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ManageEngine, the real-time IT management company, announce the addition of user behavior analytics (UBA) to ADAudit Plus, its integrated Active Directory (AD), Azure AD, Windows server, file server, and workstation auditing solution. Available immediately, the latest version of ADAudit Plus helps IT security teams detect previously undiscoverable threats while also reducing the number of false positives.

ManageEngine will be exhibiting ADAudit Plus and the rest of its IT management solutions at GITEX Technology Week through Oct. 18, 2018, at the Dubai World Trade Centre, Stand B7-01, Hall 7.

Insider threats continue to challenge organizations of all sizes, and detecting them requires establishing a baseline of normal activities specific to each user over an extended period of time and reporting any deviations from the norm.

It is humanly impossible for IT security professionals to perform those detection tasks, which is why insider threats fly below the radar of solutions that don’t utilize UBA. With its user behavior-based model, ADAudit Plus can detect potential insider threats and automatically notify concerned personnel.

False positives — alerts that indicate the presence of a threat that does not actually exist — are the leading cause for delayed breach detection. According to a survey published by SANS Institute in June 2017, only half of the respondents detected breaches in less than 24 hours. False positives can be reduced by setting thresholds specific to each user based on their level of activity rather than using a blanket threshold across the organization, but this is another task that’s impossible for IT security professionals to manually perform.

Caught between setting lower organization wide threshold values and triggering more false positives — or configuring higher threshold values and missing a breach — security teams often choose the former. With its dynamic alert thresholds, ADAudit Plus reduces the number of false positives and buys ample time for security teams to focus on the real indicators of compromise.

ADAudit Plus applies machine learning to create a baseline of normal activities that is specific to each user and only notifies security personnel when there is a deviation from this norm. For example, a user who consistently accesses a critical server outside of business hours wouldn’t trigger a false positive alert because that behavior is typical for that user. On the other hand, ADAudit Plus would instantly alert security teams when that same user accesses that server during a time they’ve never accessed it before, even though the access falls within business hours.