MUMBAI, India, Jan. 9 -- Intellectual Property India has published a patent application (202541133934 A) filed by Madhankumar C; Jeevithaa R V; Jenifer. J; Jackson J; Mr. K. Gowrishankar; Dr. M. Krishna Sudha; and Srimathi T, Pollachi, Tamil Nadu, on Dec. 31, 2025, for 'cognitive zero-trust orchestration framework using continual learning for adaptive cyber-physical infrastructure protection.'
Inventor(s) include Jeevithaa R V; Jenifer. J; Jackson J; Mr. K. Gowrishankar; Dr. M. Krishna Sudha; and Srimathi T.
The application for the patent was published on Jan. 9, under issue no. 02/2026.
According to the abstract released by the Intellectual Property India: "Cognitive Zero-Trust Orchestration Framework Using Continual Learning for Adaptive Cyber-Physical Infrastructure Protection Abstract The present invention proposes a Cognitive Zero-Trust Orchestration Framework using Continual Learning for adaptive protection of cyber-physical infrastructure operating in dynamic and adversarial environments. Unlike conventional perimeter-based or static zero-trust models, the proposed framework introduces a cognition-driven security orchestration layer that continuously learns, reasons, and adapts trust decisions across heterogeneous cyber-physical components, including sensors, actuators, control systems, edge devices, and cloud services. The framework integrates continual learning mechanisms to enable real-time evolution of security policies based on changing system behaviors, threat patterns, operational contexts, and environmental conditions. A multi-layer trust evaluation engine dynamically computes trust scores using behavioral telemetry, identity attributes, temporal access patterns, and physical-state correlations, thereby eliminating implicit trust at all stages of interaction. The orchestration layer autonomously enforces micro-segmentation, adaptive authentication, and context-aware access control across distributed infrastructure without disrupting system availability. A key novelty of the invention lies in its ability to retain previously learned security knowledge while incorporating new threat intelligence, thereby preventing catastrophic forgetting and enabling long-term resilience against zero-day attacks, insider threats, and coordinated cyber physical intrusions. The framework further incorporates cognitive feedback loops that align cyber risk signals with physical process deviations, enabling early anomaly detection and proactive threat containment. The proposed system is applicable to critical infrastructures such as smart grids, industrial automation systems, intelligent transportation networks, healthcare cyber-physical systems, and defense installations. By combining zero-trust principles with continual learning and cognitive orchestration, the invention delivers a self-adaptive, scalable, and future-ready security architecture capable of protecting mission critical cyber-physical infrastructures against evolving and sophisticated attack vectors."
Disclaimer: Curated by HT Syndication.
