What is a primary characteristic of machine learning in Illumio's threat detection strategy?

The primary characteristic of machine learning in Illumio's threat detection strategy is that it learns from patterns to improve detection over time. This capability allows the system to adapt to new and evolving threats by identifying anomalies and understanding normal behavior within the network. As it processes more data, the machine learning model refines its algorithms and enhances its ability to make accurate threat predictions and detections.

Unlike manual intervention, which can slow down the response to security incidents, the machine learning approach automates the detection process, enabling faster identification of potential threats. Additionally, the system does not ignore small-scale threats; instead, it embraces a comprehensive view of potential risks, irrespective of their scale. Lastly, relying on a fixed set of rules would hinder the adaptive nature of the system, as it would not be able to evolve with new threats known only through emerging data patterns. Thus, the capability to learn from patterns is crucial for maintaining an effective and proactive threat detection strategy.