What is a use case for Machine Learning Exclusions?

The use case for Machine Learning Exclusions is particularly relevant when a file is consistently executing from a specific path. This scenario indicates a commonly trusted file that exhibits pattern behavior deemed benign by the system's learning model. By allowing these well-defined files to execute without triggering alerts or actions, organizations can streamline operations and reduce false positives, which enhances overall system efficiency.

This approach helps in situations where specific executables are known to perform valuable functions while being flagged as suspicious by automated systems. Machine Learning Exclusions are designed to learn and adapt to the normal behavior of applications over time, ensuring that trusted files or applications do not unnecessarily detract from performance or lead to unnecessary investigations by security teams.

In contrast, scenarios such as unpredictable file behavior or when blocking is not feasible would typically require a different strategy rather than exclusions, as there is an inherent risk associated with a lack of certainty regarding the safety of the application. Likewise, the need for all files to be detected ties back to maintaining robust security practices rather than creating efficient workflows for files that are already well-understood and trusted.