How it Works
The system analyzes historical file metadata and behavioral patterns using machine learning and statistical profiling. It builds a baseline of normal activity — such as file sizes, access times, formats, and content structures — and continuously monitors incoming files against this baseline. When a file exhibits unusual characteristics, such as unexpected size spikes, missing fields, or irregular access patterns, it is flagged as anomalous. This approach allows for dynamic, data-driven anomaly detection that evolves with the system over time.
