Enhancing Data Provenance, Integrity, Security, and Trustworthiness in Distributed and Federated Multi-Cloud Computing Environments

Abstract

Due to the increasing trend in distributed cloud environments, strong data provenance and integrity practices are even more important than before to ensure answers to security and traceability requirements as well as compliance.  The new challenges, developments, and best practices in monitoring and security of data for cloud systems are discussed in this paper. Key challenges include scalability limitations, privacy vs. transparency trade-offs, and regulatory compliance issues. To address these concerns, blockchain-based provenance tracking, AI-driven anomaly detection, cryptographic hashing, and privacy-preserving techniques such as homomorphic encryption and secure multiparty computation (SMPC) have emerged as innovative solutions. The study also examines real-world implementations in healthcare, finance, and supply chain management, demonstrating how organizations leverage provenance tracking to enhance trust, security, and operational efficiency. Additionally, the paper discusses standardization efforts such as W3C PROV and ISO 27037, which aim to improve interoperability and legal compliance. Moving forward, advancements in federated learning, decentralized identity management, and quantum-resistant cryptography will play a crucial role in enhancing provenance tracking and ensuring secure cloud ecosystems. By integrating AI-driven monitoring, blockchain scalability solutions, and adaptive compliance frameworks, organizations can build resilient, transparent, and tamper-proof data management systems in an increasingly digital world.