Employees often play an unwitting role in many security incidents, from accidental data breaches to intentional malicious attacks. Unfortunately, most organizations don’t have the right protocols and processes to identify potential risks posed by their workforce.

Based on a survey conducted by SANS Institute, 35% of respondents said they lack visibility into insider threats, while 30% said the inability to audit user access is a security blind spot in their organizations. In addition, the 2023 X-Force Threat Intelligence Index reported that 41% of security incidents involved phishing for initial access.

This means that organizations are vulnerable to costly and damaging security incidents caused by their own people — whether through negligence or deliberate intent. Detecting insider threats is challenging for many security teams, and traditional security measures are no longer sufficient to address this issue. However, by leveraging user behavior analytics (UBA), organizations can detect and prevent insider threats more effectively.

What is user behavior analytics?

User behavior analytics (UBA) is a security software that detects unusual behavior and anomalies in user activity by collecting various data types. UBA uses machine learning, automation and artificial intelligence to analyze data from various sources, such as logs, network traffic and endpoint devices, to create a baseline of normal user behavior. UBA then monitors behavior in real-time and alerts security teams when it detects anomalies that could indicate an insider threat.

Benefits of user behavior analytics in detecting insider threats

UBA provides several benefits in detecting insider threats, such as:

• Ability to detect abnormal user behavior: UBA can detect unusual behavior, such as a user logging in from an unfamiliar device or location, accessing sensitive information during unusual hours or failing to log in multiple times.
• Contextual analysis: UBA can analyze user behavior against various contextual factors, such as the user’s job role and location, as well as other activities happening in the network. This helps identify anomalies that may be difficult to detect using traditional security tools.
• Reduced false positives: Advanced algorithms and machine learning can enable UBA to minimize false positives by distinguishing between normal and abnormal user behavior.
• Real-time alerts: UBA provides real-time alerts to security teams when anomalous behavior is detected, allowing them to act quickly to prevent a potential insider threat.

Use cases for user behavior analytics

There are several use cases for UBA in detecting insider threats:

• Detecting unauthorized access to sensitive data: UBA can detect when an employee accesses sensitive data not required for their job role, indicating a potential insider threat.
• Identifying compromised credentials: UBA can detect when an employee’s credentials have been compromised. These attackers gain access to authorized credentials through phishing schemes, brute-force attacks and other means.
• Detecting data exfiltration: UBA can detect when malicious actors attempt to exfiltrate data from compromised servers, workstations or other devices.

Leveraging UBA and SIEM to detect insider threats

Most organizations have a security information and event management (SIEM) solution to centralize log and flow data, correlate events, automate incident detection and response and manage compliance requirements. SIEM solutions can also help detect insider threats by integrating with UBA.

The IBM Security QRadar SIEM UBA app leverages advanced analytics and machine learning to establish a baseline of employee behavior patterns within your organization. By analyzing existing data within QRadar SIEM, the UBA app generates new insights into user behavior and risk, enabling you to detect and respond to threats proactively.

UBA adds two major functions to QRadar: risk profiling and unified user identities.

• Risk profiling: Assigning risk levels to security use cases, allowing for threat prioritization.
• Unified user identities: Combining disparate user accounts by analyzing data imported from various sources like Active Directory, lightweight directory access protocol (LDAP), reference tables or comma-separated values (CSV) files.

IBM QRadar SIEM UBA app leverages a machine learning add-on, which augments the UBA app. It includes rules and tuning, allowing you to determine the parameters that QRadar SIEM will use. Security teams can enhance the UBA capabilities and automate incident response, making it easier to detect and prevent insider threats.

If you want to learn more about leveraging UBA and SIEM to detect insider threats, sign up for our upcoming webinar on June 8, Uncovering the Hidden Risk: Leveraging QRadar SIEM to Address Insider Threats. During the webinar, we will explore how IBM Security QRadar SIEM can help your organization detect and respond to insider threats. Our IBM Security expert will demonstrate how the UBA app’s two essential functions, risk profiling and unified user identities, can be used to enhance your organization’s security posture.

If you are interested in learning more about QRadar SIEM, schedule a 1:1 demo with an IBM Security expert here.