Zero Trust Frameworks for Misinformation Detection in Industry 5.0

Abstract This paper examines the application of the Generative Pre-trained Transformer (GPT) model for misinformation detection as part of Zero-Trust security models in Industry 5.0. As the transition to Industry 5.0 gains momentum, characterized by heightened interconnectedness and human-machine interaction, the necessity for rigorous security measures becomes critical. Disinformation poses significant threats, potentially undermining operational efficiency and safety, while causing substantial financial and reputational damage. To address these risks, we propose leveraging GPT, a natural language processing tool, to develop an effective misinformation detection system that enhances data integrity and trust in human-machine interactions within Industry 5.0. This paper discusses integrating a GPT-based misinformation detection system with existing security measures in Industry 5.0, showcasing its potential to strengthen a Zero-Trust security framework. We provide a detailed architectural specification of how the GPT-3 classifier integrates as a Policy Decision Point (PDP) within a Zero Trust Network Access (ZTNA) framework. Through case studies and simulations, we demonstrate the value of this machine learning model in protecting Industry 5.0 environments from misinformation-related threats. We also present inference latency analysis across GPT-3 variants to evaluate suitability for real-time industrial deployment. The research emphasizes the need for continuous vigilance and adaptable security strategies, highlighting the crucial role of GPT-based misinformation detection in maintaining a secure and reliable environment for human-machine interactions within the rapidly evolving context of Industry 5.0.