[2026 Latest] Behavior Detection to Prevent Insider Threats: Data Leakage Countermeasures in Zero Trust

In today's world, where corporate DX is advancing and cloud usage has become the norm, traditional "perimeter security" has reached its limits. Particularly serious are insider threats by users with legitimate privileges and "impersonation" via stolen accounts. These cannot be distinguished from normal business operations using traditional log monitoring or rule-based detection. Based on real-world experience in the field, this article explains the importance of "User and Entity Behavior Analytics (UEBA)," where AI captures "unusual user movements" in real-time, and specific countermeasures in a Zero Trust environment.

1. The Limits of Manual Monitoring and the Threat of "Low and Slow" Attacks

A common issue we encounter in the field is the frustration that "we collect massive amounts of logs, but can't determine what is abnormal." Traditional security measures are based on fixed rules, such as "denying access from specific IP addresses" or "prohibiting late-night logins." However, malicious insiders or attackers who have stolen credentials exfiltrate data bit by bit using legitimate tools during business hours.

This is a "Low and Slow" attack. Because it appears to blend into normal business workflows at first glance, detection via manual monitoring or traditional SIEM (Security Information and Event Management) is extremely difficult. The following graph shows the percentage of "internal factors" in recent data breach incidents.

In our consulting work, particularly for companies receiving In-house EC Site Construction and Growth Support, the handling of confidential information such as customer lists and sales data is a major challenge. The key to protecting brand trust lies in how effectively you can visualize the "behavior" of staff members who have access privileges to the management console.

2. How Behavior Detection AI Identifies "Insider Threats"

Behavior Detection AI (UEBA: User and Entity Behavior Analytics) learns "standard behavior patterns" for each user from historical logs. It profiles factors such as typical login times, frequently used devices, and file access trends, then "scores" any movements that deviate from these patterns.

For example, if an employee who normally only touches the accounting system suddenly accesses a shared server for engineers and starts downloading a large number of files, the AI will immediately raise an alert. Its greatest feature is the ability to detect "behavior unlike that person," even if the ID and password are correct. A common scenario in the field is the exfiltration of customer information by employees just before they resign; such actions can be prevented by detecting sudden spikes in access frequency or the connection of unusual USB devices.

3. Key Points for Security Implementation in a Zero Trust Environment

In the Zero Trust principle of "Never Trust, Always Verify," behavior detection is an indispensable component. Beyond mere implementation, it is necessary to build a system that integrally evaluates three points: "Identity (Who)," "Device (What)," and "Context (Under what circumstances)."

In actual support projects, tuning to reduce false positives is also crucial. Overly strict restrictions that ignore business processes can significantly decrease productivity. For instance, in the context of In-house EC Site Construction and Growth Support, a surge in access during sale periods is "normal" behavior. By reflecting such business seasonality and specificities in the AI's training data, it becomes possible to operate in a way that extracts only truly high-risk actions.