Beyond Traditional SIEM: How UEBA Uncovers Hidden Security Threats
In today’s complex cyber landscape, signature-based security tools are no longer enough. Sophisticated attackers and insidious insider threats often bypass traditional defenses by using legitimate credentials and tools. This is where User and Entity Behavior Analytics (UEBA) emerges as a critical layer of defense, transforming how security teams detect and respond to threats.
Instead of just looking for known bad signatures, UEBA focuses on understanding what is normal within your environment and flags what is abnormal. It provides the context needed to separate real threats from the noise, empowering security teams to act decisively.
What is User and Entity Behavior Analytics (UEBA)?
User and Entity Behavior Analytics is a category of security solutions that uses machine learning, statistical analysis, and behavioral modeling to reveal threats that other tools miss. It operates on a simple but powerful premise: while attackers can steal credentials or exploit vulnerabilities, they cannot perfectly mimic the day-to-day behavior of a legitimate user.
The “entities” in UEBA refer to non-human actors like servers, routers, and endpoints. By monitoring both users and devices, the system builds a comprehensive picture of typical activity across the entire network.
How UEBA Works: From Baseline to Alert
UEBA technology operates through a multi-stage process that intelligently filters and analyzes vast amounts of data to pinpoint credible threats.
Building a Behavioral Baseline: The first step is data ingestion and learning. The UEBA system consumes logs and event data from various sources, such as Active Directory, VPNs, firewalls, and endpoint agents. Over time, it learns the unique patterns of each user and entity. This includes typical login times, geographic locations, applications used, data access patterns, and network traffic volume. This profile of “normal” becomes the baseline.
Detecting Anomalies: Once a baseline is established, the system continuously monitors for deviations. An anomaly is any action that falls outside a user’s or entity’s normal behavioral profile. Examples of detected anomalies include:
- A user logging in at 3:00 AM for the first time.
- An accountant attempting to access sensitive engineering files.
- A server suddenly communicating with an unknown external IP address.
- An abnormally large amount of data being downloaded from a file share.
Contextual Risk Scoring: Not every anomaly indicates a threat. A user logging in while on vacation is an anomaly but likely harmless. UEBA excels by adding context and applying a risk score to these deviations. A single low-risk anomaly may be ignored, but a chain of related anomalies will rapidly increase a user’s risk score. For example, an unusual login location followed by failed access attempts and finally a large data transfer will trigger a high-priority alert. This scoring mechanism is crucial for drastically reducing false positives and preventing alert fatigue for security analysts.
Generating Actionable Alerts: When a user’s or entity’s cumulative risk score crosses a predefined threshold, the system generates a high-fidelity alert. This alert presents analysts with a clear, contextualized timeline of the anomalous activities, allowing them to quickly understand the potential threat and begin their investigation.
Key Threats Uncovered by UEBA
By focusing on behavior, UEBA is uniquely effective at identifying threats that often operate under the radar.
Insider Threats: Whether malicious or accidental, insiders with legitimate access pose a significant risk. UEBA can detect when an employee begins accessing data unrelated to their job function, or a disgruntled employee starts hoarding data before resigning.
Compromised Accounts: This is one of the most common attack vectors. UEBA can identify when an attacker is using stolen credentials because the attacker’s behavior—such as using different tools, accessing new systems, or performing network reconnaissance—will deviate from the legitimate user’s established baseline.
Data Exfiltration: Spotting the slow and low theft of data is challenging for traditional tools. UEBA can flag unusual data movement, such as an endpoint suddenly sending large volumes of data to an external cloud storage service or a user accessing and downloading files in a pattern inconsistent with their normal duties.
Privilege Escalation: Attackers often seek to escalate their privileges to gain deeper access. UEBA can detect the signs of this, such as a standard user account attempting to run administrative commands or trying to add itself to a privileged group.
Actionable Security Tips for Leveraging UEBA
To get the most out of a UEBA solution, organizations should follow a few best practices:
Ensure Comprehensive Data Collection: The effectiveness of UEBA depends on the quality and breadth of the data it analyzes. Ensure you are feeding it logs from all critical sources, including domain controllers, file servers, VPNs, cloud applications, and endpoints.
Tune and Customize Your Models: While modern UEBA systems are highly automated, every organization is unique. Work with your security team to tune the models and risk thresholds to align with your company’s specific risk appetite and operational realities.
Integrate with Your Broader Security Ecosystem: UEBA should not be a silo. Integrate it with your SIEM and Security Orchestration, Automation, and Response (SOAR) platforms to enable automated responses, such as disabling a suspicious account or isolating an endpoint, for faster threat containment.
In conclusion, UEBA represents a fundamental shift toward a more proactive and intelligent security posture. By looking beyond static rules and focusing on the dynamic context of behavior, organizations can effectively uncover the stealthy threats that were once nearly impossible to find.
Source: https://www.kaspersky.com/blog/ueba-rules-in-kaspersky-siem/54060/
