UEBA (User and Entity Behavior Analytics)

What is UEBA

UEBA (User and Entity Behavior Analytics) is a cybersecurity technology that analyzes the behavior of users and technical entities (servers, routers, applications, databases) to detect anomalies indicating threats. UEBA uses machine learning to build a profile of "normal" behavior and detect deviations that may indicate a hack, insider actions or compromised accounts.

In simple terms, UEBA is like a video surveillance system that does not just record who entered the building, but analyzes how this person behaves. If an employee who usually arrives at 9 in the morning and works with documents suddenly appears at 3 in the night and tries to copy a terabyte of data to a flash drive — UEBA will notice this deviation and raise an alarm.

Unlike classic protection tools (antiviruses, antispam filters, firewalls), which work according to known signatures and rules, UEBA focuses on behavior. This makes it possible to detect zero-day (0-day) attacks, advanced persistent threats (APT) and insider threats that do not have known signatures. According to Gartner, by 2026 more than 60% of organizations will use UEBA as part of their cybersecurity strategy, which is due to the growth of attacks on accounts and internal threats.

How UEBA works

UEBA uses a multi-level architecture that includes several stages of data processing for detecting hidden threats:

1. Data collection: The system aggregates data from various sources: application and operating system logs, network traffic, activity in corporate systems (ERP, CRM, Active Directory), data from EDR/XDR, authentication logs, data from DLP systems and SIEM. The more data sources, the more accurate the profiles and the higher the quality of detection.
2. Baseline building: ML algorithms create a profile of normal behavior for each user and device on the basis of historical data (usually 3-6 months). The profile includes: typical login and logout time, used applications and services, volume of transferred data, typical geolocation, frequency of actions, typical work patterns.
3. Anomaly detection: Any deviation from the profile (for example, downloading a terabyte of data at night, login from an unusual geolocation, mass deletion of files, launching uncharacteristic processes) is marked as suspicious. Each event is assigned a "risk score" from 0 to 100.
4. Risk assessment and correlation: UEBA correlates anomalies with each other and with external threats (for example, from Threat Intelligence). A single anomaly may be harmless, but their combination (for example, login from a new device + downloading a large amount of data + sending to an external e-mail) gives a high level of risk.
5. Alerting and response: When the risk threshold is exceeded, an alert is generated for the information security monitoring center (SOC). Modern UEBAs also integrate with SOAR platforms for automatic response according to given scenarios (playbooks).

UEBA is actively used to protect critical systems working with the digital ruble and the Fast Payment System (FPS), since it makes it possible to detect anomalies in financial operations in real time.

Key advantages of UEBA

The implementation of UEBA gives organizations a number of critically important advantages that make it a mandatory element of a modern security system:

• Detection of insider threats: UEBA reveals employees who try to steal or leak data, work under the accounts of other persons or act at uncharacteristic times. This is especially important for protecting trade secrets and personal data.
• Detection of compromised accounts: Detecting anomalous behavior of stolen or compromised accounts (for example, login from another country, uncharacteristic actions). This makes it possible to block access before the attacker causes damage.
• Detection of targeted attacks (APT): Detecting the hidden activity of attackers inside the network who have already passed the protection perimeter and act slowly and carefully. APT attacks often last for months, and UEBA helps to notice them at early stages.
• Reduction of false positives: ML algorithms more accurately distinguish anomalies from ordinary user errors, which reduces the load on the SOC and allows analysts to focus on real threats. The accuracy of UEBA reaches 95-99% with correct configuration.
• Detection of zero-day attacks: Since UEBA does not use signatures, it can detect new, previously unknown attacks that do not have known patterns.

In the conditions of import substitution, domestic UEBA solutions integrated with Russian platforms, electronic signature systems and CIPF are developing in Russia. This makes it possible to use UEBA in government information systems and at CII facilities.

UEBA in the cybersecurity ecosystem

UEBA is not an isolated solution, but is part of a comprehensive information protection ecosystem. For maximum effectiveness, UEBA is integrated with other components:

• SIEM systems: UEBA enriches SIEM with behavioral analysis data, adding context to security events. SIEM, in turn, provides UEBA with logs and events for analysis.
• DLP systems: UEBA helps DLP to detect not only the facts of data transfer, but also suspicious behavior preceding a leak (for example, mass file copying).
• EDR/XDR: UEBA analyzes user behavior on endpoint devices, supplementing EDR data about processes and files. Together they create a complete picture of activity.
• SOAR: UEBA transfers alerts to SOAR for automatic response: for example, when an anomaly is detected, the system can automatically block an account or isolate a device.

The implementation of UEBA requires an integrated approach. The design service will help determine data sources, configure ML algorithms and thresholds, and training courses will allow analysts to work effectively with the behavioral analysis system.