Published Paper Details:

This project introduces a real-time, AI-powered system for detecting data download duplication, modification, and authentication issues. Moving beyond traditional hash- based integrity checks, the system employs machine learning models to establish and monitor the behavioral fingerprint of files and their sources. The implementation integrates a crucial Multi Factor Authentication (MFA) layer, requiring users to verify their identity time-sensitive security code before executing sensitive actions like overriding an AI alert or modifying system settings. Key technologies include TensorFlow/Py Torch for AI/ML modeling, a PostgreSQL database for storing file metadata and behavioral data, and a flexible alerting system for notifying users in real time. By integrating advanced analytics with a comprehensive security framework and strong user authentication, this solution aims to significantly enhance data integrity, security, and operational resilience against a wide range of cyber threats and human errors.