AD domain change audit software-simplifies system threat detection through user behavior analysis

With the progress of science and technology, the rapid development of network popularity, network threats are increasing, violations are becoming more and more common. Many companies rely on technology to protect themselves, but people also use technology to find ways to circumvent these defenses. As artificial intelligence enters the field of technology, enterprises need to have sufficient ability to integrate this new security technology.

Advantages and challenges in threat Detection of AD Domain change Audit Software

Internal threats remain a challenge for enterprises of all sizes, and one way to detect them is to establish a normal activity baseline specific to each user over an extended period of time, and then monitor any deviations from that standard. However, it is almost impossible to detect these deviations manually.

Another noteworthy is the false positive alarm, which is the main culprit for the distraction of delayed vulnerability detection. False positives can be reduced by setting thresholds specific to each user based on the level of activity rather than using a series of thresholds across the organization, but this is still a task that IT security professionals cannot perform manually.

AD Domain change Audit Software user behavior Analysis (UBA) engine

AD domain change audit software ensures that important network resources such as domain controllers audit and monitor all objects in the AD domain in real time-users, groups, GPO, computers, OU, DNS, AD schema and configuration information changes to generate reports, more than 200 detailed event-specific GUI reports and mail alerts.

UBA in the AD domain change audit software helps the IT security team simplify threat detection and improve system security by using machine learning to create a normal activity baseline specific to each user, detect potential threats in the security ecosystem and notify relevant personnel.

UBA combines data analysis and machine learning to define dynamic thresholds according to actual user behavior. With its alarm threshold, the number of false positives is reduced, so that the security team has enough time to focus on maintaining the real system security indicators.

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