Artificial Intelligence Enabled Zero-Trust Cyber Security Framework for Smart Healthcare Infrastructure

Abstract

Smart infrastructure deployment in healthcare, such as the Internet of Medical Things (IoMT) devices, cloud-based clinical platforms and intelligent patient monitoring systems, has greatly enhanced the vulnerabilities of the healthcare infrastructure. Traditional perimeter security solutions are not enough to defend against advanced attacks like ransomware, unauthorised access, data breaches or insider threats. These challenges led to proposing an AI-enabled Zero-Trust Cyber Security Framework for smart healthcare infrastructure that continuously verifies the users, devices, and network activities before providing access to critical medical resources. The plan in the proposed infrastructure is to combine Zero-Trust Architecture (ZTA) principles with an AI-based threat detection system that can detect unusual behavior and patterns of malicious traffic in real time. An adaptive intrusion detection, dynamic trust evaluation and intelligent access control are implemented with the help of a hybrid deep learning model. The experimental validation has been performed on cybersecurity datasets related to the healthcare industry and simulated smart healthcare traffic scenarios. The proposed framework achieves high results for attack detection accuracy, low false positive and low authentication latency compared to the conventional security techniques, as shown by results. The framework improves security resilience, scalability and instant protection for the next generation smart healthcare ecosystems.