Insider threats pose a significant concern for organizations, both in terms of security and financial implications. According to IBM’s Cost of a Data Breach report, insider threats accounted for the costliest initial attack vectors, with an average cost of 4.9 million U.S. dollars per organization.
To combat these threats effectively, organizations need a solution that can quickly identify and contain insider threats. User Behavior Analytics (UBA) with AI and machine learning is one such solution that can significantly enhance an organization’s security posture.
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How UBA Works
UBA leverages machine learning to analyze user behavior and identify anomalies and potential threats. When integrated with a Security Information and Event Management (SIEM) solution, UBA can assist security professionals in detecting and responding to insider threats more effectively.
Let’s take a closer look at how UBA works as part of a leading SIEM solution, such as QRadar SIEM from IBM Security.
UBA in Action with QRadar SIEM
The QRadar SIEM platform comes with a built-in UBA app that enables security analysts to quickly review alerts and identify insider threats. By combining use cases or rules with machine learning, UBA learns the normal behavior of users on a network and their associated peer groups. It takes a minimum of seven days for UBA to learn user patterns and detect suspicious anomalies.
The UBA app provides a comprehensive dashboard with various tools to understand current risks. Analysts can prioritize employees based on risk, create watch lists for specific groups, and view alerts or offenses generated by the UBA app.
Investigating Insider Threats
When viewing a risky employee, analysts can access relevant information about their behavior, including the number of identities associated with the person, offenses related to the individual, a timeline of user events, and Indicators of Compromise (IOCs) associated with each event.
The QRadar SIEM platform also provides high-level information about potential security threats, including correlated events, source and destination IP addresses, and mitre attack mappings. Analysts can quickly assess key observables, criticality levels, threat actors, malware families, and high-value assets and users involved.
To enhance its analysis, QRadar encourages human feedback from security professionals to validate or challenge its findings. Additionally, natural language insights allow analysts to quickly identify IOCs and observed events.
Visualizing Relationships and Investigations
QRadar offers an offense relationship graph that visualizes key IOCs and relationships within an alert. Analysts can toggle between relationships found through internal event and flow data analysis and those uncovered using AI for external research. Human feedback can be provided to reinforce QRadar’s learning.
Once an investigation is complete, analysts can review other insider threats, IOCs, and correlated information within the offenses summary.
Conclusion
QRadar SIEM, integrated with UBA, represents a significant advancement in leveraging AI and automation for security operations. By streamlining processes, enhancing skills, and providing actionable insights, QRadar empowers security analysts to stay ahead of emerging threats and fortify their organization’s defenses.
To learn more about QRadar SIEM and experience its capabilities firsthand, visit the Techal website. Stay proactive in protecting your organization from insider threats with AI-infused SIEM.
