To effectively optimize ransomware detection and reduce false positives, a multi-faceted approach is best: B: Correlate with other suspicious activities: This is a cornerstone of high-fidelity detection. Ransomware doesn't just encrypt files; it often performs other malicious actions like deleting shadow copies Cvssadmin delete shadows), making network connections to C2, or attempting to disable security services. Correlating these events with file encryption drastically reduces false positives. C: Exclude legitimate applications: Directly excluding known, legitimate applications (like 'WinZip.exe', that might perform encryption is a simple yet very effective way to eliminate a large class of false positives. D: Leverage Machine Learning/Behavioral Analytics: XSIAM's strength lies in its ML and behavioral analytics. For complex threats like ransomware, these capabilities can identify anomalous patterns of file modification, encryption, and deletion, even without specific IOCs. This complements indicator rules by detecting new or obfuscated variants. This might involve creating a new behavioral rule or relying on existing XDR analytics. Option A would increase false positives. Option E is too simplistic; ransomware affects files of all sizes, and a IGB threshold would miss many attacks.