8 min readLast Updated on April 26, 2023 by Satyendra
Due to the increasing size and complexity of corporate networks, it has become easier for hackers to infiltrate a corporate network disguised as an internal employee. If undetected, this can lead to repeated theft of sensitive data and financial loss. The goal of User Behavior Analytics (UBA) is to identify suspicious patterns of behavior in order to expose stealthy attacks and insider threats on a network.
What is User Entity Behavior Analytics?
User Entity Behavior Analytics (UEBA) is a cybersecurity technology that monitors the behavior of users and entities within an organization’s network to detect and mitigate potential security threats. UEBA solutions use machine learning algorithms and statistical models to analyze patterns of user behavior and identify anomalies that could indicate a security breach.
User Entity Behavior Analytics solutions can track various user activities such as login attempts, file access, and email usage to build a profile of each user’s behavior. By analyzing these profiles, UEBA solutions can detect suspicious behavior that deviates from normal usage patterns. For example, UEBA may detect a user attempting to access files outside of their normal work hours or attempting to access sensitive files that are not relevant to their job role.
User Entity Behavior Analytics can also analyze entity behavior, which refers to the behavior of non-human entities such as servers, applications, and IoT devices. By monitoring entity behavior, UEBA can detect potential security breaches caused by compromised entities or suspicious activity from external entities.
UEBA solutions can provide organizations with a wide range of benefits, including improved threat detection and response, reduced time to identify and investigate security incidents, and increased visibility into user and entity behavior. UEBA can also help organizations comply with regulatory requirements such as GDPR and HIPAA by providing enhanced monitoring and control of user data access.
However, UEBA solutions are not a silver bullet for cybersecurity. UEBA requires extensive data collection and analysis, which can be resource-intensive and may lead to false positives or missed threats. To effectively implement UEBA, organizations must ensure that their systems and processes are properly configured and that they have a comprehensive understanding of their network and user behavior.
The Difference Between UEBA and UBA
In October 2017, Gartner released a new market guide for UEBA, which includes the additional letter “E” to recognize the need for profiling entities besides users to more accurately pinpoint threats. UEBA software correlates user activity and other entities such as endpoints, applications, and networks to protect against both internal and external threats, whereas UBA solutions generally only focus on users and the data they interact with.
How does User Entity Behavior Analytics work
UEBA works by collecting and analyzing large amounts of data from various sources, including log files, network traffic, and user activity logs. The data is then fed into machine learning algorithms that analyze patterns and identify anomalous behavior. The system can then alert security teams to potential security incidents or threats.
One of the key features of User Entity Behavior Analytics is its ability to establish a baseline for normal user behavior. The system continuously monitors user activity and compares it to the established baseline. If the system detects behavior that deviates from the norm, it generates an alert for further investigation. This allows security teams to quickly identify potential threats and take appropriate action.
UEBA also uses advanced analytics to identify potential threats based on the behavior of entities or groups of users, rather than just individual users. For example, if a group of users with access to sensitive data suddenly starts accessing that data at odd hours or from unusual locations, the UEBA system may flag this as a potential security threat.
UEBA systems can also help detect insider threats, which are often difficult to identify using traditional security methods. By analyzing user behavior over time, UEBA can identify patterns of behavior that indicate an insider threat. This can include things like excessive data access, unusual logins, or attempts to bypass security measures.
Why Companies Need UEBA
The dynamic detection capabilities of UEBA solutions help to save time by reducing false positives, which are common when relying on static correlation rules. UEBA also provides the following benefits to companies:
- UEBA helps identify data breaches, privilege abuse, and policy violations made by employees.
- UEBA alerts you when a user accesses protected data without a legitimate reason.
- UEBA alerts you when privileged users are created or when permissions are granted that are not appropriate for a user’s role.
- UEBA helps detect attacks on cloud-based entities and third-party authentication systems.
User Entity Behavior Analytics Best Practices
Below are some recommended practices for UEBA:
Define use cases
Prior to integrating UEBA into your system, carefully outline objectives and use case scenarios for the technology. Determine the specific activities or behaviors that necessitate monitoring and ensure that UEBA aligns with your overall security strategy.
Define data sources
UEBA works by collecting and analyzing large volumes of data from multiple sources including logs, network traffic, and endpoint data. Likewise, data can be aggregated from both on-premise and cloud-based environments. Make sure that you understand and document the data sources that are available to you, and ensure that the aggregated data is normalized in a way that provides context for analysis.
Setup rules and thresholds
Although UEBA uses machine learning algorithms to tackle anomaly detection, human intervention is necessary for setting up rules and thresholds. These rules and thresholds should be configured based on your use cases and organizational background to ensure the proper detection of abnormal behaviors.
Integrate with other security tools
Instead of using UEBA in isolation, it should be used with other security tools such as SIEM, EDR, and IPDS solutions as an integral part of a broader security architecture. Doing so will provide a comprehensive overview of security events and facilitate faster incident response.
Continuously monitor and improve
UEBA is not a one-time process, but a continual process that necessitates ongoing monitoring to identify new use cases and modify thresholds and rules as necessary.
User Entity Behavior Analytics vs. SIEM
Both UEBA and SIEM are valuable cybersecurity tools that can identify potential threats by gathering cybersecurity data. However, there are some notable differences between them.
Alerting
SIEM systems tend to generate too many false positives, causing cybersecurity teams to overlook actual cyber threats. UEBA solutions, however, provide risk scoring that allows for a more nuanced ranking of potential threats. UEBA also allows companies to customize security measures to specific risks, thus reducing the number of false positives even further.
Log retention
SIEM can store event data for long periods of time (usually up to 365 days), while UEBA solutions tend to focus more on real-time data or data that is typically less than 30 days old.
Rules and thresholds
SIEM is rule-based and mostly uses IP addresses for grouping activities, while UEBA looks for anomalies and groups events by users or machines. However, some modern UEBA solutions provide rule-based features that allow you to detect and respond to events that match a pre-defined threshold condition.
UEBA solutions can work in tandem with SIEM solutions to give organizations more insights into how users engage with sensitive corporate data. When combined, these systems can offer faster incident detection and response capabilities, strengthening an organization’s overall cybersecurity posture.
How Lepide Helps with User Entity Behavior Analytics
The Lepide Data Security Platform aggregates and correlates event data from multiple sources, including cloud-based environments. It uses machine learning techniques to identify anomalies and generate real-time alerts that provide context for further analysis. It can also detect and respond to events that match a pre-defined threshold condition, making it easy to automate a response to potential security threats. With Lepide, you can have multiple SIEM integrations simultaneously, streamlining your security operations.
If you’d like to see how the Lepide Data Security Platform can help to analyze user behavior, schedule a demo with one of our engineers.
User Entity Behavior Analytics FAQs
Q: What are the benefits of using UEBA security?
A: UEBA security can detect insider threats, identify compromised credentials, and help prevent data breaches. It provides you with real-time alerts when abnormal behavior is detected, allowing you to respond and mitigate damage quickly.
Q: Do I need UEBA if I already have traditional security measures in place?
A: UEBA is not a replacement for traditional security measures such as firewalls, antivirus programs, and intrusion detection systems. Instead, it provides an additional layer of security by analyzing user and entity behavior, which can help detect and prevent attacks that may otherwise go undetected.
Q: Is UEBA difficult to implement?
A: UEBA can be difficult to implement, as it requires collecting and analyzing large amounts of data. However, most UEBA solutions come with pre-configured models and rules to make implementation easier. While implementing UEBA is arguably easier than implementing SIEM, it helps to have the right expertise and resources in place to ensure a successful deployment.
Q: Is UEBA only for large enterprises?
A: While UEBA was initially designed for larger organizations with more complex IT environments, smaller businesses can also benefit from UEBA. There are UEBA solutions available that cater to organizations of all sizes and can be tailored to meet their specific requirements.
