To minimize false positives for required applications, what can be created?

Creating ML Exclusions is an effective way to minimize false positives for required applications. Machine Learning (ML) algorithms used in threat detection can sometimes misidentify benign applications or behaviors as malicious, leading to false positives. By establishing ML Exclusions, you inform the system to disregard certain applications or behavioral patterns during its analysis, thereby allowing legitimate applications to run without being flagged as potential threats. This helps maintain operational integrity and reduces unnecessary alerts while ensuring that the security posture remains strong.

While other options, such as network security groups, firewall rules, and incident reports, serve important roles in a security framework, they are not specifically aimed at minimizing false positives in the way that ML Exclusions are. Network security groups can manage access and control traffic flows, firewall rules are set to block or allow certain types of traffic, and incident reports are used for documenting security events but do not directly influence the algorithmic classification of applications. Therefore, the establishment of ML Exclusions specifically targets the challenge of minimizing false detections related to trusted applications.