AI-DRIVEN ZERO TRUST SECURITY MODELS FOR PROTECTING CLOUD AND IOT INFRASTRUCTURES

Abstract

The fast increase in the number of cloud computing and Internet of Things (IoT) devices has radically changed the infrastructure of organizations, and it has also increased the area of the attack that can be used by adversaries. The conventional perimeter-based security models have become more and more insufficient in protecting against the increasingly complex multi-vector threats in these dynamic environments. This paper investigated how AI-driven Zero Trust security models help secure cloud and IoT environments by using a qualitative, exploratory research design. Fifteen cybersecurity professionals, such as cloud security engineers, IoT specialists, and IT managers, took part in semi-structured interviews and were complemented by analyzing documents on industrial reports, white papers, and organizational security policies. Thematic analysis was used to analyze the data and identified four fundamental themes, namely, the ineffectiveness of traditional security environments, the operational benefit of ongoing AI-driven authentication and surveillance, implementation challenges such as resource limitations and intricacy of integration and (4) future directions toward self-healing, autonomous security environments. Results suggest that AI-enhanced Zero Trust architectures have substantial benefits in terms of improvements in the accuracy of threat detection, shorter mean time to respond, and facilitating adaptive policy implementation in heterogeneous settings. The paper has concluded that smart, context sensitive security structures have to be the primary goal of organizations in order to be resilient to the changing cyber threats. Discussed are implications to practice, policy, and future research.