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Securing the metaverse: Machine learning–based perspectives on risk, trust, and governance

Publication Type : Journal Article

Publisher : Elsevier BV

Source : International Journal of Information Management Data Insights

Url : https://doi.org/10.1016/j.jjimei.2025.100356

Keywords : Metaverse security, Cybersecurity governance, Decentralized identity, Privacy protection, User behavior, ADO-TCM framework, Threat detection, Regulatory compliance, BERTopic modeling

Campus : Amritapuri

Center : Centre for Cybersecurity

Year : 2025

Abstract : The rapid expansion of the metaverse presents significant cybersecurity and privacy challenges, requiring structured, data-driven analysis. This study applies the ADO-TCM framework and BERTopic modeling to examine drivers of cybersecurity risk, theoretical responses, and interdisciplinary research gaps. Using PRISMA guidelines, 86 peer-reviewed studies were analyzed to identify key antecedents—technological vulnerabilities, user behavior, regulatory fragmentation, economic incentives, and cultural factors—shaping decisions in compliance, deployment, and education. These, in turn, influence outcomes like trust, threat mitigation, and scalability. The review identifies five latent themes: secure identity, privacy, trust, governance, and AI’s role in shaping risk. The study maps diverse theoretical lenses—cognitive, behavioral, strategic, and technological—used to interpret immersive threats and decision-making in metaverse contexts. Contributing a novel, empirically grounded synthesis, this research advances the information management literature and proposes a forward-looking agenda focused on adaptive security, ethical AI, interoperability, regulatory convergence, and intelligent, user-centric architecture for immersive ecosystems.

Cite this Research Publication : Krishnashree Achuthan, Sasangan Ramanathan, Raghu Raman, Securing the metaverse: Machine learning–based perspectives on risk, trust, and governance, International Journal of Information Management Data Insights, Elsevier BV, 2025, https://doi.org/10.1016/j.jjimei.2025.100356

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