MUMBAI, India, Sept. 14 -- Intellectual Property India has published a patent application (202611087982 A) filed by Manipal University Jaipur on July 18, 2026, for A Threat Modeling Framework For Security, Privacy, And Reliability Assessment Of Ai-Enabled Liver Digital Twins.
Inventors include Sanskriti Srivastava; and Amit Kumar Sharma.
The application for the patent was published on September 11, 2026, under issue no. 37/2026.
Abstract: The present invention relates to a multi-layer threat modeling framework for assessing and enhancing the security, privacy, and reliability of artificial intelligence (AI)- enabled liver digital twin (LDT) systems. The framework comprises three layers: Layer 1 (Data Acquisition) enforces the use of secure hardware modules and sensors. Sensor authentication and anomaly detection to make sure the signal is safe and good to go; Layer 2 (Communication & Storage) to make sure the data is not vulnerable to changes and attacks, the layer uses end to end encryption for the transfer and communication of data and a Blockchain based audit trail. That way, we would have a solid data transfer record which is untouchable. To also reduce the frequency of data transfer from cloud to local machines, sensitive data will be used to train model local; and Layer 3 (Computation & Deployment) to identify bad inputs or dangerous inputs, denial of system level prompts and continuous integrity checks to protect the model from manipulation and reverse engineering. The framework thereby provides a unified threat modeling and protection architecture for identifying, assessing, and mitigating security, privacy, and reliability threats throughout the lifecycle of an AI-enabled liver digital twin. The framework addresses the expanded attack surface created by the integration of IoMT devices, distributed medical sensors, edge computing systems, cloud-based storage, communication networks, and AI-based computational models, thereby improving the trustworthiness, resilience, integrity, and secure operation of the liver digital twin.
Disclaimer: Curated by HT Syndication.
