The convergence of Artificial Intelligence (AI) and the Internet of Things (IoT) has enabled Artificial Intelligence of Things (AIoT) systems that support intelligent and responsive smart societies, but it also introduces major security and privacy concerns across domains such as healthcare, transportation, and smart cities. This Systemic Literature Review (SLR) addresses three research questions: identifying major threats and challenges in AIoT ecosystems, reviewing state-of-the-art security and privacy techniques, and evaluating their effectiveness. An SLR covering the period from 2020 to 2025 was conducted using major academic digital libraries, including IEEE Xplore, ACM Digital Library, ScienceDirect, SpringerLink, and Wiley Online Library, with a focus on security- and privacy-enhancing techniques such as blockchain, federated learning, and edge AI. The SLR identifies key challenges including data privacy leakage, authentication, cloud dependency, and attack surface expansion, and finds that emerging techniques, while promising, often involve trade-offs related to latency, scalability, and compliance. The study highlights future directions including lightweight cryptography, standardization, and explainable AI to support secure and trustworthy AIoT-enabled smart societies.
Khan et al. (Thu,) studied this question.