What do ML exclusions prevent apart from stopping file uploads to the CrowdStrike cloud?

Machine Learning (ML) exclusions are designed to allow specific files or processes to bypass the machine learning engine's analysis when determining if a file is malicious. By implementing ML exclusions, certain files may be declared safe based on criteria defined by the organization or user, thus avoiding unnecessary alerts or actions related to those files.

The correct answer indicates that ML exclusions specifically prevent static file-based detections. This means that files marked for exclusion won't be subjected to the same scanning and detection algorithms that apply to other files, which is important for preventing false positives or interruptions in operations that could result from detection flags raised by static analysis methods.

In the context of CrowdStrike, static file-based detections are often the first line of defense, relying on file signatures and characteristics to identify potentially harmful files. When an ML exclusion is applied, the file is exempted from this detection process, which can be critical for applications or files that have been vetted and are known to be benign yet could trigger alerts due to their nature or behavior.