Trustworthy Data Intelligence: Neuro-Symbolic Verification for Provenance Reasoning, Confidence Propagation, and Trust Scoring Across Enterprise Analytics Ecosystems

Abstract

Enterprise analytics relies on outputs that pass through multi-stage pipelines, yet trust in those outputs is often assumed rather than verified. The main gap is the lack of architectures that combine provenance reasoning, symbolic verification, confidence propagation, and automated trust scoring across enterprise workflows. This study presents a neuro-symbolic verification architecture for trustworthy data intelligence that builds provenance graphs, evaluates consistency rules, propagates confidence through transformation chains, and computes trust scores across analytics stages. The results show stronger provenance verification and confidence stability across reasoning cycles, with trust scores remaining clearly differentiated under different verification conditions. The study shows that trust can be treated as a computable property of enterprise analytics.