Unsupervised AI

Whitepaper: Actionable Anomalies – How MixMode AI Makes Your Security Data Smarter

In today’s ever evolving cybersecurity landscape there are major problems facing professionals that continue to worsen. These problems center around a shortage of tools advanced enough to understand the baseline of a network in order to pinpoint anomalies and a massive information overload problem in the form of security alerts.

Generative Unsupervised Learning vs. Discriminative Clustering Technology: Which Prevents Zero-Day Attacks?

Knowing the difference between Discriminative and Generative Unsupervised Learning can tell you a lot about the effectiveness of a cybersecurity solution’s artificial intelligence, for example, whether or not that security solution can perform actions like identifying and stopping a zero-day attack.

Case Study: MixMode AI Detects Attack not Found on Threat Intel

In October, 2019 a MixMode customer experienced an incident where an external entity attacked a web server located in their DMZ, compromised it, and then pivoted internally through the DMZ to attempt access of a customer database. While the attacker was successful in penetrating the customer’s network, MixMode was able to detect the event before they were successful in penetrating the customer database.

Turning the Unsupervised Tables on the Turing Test

Unsupervised artificial intelligence, also known as context-aware or third-wave AI, is notoriously difficult to explain because there lacks an appropriate test to understand just how powerful the intelligence is. The widely known Turing Test for AI testing is no longer the right framework for modern-day AI testing.