEBA analyzes data transfers to determine whether the destination is legitimate and assesses the transmitted data’s appropriateness for the user’s role and context.

Learn more in our detailed guide to UEBA security (coming soon)

UEBA vs. SIEM

Security Information and Event Management (SIEM) is a comprehensive set of technologies providing an in-depth view of an organization’s security profile. It uses threat data and event information to help security teams identify normal patterns and anomalous events or trends. UEBA uses a similar approach, alerting security teams to anomalies, but it adds user and entity activity to the data mix.

In short, SIEM focuses on security events, and UEBA emphasizes behavioral patterns. Another major difference is that SIEM is a rule-based solution. Attackers can identify and evade SIEM rules to avoid detection. SIEM rules usually aim to identify threats immediately, but advanced attacks may occur over a long period.

On the other hand, UEBA uses sophisticated algorithms and risk scoring instead of rules. These techniques allow UEBA to identify suspicious activity when spread over time. An IT security best practice is to combine UEBA and SIEM to provide well-rounded threat detection.

Learn more in our detailed guide to UEBA and SIEM (coming soon)

What Are the Pros and Cons of UEBA?

UEBA helps strengthen an organization’s security system, covering the blindspots that traditional security solutions cannot, including SIEM, user monitoring, and rule-based access control (RBAC). Implementing UEBA alongside other security tools helps increase their flexibility, minimize false positives, and identify more advanced threats.

Organizations can significantly improve their security posture with the following benefits of a UEBA solution:

• Automated data analytics—UEBA tools collect and process large volumes of user and entity activity logs within an organization’s infrastructure. These logs enable risk scoring of various security events, saving time and effort for the security team. Security analysts can prioritize high-risk security events instead of manually analyzing everything.
• Real-time and advanced threat detection—conventional security monitoring tools cannot detect threats as quickly as UEBA, allowing malicious actors to cause more damage to the organization before being identified and blocked. UEBA identifies the subtle behavioral changes of a user or entity before it explicitly breaks the organization’s security rules. Early detection can help prevent an event from escalating to a serious incident.
• Automated response—UEBA tools typically alert security teams when they detect suspicious behavior, but they can also block threats automatically. If so configured, UEBA can initiate an automated threat response to contain a suspicious user or entity and stop an attack before it causes damage. It buys time for the security analysts to investigate an incident.
• Low maintenance—while configuring a UEBA tool might be challenging, it requires little maintenance after the initial setup. Once the UEBA solution has the necessary data, behavioral baselines, and trained algorithms, it can operate autonomously with minimal IT and security team oversight. Aside from the occasional fine-tuning or addition of a baseline, the tool can automatically adjust to a changing security environment using its machine learning algorithms.

The deployment and configuration of UEBA tools can involve several challenges:

• Building behavioral baselines—UEBA solutions are not out-of-the-box, requiring extensive training and customization. The algorithm must learn the behavioral baselines based on user behavior data before identifying threats in the real world. This process takes time (up to three months), so organizations should factor this in when adopting a UEBA solution. UEBA is effective as a long-term strategy but not for urgent improvements.
• Less effective slow attack detection—UEBA is most effective when users or entities rapidly change their normal behavior. For example, it can immediately identify an attacker compromising an accounting or stealing data as a one-off. However, some attacks progress slowly, especially if they involve infiltrators or malicious insiders who prepare and execute their attacks over a long period. Sometimes, these attacks involve very small actions repeated daily over several months, such as transferring small amounts of sensitive data. UEBA tools might view these as suspicious actions because they become part of the user’s normal behavioral pattern.
• Required expertise—training UEBA requires special skills and knowledge because each organization needs customized user behavior datasets. Generic datasets are not effective given the differences in the responsibilities of users and entities in different organizations. Preparing these datasets is complicated and involves ML training expertise. Organizations have to train in-house experts or hire third parties to build their datasets.
• High-investment deployments—implementing UEBA requires significant time and effort, and each part of the setup (configuration, training, integration) has its associated costs. Given the complexity of UEBA technologies, organizations need to employ AI specialists, which adds to the cost of the UEBA tool.

User and Entity Behavior Analytics Best Practices

After an organization sets up and activates its UEBA system, it can apply several principles to help ensure that the system performs well. The following security best practices apply to various security tools, including UEBA. How an organization uses these systems is an important factor in increasing security by ensuring all employees have the necessary knowledge to implement security.

Staff Training

A major factor in the proper use of UEBA systems is ensuring that employees have the knowledge and skills necessary to operate these systems. Organizations can use a security memo template to help employees understand the importance of security. It is also important to raise security awareness and best practices throughout the year—this applies to UEBA systems and other types of security software.

UEBA Baselining

Business security goals and proper user activity are essential considerations for the baselining period. This period should not be too long or too short—if the baselining period ends too soon, there may not be enough time to collect accurate information, and the false positive rate may increase. On the other hand, if an organization takes a long time to baseline information, some malicious activity could pass as normal behavior.

Because user and entity activity is constantly changing, baseline data may require periodic updates. Employees can switch roles, projects, and privilege levels, enabling them to perform new activities. Organizations can configure their UEBA system to collect data automatically and adjust baselines as changes occur.

Anticipating Insider Threats

This best practice is more specific to UEBA solutions—organizations must consider their overall threat profile when creating rules and policies to identify attacks. One of the main advantages of UEBA is its ability to detect insider threats effectively, just as it detects external threats. However, insider threat detection is only possible if the system is configured to look for them.

Restricting Access

To secure their UEBA systems, organizations must give the appropriate personnel the appropriate privileges. Not everyone should have access to the UEBA system. Only specific team members should have permission to view security data, and the system should only notify these individuals.

Preparing for Privilege Escalation

Many organizations underestimate the threat posed by non-privileged user accounts. Attackers often target low-access accounts and use them to escalate their privileges and hack sensitive systems. A UEBA system helps detect unauthorized attempts to escalate privileges. The software should be configured to alert the security team when privilege escalation incidents occur.

User Behavior Analytics with Cynet

Cynet’s AutoXDR platform includes a User Behavior Analytics component that monitors user behavior to spot and isolate compromised accounts and malicious insiders. It can help identify suspicious activity such as anomalous logins, users accessing sensitive data from unsecured locations or devices, and first time logins to sensitive systems.

Cynet UBA provides the following capabilities:

• Customize baseline user behavior – use associate information, like role, group, geolocation, working hours and more to define normal behavioral patterns, then automatically detect suspicious activity, such as first-time and off-hour logins.
• Get real-time activity context – continuously correlates user activities against other events — such as endpoints, files and external network locations — to provide holistic information to determine real-time risk levels.
• Automate alerts and remediation – sends alerts upon detecting suspicious activity. You can also automatically disable compromised accounts or review activity context to take necessary action.
• Verify users with two-factor authentication – continuously correlates user activities against other events — such as endpoints, files and external network locations — to provide holistic information to determine real-time risk levels.

Learn more about Cynet UBA and Cynet AutoXDR 360