Detecting suspicious activity by rogue users is a difficult challenge. Traditional rule-based approaches to user threat detection generate so many alerts that you can’t possibly investigate them all; you end up wasting your time chasing down false positives and risk missing the real threats altogether, leaving your organization at risk of a data security breach. But what if you had sophisticated pattern recognition that raises alerts only when user behavior is anomalous enough to be truly indicative of compromised credentials or privilege abuse?
Quest® Change Auditor Threat Detection uses advanced machine learning, user and entity behavioral analytics (UEBA), and SMART correlation technology to accurately spot anomalous activity and identify the highest risk users in your environment, so you can effectively protect your data and your business. This tech brief explains how it works.