Nirupam Samanta, CISSP, CCSP, is lead cybersecurity engineer at Visa.
With the rise of AI, cyberattacks have become more advanced, frequent and devastating—especially as threats like insider attacks, credential theft and lateral movement bypass traditional perimeter-based security methods.
Recent IBM research highlights that cybercriminals are now using AI and machine learning to automate and enhance attacks, including AI-driven phishing campaigns and malware that adapts to evade detection. Microsoft also found that AI is allowing for attacks to expand in both scale and efficiency, with AI tools aiding in reconnaissance, targeting and execution.
In response, security teams are turning to behavior-based detection methods like user and entity behavior analytics (UEBA), which leverages machine learning to identify anomalies in user behavior that may signal compromised accounts or insider threats.
This article explores how UEBA works and how organizations can implement it effectively.
The Evolution Of UEBA
Security systems have historically depended on predefined rules and pattern-matching to detect threats. While effective against known threats, these approaches struggle against novel or obfuscated attacks. Signature-based systems like traditional intrusion detection systems (IDS), for example, can struggle with subtle or long-term behavioral threats or when threat actors employ modern AI-driven techniques.
Security teams then shifted toward behavior-based analysis to address threats that bypass traditional detection methods. By analyzing how users and entities normally interact with systems, UEBA can detect changes in behavior rather than specific attack signatures.
The integration of AI and machine learning into security operations has significantly improved UEBA. Techniques such as clustering, classification and anomaly detection allow UEBA systems to process large volumes of data and discern subtle changes in behavior that might otherwise be missed by humans or static rules.
As a result, UEBA has become a cornerstone of modern security strategies.
The Core Components Of UEBA
UEBA solutions typically consist of four key components working together to detect and respond to threats:
1. Data collection involves ingesting information from a variety of sources such as identity access management (IAM) systems, security information and event management (SIEM) tools, Active Directory logs, virtual private network (VPN) access records, endpoint detection and response (EDR) platforms, cloud service logs and application or database access logs.
2. Once collected, the system builds baseline models to understand normal behavior patterns across users and entities, including login habits, application usage, network activity and device interactions.
3. Anomaly detection then compares current activity against these baselines using techniques like statistical thresholding, peer group analysis, temporal analysis and machine learning, assigning risk scores based on the severity and context of deviations.
4. Finally, alerting and response mechanisms correlate and prioritize anomalies to reduce false positives, often integrating with security orchestration, automation and response (SOAR) platforms to trigger automated actions such as account suspension, network isolation or step-up authentication.
By establishing baselines of normal activity and applying machine learning to spot deviations, UEBA enhances detection beyond traditional rule-based systems, supporting faster, more accurate responses.
The Use Cases Of UEBA
With its ability to detect behavioral anomalies in real time, UEBA supports a wide range of cybersecurity applications.
One of the most crucial roles that UEBA can play is in data protection and identity management. It can flag unusual data transfers, unauthorized access attempts and suspicious activity, including covert channels and tunneling.
When integrated with IAM, UEBA and AI can also provide real-time behavioral insights. Analyzing login habits, device use and peer behavior enables adaptive authentication, allowing seamless access for low-risk users, prompting MFA for medium risk or blocking access for high-risk activity.
Finally, and critically, UEBA helps identify compromised identities in real time through anomalies like logins from unusual locations or impossible travel. When combined with threat intelligence, this can help organizations uncover known attack tactics.
Conclusion
Adopting UEBA offers powerful threat detection benefits but comes with several challenges.
Companies may struggle with data integration, as UEBA requires large volumes of data from diverse sources like network logs, authentication systems, endpoints and cloud services. Consolidating and normalizing this data, especially in fragmented or legacy environments, can be complex and resource-heavy.
Importantly, organizations will also need to establish behavioral baselines, as UEBA systems work by understanding “normal” user activity. This can be particularly difficult in dynamic environments with frequent role or behavior changes. Limited historical data or inconsistent patterns can lead to false positives or negatives, reducing trust in alerts during early deployment.
Privacy and compliance concerns can impact adoption. UEBA’s detailed user monitoring can raise privacy issues under strict regulations like GDPR or CCPA. Organizations must implement proper governance, data anonymization and transparency to ensure legal and ethical compliance.
Finally, effective UEBA use demands expertise in machine learning, behavioral analytics and cybersecurity—skills often lacking in smaller organizations. The financial costs of acquiring, deploying and maintaining UEBA tools, along with ongoing tuning and response efforts, can be prohibitive.
To overcome these challenges, organizations need a strategic, phased approach—emphasizing strong data governance, cross-functional collaboration and investment in skills and technology. When implemented correctly, UEBA can become a foundational tool for keeping pace with cybersecurity threats in the age of AI.
The views and opinions expressed in this article are solely those of the author. They do not reflect the views, positions or policies of any organization or institution the author may be affiliated with unless explicitly stated.
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