An Intellyx Brain Candy Brief
AI – machine learning in particular – has rapidly come to dominate the cybersecurity threat detection market, the latest salvo in the unending cat-and-mouse game with hackers.
Most threat detection vendors depend upon supervised machine learning, which is generally faster and more accurate than its unsupervised cousin but depends upon a baseline as a starting point.
However, such baselines can be problematic in a world where hackers continually change their tactics to evade detection.
In contrast, SecBI begins with unsupervised machine learning that consumes vast quantities of log data and other timestamped information to determine clusters of suspicious events that warrant further investigation.
Only then does SecBI feed such clusters into its supervised learning algorithms, thus reducing false positives and saving the time and effort of security analysts, so they can focus their efforts on mitigating any threats.
Copyright © Intellyx LLC. Intellyx publishes the Agile Digital Transformation Roadmap poster, advises companies on their digital transformation initiatives, and helps vendors communicate their agility stories. As of the time of writing, none of the organizations mentioned in this article are Intellyx customers. To be considered for a Brain Candy article, email us at pr@intellyx.com.
