AI-based ransomware detection focuses on identifying malicious file-encryption activities and abnormal file-system behavior that may indicate a ransomware attack against Linux-based file services. It involves monitoring file operations, access patterns, file modification frequency, and behavioral changes associated with shared-file access and file-system activity to distinguish potentially malicious encryption behavior from legitimate operations. By combining machine learning-based behavioral classification with deep learning-based temporal sequence analysis, this approach helps recognize ransomware indicators, detect suspicious activity patterns, and identify potential attacks at an early stage. The resulting detections support timely investigation, appropriate containment measures, and improved protection of files and shared resources against unauthorized encryption and potential data loss.