The convergence of artificial intelligence, cyber-physical systems, and distributed networking has accelerated the evolution of industrial intelligence across edge, cloud, and cross-organizational communication environments. However, existing governance mechanisms remain fragmented across data management, service orchestration, and knowledge-based decision-making, making it difficult to ensure reliability, accountability, compliance, and explainability throughout the industrial intelligence stack. To address this gap, we present TRISK (TRusted Industrial Data-Service-Knowledge governance), a conceptual and taxonomic framework for trustworthy industrial intelligence. TRISK is grounded in a five-dimensional trust model covering quality, security, privacy, fairness, and explainability, and formalizes how trust is constructed, propagated, aggregated, and fed back across data, service, and knowledge layers in networked industrial systems. Through a structured synthesis of more than 100 representative studies, standards, and technical reports, we examine data governance as the foundation of trust construction, service governance as the mediation layer for trustworthy execution, and knowledge governance as the semantic anchor for reasoning, validation, and feedback adaptation. We further discuss industrial implementation patterns, cross-industry implications, and the role of emerging communication and computing technologies. Finally, we outline a future research roadmap toward adaptive, verifiable, and human-aligned industrial governance for Industry 5.0.

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