Stanford Database Library of American Journal of Applied Science and Technology

Vol. 5 No. 11 (2025)
Articles

Evolving Frameworks for Digital Trust: A Multidimensional Analysis of Data Governance in Cloud, IoT, and Multimodal AI Ecosystems

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  • Helvina L. Orvest
Helvina L. Orvest
School of Governance & Emerging Technologies, University of Amsterdam, Netherlands

Published 2025-11-26

Keywords

  • Data Governance,
  • Cloud Computing,
  • Artificial Intelligence,
  • Digital Trust

How to Cite

Helvina L. Orvest. (2025). Evolving Frameworks for Digital Trust: A Multidimensional Analysis of Data Governance in Cloud, IoT, and Multimodal AI Ecosystems. Stanford Database Library of American Journal of Applied Science and Technology, 5(11), 129–135. Retrieved from https://oscarpubhouse.com/index.php/sdlajast/article/view/17

Copyright (c) 2025 Helvina L. Orvest

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Purpose: As organizations transition toward cloud-native architectures and artificial intelligence (AI) integration, traditional data governance (DG) frameworks—originally designing for on-premise, structured data—are proving insufficient. This study aims to analyze the evolving requirements of DG in converged environments, specifically examining the intersection of Cloud Computing, Internet of Things (IoT), and Multimodal AI systems. The research seeks to establish a taxonomy of enabling factors that facilitate secure AI adoption and digital trust.

Methodology: A systematic literature review and qualitative document analysis were conducted on 33 peer-reviewed sources ranging from 2014 to 2025. The analysis utilized a risk-based modeling approach to categorize governance dimensions, contrasting cloud versus non-cloud governance taxonomies and evaluating frameworks for algorithmic auditing and multimodal data fusion.

 Findings: The review identifies that successful DG in modern ecosystems requires a shift from static compliance checklists to dynamic, "agile" governance models. Key findings indicate that cloud data governance is distinctively characterized by shared responsibility models that complicate digital forensics and custody. Furthermore, the integration of AI necessitates new governance layers for "grey data" and multimodal inputs, particularly in high-stakes sectors like healthcare and banking. The study confirms that organizational culture and executive sponsorship are as critical as technical controls in enabling secure digital transformation.

Originality/value: This paper proposes a unified conceptual framework that bridges the gap between technical data management and strategic corporate governance. It uniquely addresses the governance of "multimodal" data streams and provides a roadmap for internal auditors to engage with AI systems despite the current lack of standardized guidance.

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References

  1. Aldoseri, A., Al-Khalifa, K. N., & Hamouda, A. M. (2024). Methodological approach to assessing the current state of organizations for AI-Based digital transformation. Applied System Innovation, 7(1), 14.
  2. Alhassan, I., Sammon, D., & Daly, M. (2016). Data governance activities: an analysis of the literature. Journal of Decision Systems, 25(sup1), 64–75.
  3. Alhassan, I., Sammon, D., & Daly, M. (2019). Critical success factors for data governance: A theory Building approach. Information Systems Management, 36(2), 98–110.
  4. Al-Ruithe, M., & Benkhelifa, E. (2018). Determining the enabling factors for implementing cloud data governance in the Saudi public sector by structural equation modelling. Future Generation Computer Systems, (article in press), 1-16.
  5. Al-Ruithe, M., Benkhelifa, E., & Hameed, K. (2016a). A Conceptual Framework for Designing Data Governance for Cloud Computing. Procedia Computer Science, 94, 160-167.
  6. Al-Ruithe, M., Benkhelifa, E., & Hameed, K. (2016b). Key Dimensions for Cloud Data Governance. 2016 IEEE 4th International Conference on Future Internet of Things and Cloud (FiCloud), 379-386.
  7. Al-Ruithe, M., Benkhelifa, E., & Hameed, K. (2018a). A systematic literature review of data governance and cloud data governance. Personal and Ubiquitous Computing, 1-21.
  8. Al-Ruithe, M., Benkhelifa, E., & Hameed, K. (2018b). Data Governance Taxonomy: Cloud versus NonCloud. Sustainability, 10(1), 1-26.
  9. Al-Ruithe, M., Mthunzi, S., & Benkhelifa, E. (2016c). Data Governance for Security in IoT & Cloud Converged Environments. 2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA), 1-8.
  10. Alvarez-Romero, C., Martínez-García, A., Bernabeu-Wittel, M., & Parra-Calderón, C. L. (2023). Health data hubs: An analysis of existing data governance features for research. Health Research Policy and Systems, 21, 70.
  11. An, Z., Zhang, D., & Liang, Y. (2021). Research on Data Governance Framework for Fire Department. ACM International Conference Proceeding Series.
  12. Aunimo, L., Alamäki, A. V., & Ketamo, H. (2019). Big data governance in agile and data-Driven software development: A market entry case in the educational game industry. IGI Global.
  13. Austin, L. M., & Lie, D. (2021). Data trusts and the governance of smart environments: lessons from the failure of sidewalk labs’ urban data trust. Surveillance & Society, 19(2), 255–261.
  14. Ayappane, B., Vaidyanathan, R., Srinivasa, S., Upadhyaya, S. K., & Vivek, S. (2024). Consent Service Architecture for Policy-Based Consent Management in Data Trusts. ACM International Conference Proceeding Series, 155–163.
  15. Azeroual, O., Nikiforova, A., & Sha, K. (2023). Overlooked Aspects of Data Governance: Workflow Framework for Enterprise Data Deduplication. International Conference on Intelligent Computing, Communication, Networking and Services (ICCNS), 65–73.
  16. Baltrušaitis, T., Ahuja, C., & Morency, L. P. (2019). Multimodal Machine Learning: A Survey and Taxonomy. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41, 423–443.
  17. Barker, J. M. (2016). Data Governance: The missing approach to improving data quality. ProQuest LLC.
  18. Bayoumy, K., Gaber, M., Elshafeey, A., Mhaimeed, O., Dineen, E. H., Marvel, F. A., Martin, S. S., Muse, E. D., Turakhia, M. P., Tarakji, K. G., et al. (2021). Smart wearable devices in cardiovascular care: Where we are and how to move forward. Nature Reviews Cardiology, 18, 581–599.
  19. Bindley, P. (2019). Joining the dots: How to approach compliance and data governance. Network Security, 2019(2), 14–16.
  20. Bone, J. (2020). Auditing artificial intelligence: Internal auditors can develop a framework for conducting AI engagements, despite a lack of standards and guidance. Internal Auditor, 77(5), 20–21.
  21. Bordey, G. (2018). Agile in data governance design. Business Intelligence Journal, 23(2), 23–32.
  22. Borek, A., Parlikad, A. K., Woodall, P., & Tomasella, M. (2014). A risk based model for quantifying the impact of information quality. Computers in Industry, 65(2), 354–366.
  23. Borgman, C. L. (2018). Open data, grey data, and stewardship: Universities at the privacy frontier. Berkeley Technology Law Journal, 33(2), 365–412.
  24. Bowen, G. A. (2009). Document analysis as a qualitative research method. Qualitative Research Journal.
  25. Brown, P. A., & Anderson, R. A. (2023). A methodology for preprocessing structured big data in the behavioral sciences. Behavior Research Methods, 55, 1818–1838.
  26. Burniston, T. R. (2015). Data governance. ABA Banking Journal, 107(4), 56–57.
  27. Calabrese, J., Esponda, S., & Pesado, P. M. (2020). Framework for data quality evaluation based on ISO/IEC 25012 and ISO/IEC 25024. VIII conference on cloud computing, big data & emerging topics.
  28. Caldwell, P. H., & Bennett, T. (2020). Easy guide to conducting a systematic review. Journal of Paediatrics and Child Health, 56(6).
  29. Casey, E. (2019). The chequered past and risky future of digital forensics. Australian Journal of Forensic Sciences, 51(6), 649–664.
  30. Casino, F., Dasaklis, T., Spathoulas, G., Anagnostopoulos, M., Ghosal, A., Borocz, I., Solanas, A., Conti, M., & Patsakis, C. (2022). Research trends, challenges, and emerging topics in digital forensics: A review of reviews. IEEE Access, 10, 1−1.1
  31. Elshawi, R., Sakr, S., Talia, D., & Trunfio, P. (2018). Big Data Systems Meet Machine Learning Challenges: Towards Big Data Science as a Service. Big Data Research, 14, 1–11.2
  32. Lahat, D., Adali, T., & Jutten, C. (23015). Multimodal Data Fusion: An Overview of Methods, Challenges, and Prospects. Proceedings of the IEEE, 103, 1449–1477.
  33. Rajgopal, P. R., & Yadav, S. D. (2025). The role of data governance in enabling secure AI adoption. International Journal of Sustainability and Innovation in Engineering, 3, 1–25.