Exabeam

Exabeam

Minimal resources, high level of security

Visualization of normal user operations and detection of signs of threats

Today's advanced targeted attacks and internal fraud can be carried out by disguising legitimate credentials, devices, and work. As a result, it is difficult to determine whether a user's behavior is normal or malicious, and it is becoming increasingly difficult to detect it with existing solutions or with the human eye. Exabeam SMP utilizes machine learning (UEBA) on collected logs to generate normal behavior patterns for each person and each machine. When abnormal behavior that deviates from the normal behavior pattern is detected, the degree of risk of that behavior is automatically scored, displayed on the Exabeam SMP timeline, and notified.

No advanced skills required. Visualize threats with “stories” from huge logs

A feature of Exabeam SMP is that it creates a timeline of each user's actions by automatically linking target logs to people or devices and organizing them in chronological order using Exabeam's patented technology. Not only logs of abnormal behavior (alerts, etc.), but all logs including logs of normal behavior (access logs, etc.) are targeted. Since all actions are displayed as a story on the timeline, it is easy to compare and check previous and subsequent actions and normal actions, enabling operators without specialized knowledge to quickly judge threats and understand the situation. is possible.

Simple and intuitive user interface

In order to perform security operations quickly, operators need to be able to understand information correctly and quickly. In addition, in order to understand information, specialized knowledge of various products, knowledge of security, knowledge of the internal environment, etc. are required, but such human resources are in short supply not only in Japan but worldwide. Exabeam SMP supports the efficiency of customers' security operations by providing an easy-to-understand user interface with information that is easy to understand.

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