29, November 2025

ZTrustChain: A Decentralized Zero Trust Framework for Adaptive AI Authentication Using Blockchain Synergy

VOLUME-11; ISSUE-11; NOV-2025

Author(s): M.MOHAN , S.SUDHARSAN , C.VINOTH , S.CHANDRAKALA

Authors Affiliations:

1 Master of candidate ,Department of Cyber Security Engineering, Paavai Engineering College, Namakkal, India

2 Master of candidate ,Department of Cyber Security Engineering, Paavai Engineering College, Namakkal, India

3 Master of candidate ,Department of Cyber Security Engineering, Paavai Engineering College, Namakkal, India

4 Lecturer, Department of Cyber Security Engineering, Paavai Engineering College, Namakkal, India

DOIs:10.2015/IJIRMF/202511014     |     Paper ID: IJIRMF202511014


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: In the digital age of hyper-connectivity and autonomous systems, traditional perimeter-based security architectures have become obsolete. The growing sophistication of cyber threats, insider risks, and AI-driven attacks necessitates a paradigm shift from implicit trust to continuous verification. The Zero Trust (ZT) model—centered on the principles of “never trust, always verify” and “assume breach”—has emerged as a cornerstone of modern cybersecurity. However, most current Zero Trust implementations rely on centralized control mechanisms, which introduce single points of failure, scalability limitations, and vulnerability to insider threats. To overcome these inherent weaknesses, this paper introduces ZTrustChain, a Decentralized Zero Trust Framework that integrates blockchain technology and adaptive artificial intelligence (AI) into a unified, self-governing authentication ecosystem.

Zero Trust, Blockchain, Decentralized Identity, Adaptive Authentication, Artificial Intelligence, Federated Learning, Cybersecurity, Smart Contracts, Decentralized Trust.

M.MOHAN , S.SUDHARSAN , C.VINOTH , S.CHANDRAKALA (2025); ZTrustChain: A Decentralized Zero Trust Framework for Adaptive AI Authentication Using Blockchain Synergy. International Journal for Innovative Research in Multidisciplinary Field, ISSN(O): 2455-0620, Vol-11, Issue-11, Pp. 79-86.         Available on –   https://www.ijirmf.com/

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