Toward Data Integrity Architecture for Cloud-Based AI Systems

  • Authors

    • Dr. Adeyemi Adebayo Department of Computer Science, Federal University of Technology, Akure, Nigeria. Author

    Published 2026-01-03

  • Data Integrity, Cloud Computing, Artificial Intelligence, Data Provenance, Cryptographic Verification, Cloud Security, AI Data Pipelines, Trustworthy AI, Distributed Systems, Access Control

    Issue

    Vol. 1 No. 1 (2026)

    Section

    Articles

    How to Cite

    [1]
    A. Adebayo, “Toward Data Integrity Architecture for Cloud-Based AI Systems”, IJDEIC, vol. 1, no. 1, pp. 01–10, Jan. 2026, Accessed: Mar. 02, 2026. [Online]. Available: https://worldcometresearchgroup.com/index.php/ijdeic/article/view/42

  • Abstract

    In cloud-based AI systems, maintaining data integrity is crucial for ensuring trustworthy model outcomes and preventing erroneous decision-making. However, the dynamic, distributed, and multi-tenant nature of cloud environments presents significant challenges to guaranteeing data authenticity, completeness, and consistency throughout AI data pipelines. This paper proposes a comprehensive data integrity architecture tailored for cloud-based AI systems, leveraging cryptographic techniques, provenance tracking, and access control mechanisms. The architecture is designed to seamlessly integrate with AI workflows, ensuring real-time verification of data integrity without compromising scalability or performance. Through a case study and experimental evaluation, we demonstrate the effectiveness of the proposed approach in enhancing trustworthiness and robustness of AI services deployed in the cloud. This work lays the foundation for future research on securing data integrity in evolving AI-cloud ecosystems.

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