A HYBRID ARTIFICIAL INTELLIGENCE AND CRYPTOGRAPHY FRAMEWORK FOR SECURE DOCUMENT MONITORING

As cyber threats, data security breaches and unauthorized access are becoming more prevalent in today's digital landscape, traditional security solutions are not capable of adequately protecting sensitive documents. To counteract the challenges faced by organizations, this proposal will develop a hybrid security solution with next-generation capabilities by combining Natural Language Processing (NLP), Elliptic Curve Cryptography (ECC), and Honeypot-based monitoring systems. Using NLP, a unique fingerprint for each document will be generated based upon intelligent analysis, classification, and the creation of a unique signature or fingerprint for each document. Using ECC will
require strong encryption and digitally signed documents as part of the cryptographic process while also allowing for efficient key management. Using NLP to create realistic decoy documents that can be used as Honeypots will further provide an opportunity to mislead and trap prospective attackers. Because any interaction with the decoy document will log the intruder's IP address, access attempts, and other pertinent data through the use of an Intrusion Detection System (IDS). By integrating intelligent content analysis, proper encryption, and a deceptive defence strategy, the hybrid model will both protect unauthorized access to sensitive data as well as enable proactive threat detection and forensic investigation capabilities. Overall, this proposal will establish a scalable, flexible, and highly secure strategy for safeguarding critical digital documents in a high-risk environment