Zero-trust industrial network security using AI and explainable inference novelty

The accelerated digital transformation of industry networks has delivered unprecedented efficiency benefits but, in the process, exposed critical infrastructures to sophisticated cyber threats. Perimeter security paradigms no longer suffice in defending against insider threats, advanced persistent threats, and lateral movement within operational technology networks. Due in large part to these limitations, the concept of Zero-Trust Architecture (ZTA) has emerged as a game-changing method requiring ongoing authentication to all regardless of place or privilege. The subject of this article is the deployment of Artificial Intelligence (AI) with explainable inference to augment Zero-Trust security for industrial networks. Relative to conventional rule-based security, AI models leverage anomaly detection, deep learning, and predictive analytics to identify hidden threat vectors in real-time. AI deployment in critical infrastructure is, however, hindered by transparency regarding "black-box" models and lower operator trust and regulatory acceptability. With the inclusion of explainable AI (XAI) herein, a platform is established whereby autonomous defense system decisions are transparent, interpretable, and verifiable, and hence closing the gap between high automation and human oversight. The paper's contribution lies in two aspects: augmenting Zero-Trust enforcement with adaptive AI-driven attack detection, and in parallel providing explainable inference to facilitate accountability, interpretability, and trust in security decisions. The research makes its contribution in industrial security by providing a secure, scalable, and transparent security architecture that not only secures against future cyber-attacks but also accommodates operational resilience and compliance with regulation in Industry 4.0 settings.