How does Mimecast handle false positives in threat detection?

Mimecast effectively addresses false positives in threat detection by employing continuous machine learning to improve accuracy. This approach enables the system to learn from various data points and user interactions, refining its algorithms over time to distinguish between legitimate emails and genuine threats more effectively. As more data is analyzed, the machine learning models become adept at recognizing patterns that signify whether an email is likely to be harmful or safe.

This process not only enhances the overall accuracy of threat detection but also reduces the occurrence of false positives, ensuring that users face fewer interruptions from emails that are mistakenly flagged as threats. The dynamic nature of machine learning allows Mimecast to adapt to emerging threats and changing tactics employed by malicious actors, thereby maintaining robust email security for its users.

While continual learning is an advantage, approaches like limiting detection capabilities, ignoring user reports, or manually reviewing every flagged email would either compromise security by increasing risk, overlook valuable user feedback, or become impractical due to the high volume of emails, respectively. Thus, continuous machine learning stands out as a proactive and effective method for handling false positives.