AI-Driven Dynamic Data Contracts: Enhancing Model Performance and Governance in Cloud Platforms

Authors

  • Shankar Narayanan SGS Principal Architect, Microsoft, Texas, USA. Author

DOI:

https://doi.org/10.63282/3050-922X.IJERET-V6I1P106

Keywords:

Dynamic Data Contracts, Model Performance, AI-Driven Contracts, Data Governance, Cloud Platforms, Machine Learning, Models, Data Privacy

Abstract

Data-driven decision-making and advanced analytics are transforming how organizations operate, especially with the widespread adoption of cloud computing and artificial intelligence (AI). Yet, static data contracts—defining ingestion rules, schema constraints, and governance policies—cannot keep pace with dynamic data landscapes and evolving regulatory requirements. This paper introduces AI-Driven Dynamic Data Contracts (AIDDC), a holistic approach that uses machine learning (ML) and real-time policy orchestration to continuously interpret governance rules, monitor AI model performance, and adapt data contracts in near real time. By balancing data quality

References

[1] Weber, K., Otto, B., & Österle, H. (2009). One Size Does Not Fit All—A Contingency Approach to Data Governance. Journal of Data and Information Quality, 1(1), 1–27.

[2] Dunleavy, K. (2022). Data Contracts: Formalizing the Relationship Between Data Producers and Consumers. ACM SIGMOD Record, 20–29.

[3] Carpio, F., et al. (2020). Scalability and Elasticity in Cloud-Based AI Workloads. IEEE Cloud Computing, 7(2), 39–48.

[4] Gourdin, E., et al. (2022). ML-Driven Compliance: A Survey on the Use of Machine Learning in Regulatory Compliance. Expert Systems with Applications, 195, 116524.

[5] Mehrabi, N., et al. (2022). A Survey on Bias and Fairness in Machine Learning. ACM Computing Surveys, 54(6), 1–35.

[6] Dimick, J. (2021). The Risks and Rewards of Multi-Cloud Data Environments. Information Systems Research.

[7] Belle, V. (2021). Auditability and Explainability in AI. Data & Knowledge Engineering, 134, 101891.

[8] Arrieta, A. B., et al. (2020). Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges Toward Responsible AI. Information Fusion, 58, 82–115.

[9] TechTimes. (2024). The Impact of Data Governance on AI-Driven Business Decisions. [Online]. Available: https://www.techtimes.com/articles/308636/20241209/impact-datagovernance-ai-driven-business-decisions.htm

[10] EW Solutions. (2025). Alignment of Data Governance, AI, ML, and Emerging Technologies. [Online]. Available: https://www.ewsolutions.com/alignment-of-data-governanceartificial-intelligence-machine-learning-and-emerging-technologies/

[11] Mwangi, B. et al. (2025). AI-Driven Data Governance Framework for Cloud-Based Analytics. SSRN. [Online]. Available: https://ssrn.com/abstract=5052472

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Published

2025-03-01

Issue

Section

Articles

How to Cite

1.
Narayanan SGS S. AI-Driven Dynamic Data Contracts: Enhancing Model Performance and Governance in Cloud Platforms. IJERET [Internet]. 2025 Mar. 1 [cited 2025 Sep. 12];6(1):44-9. Available from: https://ijeret.org/index.php/ijeret/article/view/13