Tracking insider threats with AI

ADAudit Plus | February 11, 2019 | 1 min read

If you thought masked hackers in dark rooms spreading malware were your only security concern, think again. In its Insider Threat Report for 2018, Crowd Research Partners brought to light that almost 90 percent of organizations find themselves vulnerable to insider threats. What’s worse is that 50 percent of these organizations experienced an insider attack in 2018.

Malicious intent isn’t always the case in such incidents. Most of the time, it’s negligence or accidental disclosure of confidential data by employees that cause all the trouble. Either way, the real challenge is the timely detection and prevention of insider threats. However, accurately identifying such anomalous activities using traditional security solutions takes a lot of effort.

Detecting insider threats would be a lot easier with a user behavior analytics (UBA) tool powered by machine learning (ML) techniques. ML makes the job of security administrators easier by automatically spotting abnormal user behaviors without admins having to configure the threshold values and lose their bearings amongst a sea of false positive alarms.

Our guide User behavior analytics: Securing against the unexpected can help you gain better insights into how this is done. This e-book also explains how the UBA module of ADAudit Plus, a real-time AD change auditing solution, works. You’ll learn how the solution:

  • Spots abnormal user behavior by baselining each user’s normal activity pattern.

  • Detects privilege abuse by users.

  • Recognizes threats caused by negligence.

  • Distinguishes potential malware running on servers.

It doesn’t end there. There’s a lot more detail on how you can fortify your network using UBA in our free e-book.  You can also interact with one of our product experts in our free webinar to get better clarity on the matter.

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