10140063AI enabled multi-tier trust-management in Internet of Medical ThingsClosedCollaborative R&DInnovate UKThe IoMT devices without any exception are susceptible to cyber-attacks with the increase in cyber threats globally. Our market validation analysis suggests that security and data integrity challenges exist in IoMT devices, with a particular emphasis on legacy devices that were built without current day security in mind and are still in use. This not only raises serious health and safety concerns related to patients' life but also could potentially compromise protected patient information under GDPR UK regulations. We have conducted market validation through market analysis, survey, interviews and discussion with security experts in healthcare. The findings suggested the need of scalable and device independent solutions to enhance data integrity and safety of patients and end users. These were further endorsed by the recent key reports published by the Ponemon Institute and Cynerio, identifying risks which aligns with a requirement for scalable and manageable security solutions. The project proposed novel approach to trust management to improve the data security and integrity of the patients by leveraging the AI enabled deep learning methods and fuzzy logic, setting a new standard for medical device security. Utilising a growing AI sector, this can drive innovation in the healthcare sector, encouraging the development of more reliable and secure medical devices. With the market growth projected to soar, driven by the increased implementation of connected medical devices, the requirement for robust security measures becomes a valuable proposition. This approach takes advantage of a growing field that can offer a distinct advantage in terms of adaptability, scalability, and ease of deployment through easy to setup AI based device independent solution when compared to existing solutions. AI algorithm plays a vital role in this framework, leveraging behavioural anomalies in the device data to detect security threats, and predict the trustworthiness and integrity of medical devices and data streams.