AI-Powered Pervasive Computing: Discerning Threat Model to Unveil Security and Privacy Challenges

The integration of Artificial Intelligence (AI) with pervasive computing is transforming environments into intelligent, context-aware systems capable of seamless and intuitive user interactions. This convergence drives advancements across various domains, including smart homes, healthcare, and industrial automation. Traditional security paradigms often fall short in addressing AI-specific risks, such as adversarial attacks, data inference, and the complexities of autonomous decision-making in highly interconnected systems. However, it also introduces a range of security and privacy challenges that must be addressed. We critically analyse these emerging concerns, delving into the unique vulnerabilities arising from the fusion of AI’s inferential capabilities with pervasive computing’s ubiquitous data collection. We further explore why existing threat models designed for conventional IT infrastructures prove insufficient in adequately capturing the nuanced and dynamic risks posed by intelligent, autonomous, and deeply integrated pervasive systems. We present a practical threat model specifically tailored to highlight the key risks associated with AI-driven pervasive computing and discuss potential mitigation strategies to enhance security and safeguard user privacy in these interconnected environments.