A real-time
anomaly detection system for high-volume managed file transfers (MFT), consisting of: a secure, hardware-accelerated monitoring unit configured to interface with a managed
file transfer server and intercept
file transfer session data at wire-speed; a
metadata extraction engine embedded in the hardware-accelerated unit, the
metadata extraction engine configured to analyze protocol-specific session attributes, including, but not limited to,
file size, transfer duration,
encryption status, source and destination endpoints, transfer frequency, and
payload entropy; a contextual AI
inference engine communicatively coupled to the
metadata extraction engine, the AI
inference engine comprising a
deep learning model trained on labeled historical MFT activity logs to detect contextual deviations from normative behavior; a
federated learning architecture with a plurality of edge nodes, each hosting a local
anomaly detection model trained on localized transmission metadata and configured to synchronize with a central aggregator using differentially private gradient updates; an Explainable AI (XAI) subsystem integrated into and configured to generate human-readable anomaly justifications, feature importance maps, and
threat categorization labels; and a policy
orchestration module configured to dynamically execute pre-configured or AI-based security responses, where the security responses include selective session termination, quarantining of transferred files, generation of alerts, or redirection of MFT workflows.