MUMBAI, India, Oct. 12 -- Intellectual Property India has published a patent application (202641119477 A) filed by Dr. K Sandhyarani Kundra; Dr. I. Karthiga; Dr. Komal Garg; R. Usha; Rinku Badgujar; Ms. C. Sujatha; Dr. Panem Charanarur; Mrs. Ussain Kadher Ibrahima; L. Javid Ali; Dr. Archana Kumari Prasad; Ms. Isha Tewari; and Mrs. P. Nirmala on October 06, 2026, for Ai-Driven Intelligent Data Privacy Preservation And Protection System.
Inventors include Dr. K Sandhyarani Kundra; Dr. I. Karthiga; Dr. Komal Garg; R. Usha; Rinku Badgujar; Ms. C. Sujatha; Dr. Panem Charanarur; Mrs. Ussain Kadher Ibrahima; L. Javid Ali; Dr. Archana Kumari Prasad; Ms. Isha Tewari; and Mrs. P. Nirmala.
The application for the patent was published on October 09, 2026, under issue no. 41/2026.
Abstract: The present invention discloses an AI-Driven Intelligent Data Privacy Preservation and Protection System configured to secure sensitive information throughout collection, storage, processing, transmission, sharing, retention, and deletion. The system comprises a data acquisition and integration module, sensitive-data discovery and classification module, identity and context verification component, behavioral monitoring module, contextual privacy-risk assessment engine, privacy-preserving control module, automated response module, consent and policy management module, audit repository, and reporting interface. Artificial intelligence and machine learning models identify personal, financial, medical, biometric, confidential, and organizational data and assign protection levels according to sensitivity, ownership, purpose, regulatory relevance, and exposure impact. User, device, application, network, and data-access activities are continuously analysed to detect abnormal behavior, unauthorized operations, privilege misuse, and information leakage. A dynamic risk score is generated using data classification, identity, location, time, device trust, historical behavior, access purpose, and threat indicators. Based on the risk score, the system selectively applies encryption, masking, anonymization, pseudonymization, tokenization, data minimization, secure transmission, and adaptive access control. Detected threats trigger automated actions including additional authentication, access restriction, transfer blocking, session termination, device isolation, alert generation, and evidence preservation. The system verifies consent, processing purposes, retention periods, and privacy policies before authorizing data operations. Tamper-evident audit records and explainable reports support accountability, investigation, and regulatory compliance. Outcomes and administrator feedback continuously refine the models, thereby reducing false alerts and improving detection accuracy. The invention provides proactive, adaptive, scalable, explainable, and automated privacy protection across cloud, enterprise, mobile, healthcare, financial, governmental, educational, and Internet of Things environments.
Disclaimer: Curated by HT Syndication.
