AI-Augmented Zero-Trust Security Architecture for Next-Generation IoT Devices
DOI:
https://doi.org/10.63345/z5s2wr12Keywords:
Zero-Trust Architecture (ZTA), AI Security, IoT Security, Machine Learning, Threat Detection, Network Anomalies, Identity VerificationAbstract
The rapid proliferation of Internet of Things (IoT) devices has led to significant security
challenges, as traditional perimeter-based security models fail to protect against sophisticated
cyber threats. Zero-Trust Architecture (ZTA) has emerged as a promising security model,
assuming that no device, user, or network segment is inherently trustworthy. However, traditional
Zero-Trust implementations struggle with scalability and real-time threat detection. This paper
proposes an AI-Augmented Zero-Trust Security Architecture (AI-ZTA) that integrates machine
learning (ML), deep learning (DL), and behavioral analytics to enhance authentication, anomaly
detection, and adaptive security policies. The proposed framework improves threat detection
accuracy, minimizes false positives, and ensures continuous security monitoring for IoT
ecosystems. Empirical results demonstrate that AI-driven ZTA improves security efficiency,
reduces attack surface, and enhances resilience against evolving cyber threats.
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Articles are published under the Creative Commons Attribution NonCommercial 4.0 License (CC BY NC 4.0), allowing others to distribute, remix, adapt, and build upon the work for non-commercial purposes while crediting the original author.
