AI-Based User Behavior Analytics Strategy: Paving the Way for New Horizons in Insider Threat Detection

Insider threats are recognized as one of the most critical attack vectors jeopardizing an enterprise's core assets and data. Insider threats, which can cause damage as significant as, or sometimes even greater than, external attacks, manifest in various forms, ranging from simple mistakes to malicious data exfiltration. They are often difficult to detect with traditional signature-based or rule-based security systems. Particularly, as organizational boundaries become blurred with the transition to cloud environments and the expansion of remote work, meticulously analyzing the behavior of trusted internal users has emerged as a frontline security imperative.

Recently, AI-based User Behavior Analytics (UBA) technology has been rapidly gaining prominence. This technology leverages sophisticated Machine Learning (ML) models to learn users' normal behavior patterns and detect abnormal activities deviating from these patterns in real-time, thereby presenting a new security paradigm for proactively addressing insider threats. Such AI-based UBA plays a crucial role in moving beyond simple log analysis to understand complex user behavior contexts and uncover hidden signs of threats. This analysis covers how AI-based UBA can be utilized for insider threat detection, along with specific strategies and implications for its practical application.

Severity of Insider Threats as Seen Through Key Data

Insider threats continue to increase, and the associated financial losses are also escalating. According to numerous industry reports, the average cost of losses due to insider threats rises annually, imposing significant burdens on enterprises. These threats are more critical as they can lead not only to financial losses but also to a decline in corporate trust, legal disputes, and regulatory violations.

Insider threats can be broadly categorized into incidents caused by errors, data leaks due to negligence, and malicious acts with intent. Malicious insider threats, in particular, tend to result in larger-scale damage due to the prolonged time required for detection. For instance, cases of sensitive data breaches frequently take several months to be discovered, which often leads to extensive data exfiltration.

The following table illustrates general trends in key indicators related to insider threats.

These figures clearly demonstrate that the adoption of AI-based UBA systems is no longer an option but a necessity. AI UBA effectively identifies subtle anomalous signs that traditional security solutions often miss, thereby enabling enterprises to strengthen their capability to proactively recognize and respond to potential risks.

The Rise of AI-Based Insider Threat Detection

The recent acceleration of cloud migration and the proliferation of remote work have dismantled traditional enterprise security perimeters. While these changes offer users greater flexibility and accessibility, they have concurrently expanded the potential pathways for insider threats. Insider threats, which previously occurred predominantly within physical networks, can now emerge through various cloud services and remote access environments. In such environments, unpredictable user behaviors frequently occur that formalized rules cannot anticipate, exposing the limitations of existing security systems.

Against this backdrop, insider threat detection leveraging AI, particularly Deep Learning-based Machine Learning models, is emerging as a new focal point. Intuitively, AI UBA learns the normal activity data of numerous users to establish a 'normal baseline' for each user. For example, it learns the typical behavior of a developer who accesses specific systems only at certain times and handles only particular types of files. Any behavior deviating from this baseline, such as accessing a database not typically used or moving a large volume of files to external storage, is automatically identified as an 'anomalous activity' and triggers an alert.

Unpacking the core principle, AI models detect patterns, statistical anomalies, or abnormal sequences within vast amounts of log data (e.g., access records, file access, network traffic). This offers the advantage of capturing even subtle changes that are difficult to identify with traditional threshold-based rules or signature-based detection methods. Continuous model tuning and reinforcement learning are conducted to reduce false positives and minimize false negatives, evolving towards enhancing responsiveness to actual threats. For example, SeekersLab's KYRA AI Sandbox can be utilized to pre-verify the safety and robustness of these AI models and to explore defense strategies against AI-based attack techniques, thereby contributing to increased reliability of AI-based insider threat detection systems.

The Evolution of User Behavior Analytics (UBA) and the Role of Next-Generation SIEM

User Behavior Analytics (UBA) has evolved beyond mere log analysis to comprehensively analyze various contextual information, including user identity, access privileges, devices, applications, and network activities. While traditional SIEM (Security Information and Event Management) systems primarily focused on collecting logs and detecting events based on formalized rules, they demonstrated limitations in addressing unpredictable insider threats. To overcome these limitations, UBA integrates big data technologies with AI/ML to analyze user behavior patterns from multiple perspectives.

The core of UBA lies in its ability to detect unknown threats by utilizing unsupervised learning and semi-supervised learning models. This implies that the system can autonomously learn normal user behavior and identify anomalous indicators without pre-defined rules. For instance, if an employee who typically accesses the internal network only during business hours attempts to connect to a sensitive server from an external IP address late at night, this can be classified as a clear anomalous activity. UBA captures such 'jump points' and generates alerts, enabling security personnel to initiate immediate investigations.

These UBA capabilities are integrated into next-generation Seekurity SIEM/SOAR solutions, thereby generating even more powerful synergies. Seekurity SIEM consolidates and analyzes vast log data collected from various sources, providing deep insights into user and entity behavior through its UBA engine. This enables effective detection of complex insider threat scenarios such as user account compromise, privilege misuse, and attempts at internal information leakage. Furthermore, Seekurity SOAR executes automated response actions based on predefined playbooks for threats detected by UBA, preventing threat proliferation and maximizing security operations efficiency. For example, upon detecting suspicious file download activity, it can perform automated actions such as temporarily blocking the user's network access or requesting device isolation.

Integration with Zero Trust Principles

In recent years, 'Zero Trust' has become a core principle of security architecture. This principle, 'Never Trust, Always Verify,' mandates treating all users and devices, even those within the internal network, as potential threats and subjecting all access requests to stringent verification procedures. Its objective is to complement the traditional 'perimeter-based security' model, which was vulnerable to insider threats due to excessive trust placed on the internal network.

In a Zero Trust architecture, AI-based UBA plays an essential role in implementing the core element of 'Continuous Verification'. Instead of unconditionally trusting a user once they have connected to the system, their behavior is continuously monitored and analyzed post-connection, and immediate responses are taken if abnormal activities are detected. For example, UBA can detect in real-time data access that deviates from a user's normal work patterns, logins at unusual hours, or attempts at privilege escalation, thereby determining whether Zero Trust policies have been violated.

Particularly in cloud environments, solutions like FRIIM CNAPP/CSPM manage the security posture and vulnerability management of cloud infrastructure, while UBA continuously monitors the behavior of users accessing cloud resources. If FRIIM CSPM detects specific cloud misconfigurations and UBA simultaneously identifies anomalous access patterns to those resources, this could be interpreted as an insider's attempt to exfiltrate data through intentional configuration changes. This integrated approach provides a robust defense mechanism against increasingly complex insider threats in cloud environments, and, in conjunction with the Principle of Least Privilege, enables proactive responses such as automatically adjusting user access privileges upon detection of anomalous activities.

Industry-Specific Impact and Response Status

The strategy for insider threat detection through AI-based UBA varies in importance and application method depending on industry-specific characteristics and regulatory environments. Each industry possesses unique sensitive information and operational environments, necessitating a tailored approach.

• Financial Industry: This is one of the most strictly regulated industries (e.g., Electronic Financial Transaction Supervision Regulations, PCI DSS), where the security of customer financial information and transaction data is paramount. UBA is essential for real-time detection of activities attempting financial fraud and internal information leaks, such as high-value transfers, anomalous account access, and mass customer information inquiries. Maintaining compliance and preventing large-scale losses through real-time detection and automated responses is critical.
• Manufacturing Industry: The leakage of core technologies and intellectual property (IP) poses a significant threat. Research and Development (R&D) data, design blueprints, and production process information, if leaked to competitors, can result in immense damage. UBA focuses on detecting signs of intellectual property leakage, such as CAD system access patterns, mass file movements from specific servers, and the use of external storage devices. Additionally, monitoring system access behavior in Operational Technology (OT) environments is also important.
• Public Sector: Protecting citizens' personal information and securing critical national information are paramount. Compliance with domestic regulations such as the Personal Information Protection Act and ISMS-P is crucial, and UBA monitors public system access activities, civil complaint information inquiry patterns, and attempts at unauthorized information leakage. Special emphasis is placed on detecting anomalous activities of internal users with access privileges to large-scale databases.
• IT and Cloud Service Industry: Developer account compromise, cloud environment misconfigurations, and unauthorized service code alterations are key threats. Due to the dynamic nature of cloud environments, traditional security solutions alone have limitations. It is crucial to continuously analyze cloud infrastructure security vulnerabilities with FRIIM CSPM/CNAPP and monitor developer or administrator access and modification activities to cloud resources in real-time with UBA, thereby detecting insider threats within the DevOps pipeline early.

Each industry must optimize its UBA system and configure detection rules and response playbooks according to these specific characteristics. Building and operating security systems from an integrated perspective can be considered key to enhancing practical defensive capabilities.

Expert Insights

AI-based UBA is an innovative technology that is transforming insider threat detection, but its adoption and operation require careful consideration. Several key implications are presented below.

Insights from a Technical Perspective

• Importance of Data Quality and Quantity: The performance of AI models heavily depends on the quality and quantity of training data. Without accurate and abundant log data, UBA struggles to function effectively. Therefore, the process of collecting and integrating refined data from various systems must precede. Seekurity SIEM excels in providing such extensive data collection and refinement capabilities.
• Minimizing False Positives and False Negatives: Early AI UBA systems often generate numerous false positives, increasing the fatigue of security teams, or conversely, miss actual threats due to false negatives. Detection accuracy must be enhanced through continuous model tuning, learning new threat scenarios, and a hybrid approach with behavior-based rules. Utilizing KYRA AI Sandbox to pre-validate potential false positive scenarios and optimize models is particularly effective.
• Adoption of Explainable AI (XAI): The ability for an AI model to explain why it flagged a particular behavior as anomalous is highly important for security analysts. By clearly presenting detection rationales through XAI, analysts can quickly understand the situation and make appropriate response decisions.

Implications from a Business Perspective

• Long-term ROI vs. Initial Investment: The implementation of an AI UBA system may require substantial initial investment. However, considering the immense costs associated with data breaches, legal disputes, and damage to corporate image due to insider threats, UBA can offer a significantly higher long-term ROI. It is a critical investment contributing to business continuity and enhancement of corporate trustworthiness.
• Improving Security Culture and Awareness: Ultimately, people remain critical, regardless of how advanced the technology. Alongside the adoption of AI UBA, education and campaigns to raise employees' security awareness regarding insider threats must be concurrently implemented. Furthermore, it is essential to clearly communicate that UBA is a tool to protect both the enterprise and its employees, rather than a surveillance tool, to reduce resistance.

Key Message for Decision-Makers

Insider threats are no longer negligible business risks; they are critical issues that can jeopardize the existence of an enterprise. AI-based UBA can be regarded as the unique alternative capable of transcending existing limitations and establishing a proactive and intelligent defense system against insider threats. Organizations should focus on protecting their core assets and strengthening their defensive capabilities against unpredictable future threats through the adoption of specialized solutions such as Seekurity SIEM/SOAR and KYRA AI Sandbox.

Response Strategy: A Roadmap for Practical Implementation

To successfully adopt and operate AI-based UBA, a clear strategy and a phased approach are necessary. The following are short-term and mid- to long-term response plans applicable in practice.

Short-Term Strategy: Rapid Visibility Acquisition and Core Threat Detection

• Integration with Existing Systems and Data Consolidation: Begin by collecting data from currently operational log management systems, SIEM, and IAM (Identity and Access Management) solutions and integrating it with the AI UBA engine. Seekurity SIEM is optimized for providing such extensive data collection and refinement capabilities, making it effective for securing foundational data for UBA.
• Initial Model Training and High-Risk User Identification: Primarily, train the AI model based on user behavior data for critical systems (e.g., core databases, file servers, cloud resources). This enables the identification of high-risk users and the establishment of initial alerts for anomalous access patterns or data movements.
• Strengthening Detection Rules Based on Insider Threat Scenarios: Prioritize strengthening UBA's detection rules based on past insider threat cases or key scenarios reported in the industry. This serves to complement specific threat patterns that the AI model might miss during its initial learning phase.

Mid- to Long-Term Strategy: Building an Integrated Defense System and Enhancing Threat Prediction Capabilities

• Adoption of Zero Trust Architecture and UBA Integration: Gradually implement Zero Trust principles across the organization and integrate UBA as a core component of Zero Trust policies. This enables continuous verification and dynamic privilege adjustment for all users and devices. FRIIM CNAPP/CSPM supports the implementation of Zero Trust in cloud environments and, when integrated with UBA, can establish an even more robust defense system.
• Establishing Data Governance and Classification Systems: Establish a data governance framework that clearly defines the location, access privileges, and criticality of sensitive data. This becomes an essential foundation for UBA to assess the criticality of detected anomalous activities and determine response priorities.
• AI Model Safety and Preparedness for LLM-Based Threats: Continuously verify the robustness and potential vulnerabilities of UBA models using KYRA AI Sandbox. Furthermore, as the potential for insider threats exploiting Large Language Models (LLMs) increases, research and preparation are necessary to enhance UBA's detection capabilities against new attack techniques, such as LLM-based Prompt Injection.
• Security Awareness Education and Professional Capability Enhancement: Conduct regular security awareness training for employees, specifically explaining the risks of insider threats and the role of AI UBA. Moreover, professional capabilities for the continuous improvement of UBA systems must be strengthened through close collaboration among data scientists, security analysts, and AI/ML engineers.

Through such a phased approach, enterprises will be able to systematically strengthen their defenses against insider threats and effectively respond to a rapidly changing threat landscape.

Conclusion

Insider threats remain one of the most significant challenges in the enterprise security landscape. Traditional static and rule-based security solutions have shown limitations in effectively detecting sophisticated and unpredictable insider threats. In this context, AI-based User Behavior Analytics (UBA) is emerging as an essential strategy for protecting an enterprise's core assets, presenting a new paradigm for insider threat defense.

AI UBA learns normal user behavior based on vast data and precisely detects anomalous indicators deviating from it, contributing to the reduction of false positives and minimization of false negatives. Furthermore, its integration with Zero Trust principles enables continuous verification for all access and supports customized threat detection and response tailored to the specific characteristics of industries such as finance, manufacturing, public sector, and IT. SeekersLab's Seekurity SIEM/SOAR integrates this AI UBA, enabling real-time threat detection and automated responses. FRIIM CNAPP/CSPM enhances security in cloud environments, and KYRA AI Sandbox assists in building an even more robust insider threat defense system through AI model safety verification.

Strengthening proactive defense capabilities against insider threats is a task that can no longer be postponed. Through the adoption and continuous improvement of AI-based UBA, enterprises will be able to protect themselves from unpredictable future threats and ensure business continuity. It is crucial to continuously monitor how AI technology and the threat landscape evolve and to prepare proactive response strategies accordingly.

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