AI-Driven Approaches to Mitigate Data Privacy Risks in Healthcare and IT Cloud Environments

Abstract

In contrast to conventional perimeter-based security, zero trust deployment is a challenging task that calls for a new management strategy. In order to properly plan, evaluate, and manage their zero-trust cybersecurity, organisations will benefit from having a defined set of key success factors (CSFs). Wireless Body Area Networks (WBANs) have been widely used in healthcare systems, necessitating the development of new technologies like cloud computing and the Internet of Things (IoT) that can handle the processing and storage constraints of WBANs. However, a lot of security issues and difficulties were raised by this rapid cloud migration. It has the potential to improve healthcare efficiency and quality because to its scalability, robustness, flexibility, connection, cost reduction, and high-performance qualities. But it's also critical to comprehend the unique security and privacy dangers that come with this technology. This study focusses on a cloud-based home healthcare system. It presents a number of use cases and illustrates a cloud-based architecture. In order to strengthen the cybersecurity posture of cloud-based AI systems, this research also emphasises the significance of strong encryption methods, secure cloud setups, and regulatory compliance. This study attempts to provide a thorough overview of deep learning in cloud settings and its implications for the future of AI-driven cybersecurity solutions by addressing the twin aspects of innovation and obstacles.