Industrial Data-Service-Knowledge Governance: Toward Integrated and Trusted Intelligence for Industry 5.0

2026-01-08

arXiv.org

AI Summary

This work addresses the lack of a unified trust mechanism across data, service, and knowledge layers in industrial intelligence, which undermines reliability, accountability, and regulatory compliance. To bridge this gap, the authors propose Trisk—a novel framework that establishes the first holistic view of cross-layer trustworthy governance in industrial settings. Trisk introduces a five-dimensional trust model integrating quality, safety, privacy, fairness, and explainability, supported by a complementary three-dimensional classification scheme. By synergistically combining enabling technologies—including knowledge graphs, zero-trust architectures, causal reasoning, and federated/edge computing—the framework systematically addresses critical gaps in semantic interoperability and policy enforcement. Grounded in a comprehensive review and maturity assessment of over 120 studies, this research provides a systematic roadmap for advancing both theoretical foundations and practical deployment of trustworthy AI in Industry 5.0.

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Semantics and Knowledge: Provenance, trust, security and privacy, and ethical issues in managing semantic data
• Security and Privacy: Data transparency and provenance
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

Abstract

The convergence of artificial intelligence, cyber-physical systems, and cross-enterprise data ecosystems has propelled industrial intelligence to unprecedented scales. Yet, the absence of a unified trust foundation across data, services, and knowledge layers undermines reliability, accountability, and regulatory compliance in real-world deployments. While existing surveys address isolated aspects, such as data governance, service orchestration, and knowledge representation, none provides a holistic, cross-layer perspective on trustworthiness tailored to industrial settings. To bridge this gap, we present \textsc{Trisk} (TRusted Industrial Data-Service-Knowledge governance), a novel conceptual and taxonomic framework for trustworthy industrial intelligence. Grounded in a five-dimensional trust model (quality, security, privacy, fairness, and explainability), \textsc{Trisk} unifies 120+ representative studies along three orthogonal axes: governance scope (data, service, and knowledge), architectural paradigm (centralized, federated, or edge-embedded), and enabling technology (knowledge graphs, zero-trust policies, causal inference, etc.). We systematically analyze how trust propagates across digital layers, identify critical gaps in semantic interoperability, runtime policy enforcement, and operational/information technologies alignment, and evaluate the maturity of current industrial implementations. Finally, we articulate a forward-looking research agenda for Industry 5.0, advocating for an integrated governance fabric that embeds verifiable trust semantics into every layer of the industrial intelligence stack. This survey serves as both a foundational reference for researchers and a practical roadmap for engineers to deploy trustworthy AI in complex and multi-stakeholder environments.

Problem

• trustworthiness
• industrial intelligence
• data-service-knowledge governance
• Industry 5.0
• cross-layer integration

Innovation

• Trusted AI
• Industrial Data Governance
• Knowledge Graphs
• Zero-Trust Architecture
• Cross-layer Integration