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@article{189260,
        author = {Apurba Das and Dr Shameemul Haque},
        title = {AI-Native Zero-Trust Operational Security for Self-Evolving IoT Ecosystems},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {7},
        pages = {5521-5527},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=189260},
        abstract = {Traditional perimeter-based security models that rely on the Internet of Things (IoT) have been stretched to their limits by the fast growth of interconnected devices, revealing critical weaknesses in the dynamic and large-scale environments. Because IoT ecosystems are programmed to be more and more independent of each other—changing configurations, sharing data, and coordinating actions in real time—the necessity for security architectures capable of keeping up with such a rapid pace has become paramount. This paper presents a conceptual model of an AI-Native Zero-Trust Operational Security Framework that provides security for self-evolving IoT ecosystems operating beyond the scope of static policies and manual control. This model facilitates the utilization of on-demand and adaptive machine learning techniques, non-stop authentication, and context-sensitive verification to ensure that all hardware, software, and data channels are not trusted until they are verified.
The first distinction between the suggested design and the existing methods is that it focuses on the prevention of attacks through the prediction of risk patterns, learning from the changes in the operations, and modifying the policies on the fly by the computer without the need for the intervention of the human, rather than the detection of attacks after they have happened. The research addresses, along with operational challenges, the issues of unpredictable device behavior in the real world, different standards, and network conditions that may vary. By introducing a security cover of multiple levels starting from the identity of the device, the intelligence at the edge, the behavioral-based trust scoring, and micro-segmentation, the model intends to build a secure environment that is alive and evolves through each interaction.
This publication presents a study on the future of operational security that has been realized and thus provides insights that are beneficial to researchers, practitioners, and industries in transition towards fully adaptive IoT architectures.},
        keywords = {Zero-Trust Architecture, IoT Operational Security, AI-Native Defense, Self-Evolving Ecosystems, Edge Intelligence, Behavioral Trust Scoring, Autonomous Security, Adaptive Threat Detection.},
        month = {December},
        }