MUMBAI, India, July 24 -- Intellectual Property India has published a patent application (202611060630 A) filed by Dr. Rohit Kumar Verma; Dr. Pratibha Sharma; Prof. Dr. B. K. Sarkar; and Dr. Saurabh Kumar on May 13, 2026, for Ai-Driven Dynamic Framework For Real-Time Cyber Threat Assessment And Navigation.
Inventors include Dr. Rohit Kumar Verma; Dr. Pratibha Sharma; Prof. Dr. B. K. Sarkar; and Dr. Saurabh Kumar.
The application for the patent was published on July 17, 2026, under issue no. 29/2026.
Abstract: The present invention discloses an Artificial Intelligence (AI)-driven dynamic framework for real-time cyber threat assessment and navigation in complex and distributed digital environments. The proposed system integrates multiple functional modules, including data acquisition, pre-processing, AI-based analytics, threat assessment, and adaptive response, to provide a comprehensive and intelligent cybersecurity solution. The framework continuously collects and processes heterogeneous data from sources such as network traffic, system logs, endpoint devices, cloud platforms, and external threat intelligence feeds. The invention employs advanced machine learning and deep learning algorithms to detect anomalies, classify threats, and predict potential cyber-attacks, including zero-day vulnerabilities, Distributed Denial-of-Service (DDoS) attacks, and Advanced Persistent Threats (APTs). A dynamic risk assessment mechanism evaluates identified threats based on severity, likelihood, and potential impact, enabling effective prioritization and decision-making. A key feature of the invention is its cyber threat navigation capability, which provides intelligent guidance for selecting optimal mitigation strategies. The system supports automated and semi-automated response actions such as traffic filtering, system isolation, and policy reconfiguration, thereby reducing response time and minimizing potential damage. Additionally, a decision orchestration engine ensures seamless coordination among system components and integration with existing security infrastructures. The framework further incorporates explainable AI features for enhanced transparency and a continuous feedback mechanism to improve learning models over time. Overall, the invention enhances real-time threat detection, adaptive response, and resilience of modern cybersecurity systems.
Disclaimer: Curated by HT Syndication.
