Data Trust Fabric: A Blockchain-Inspired Framework for Secure Multi-Party Data Ecosystems

In today's data-driven business landscape, organizations are increasingly engaging in cross-organizational data sharing and collaboration to unlock new insights and drive innovation. However, this growing complexity of multi-party data ecosystems has also introduced significant challenges around data trustworthiness, provenance, and regulatory compliance.This paper presents a comprehensive analysis of the Data Trust Fabric (DTF) - a blockchain-inspired framework designed to address these challenges and enable organizations to establish secure, transparent, and compliant data environments. The DTF combines the core principles of blockchain technology, such as decentralized record-keeping and cryptographic security, with modern data governance best practices to provide a robust solution for maintaining data integrity and trust in multi-party settings. Through an in-depth exploration of the DTF's architectural components, the authors examine how this framework empowers organizations to implement tamper-evident audit trails, automated policy enforcement, and decentralized identity management. Use cases across industries, such as financial services, healthcare, and supply chain management, illustrate how the DTF can be leveraged to address real-world data governance challenges, from transaction verification and regulatory compliance to patient data protection and product traceability. Furthermore, the paper delves into the technical implementation considerations, security controls, and future research directions that will shape the continued evolution of the Data Trust Fabric. As organizations navigate the complexities of the digital age, this blockchain-inspired framework emerges as a critical solution for establishing trusted, resilient, and compliant data ecosystems.