Integrating Artificial Intelligence into Enterprise Data Governance Frameworks: A Comprehensive Approach for Automated Compliance and Risk Management
DOI:
https://doi.org/10.32628/IJSRSET25121185Keywords:
Data Governance, Artificial Intelligence, Enterprise Architecture, Automated Compliance, Risk Management, Data QualityAbstract
Enterprise data governance has evolved from manual, policy-driven approaches to sophisticated frameworks requiring real-time decision-making and automated compliance monitoring. Traditional governance models struggle with data volume, velocity, and variety challenges in modern enterprise environments. This research presents an integrated artificial intelligence framework that enhances data governance through automated policy enforcement, intelligent data classification, and predictive risk assessment. Our proposed framework leverages natural language processing for policy interpretation, machine learning algorithms for anomaly detection, and automated data lineage tracking to improve governance efficiency by 67% while reducing compliance violations by 43%. The framework was validated through implementation in a financial services organization managing 2.8 petabytes of structured and unstructured data across 47 business units. Results demonstrate significant improvements in data quality metrics, regulatory compliance adherence, and operational efficiency. The research contributes a practical methodology for organizations seeking to modernize their data governance capabilities while maintaining regulatory compliance and operational transparency.
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Weber, K., Jobst, M., & Hoffmann, A. (2019). "Data governance in the age of digitalization: Challenges and opportunities." International Journal of Information Management, 47, 89-103. DOI: 10.1016/j.ijinfomgt.2019.01.013
Tallon, P. P., Ramirez, R. V., & Short, J. E. (2023). "The information artifact in IT governance: Toward a theory of information governance." Journal of Management Information Systems, 40(2), 435-464. DOI: 10.1080/07421222.2023.2176017
Chen, L., Zhang, M., & Liu, Y. (2022). "Artificial intelligence in enterprise data management: A systematic literature review." Computers & Industrial Engineering, 168, 108089. DOI: 10.1016/j.cie.2022.108089
Khatri, V., & Brown, C. V. (2010). "Designing data governance." Communications of the ACM, 53(1), 148-152. DOI: 10.1145/1629175.1629210
Cheong, L. K., & Chang, V. (2007). "The need for data governance: A case study." Proceedings of the 18th Australasian Conference on Information Systems, 999-1008.
Henderson, D., Earley, S., & Sebastian-Coleman, L. (2017). DAMA-DMBOK: Data Management Body of Knowledge (2nd ed.). Technics Publications.
Weber, S., Martinez, R., & Thompson, K. (2019). "Machine learning for automated healthcare data classification: A privacy-preserving approach." IEEE Transactions on Biomedical Engineering, 66(8), 2314- 2323. DOI: 10.1109/TBME.2019.2892147
Chen, J., & Liu, X. (2020). "Natural language processing for regulatory compliance automation in financial services." Journal of Financial Technology, 15(3), 245-262. DOI: 10.1080/jft.2020.1756234
Krishnan, S., Patel, N., & Kumar, R. (2021). "Ensemble learning approaches for automated data quality assessment in enterprise environments." Data & Knowledge Engineering, 134, 101891. DOI: 10.1016/j.datak.2021.101891
Martinez, A., & Thompson, B. (2022). "GDPR compliance challenges in large-scale enterprise data management." International Journal of Data Protection, 8(2), 156-174. DOI: 10.1007/s41347-022-00298-7
Patel, V., Singh, A., & Johnson, M. (2023). "Automated compliance monitoring in financial services: A machine learning approach." Journal of Financial Regulation and Compliance, 31(1), 78-95. DOI: 10.1108/JFRC-08-2022-0089
Kumar, P., & Singh, R. (2021). "Graph-based machine learning for automated data lineage discovery." ACM Transactions on Database Systems, 46(3), 1-34. DOI: 10.1145/3453932
Anderson, C., White, D., & Brown, E. (2023). "Explainable AI in enterprise governance: Balancing transparency and performance." IEEE Transactions on Knowledge and Data Engineering, 35(4), 3456-3467. DOI: 10.1109/TKDE.2023.3245671
Liu, H., Zhang, Y., & Wang, S. (2022). "Federated learning for privacy-preserving data governance in multi-organizational environments." Nature Machine Intelligence, 4(8), 678-689. DOI: 10.1038/s42256- 022-00512-8
Davis, R., Johnson, K., & Miller, P. (2024). "Quantum algorithms for optimization in enterprise data governance." Quantum Information Processing, 23(2), 45-62. DOI: 10.1007/s11128-024-04234-9
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