AI for Data Governance Analysts: A Practical Framework for Transforming Manual Controls into Automated Governance Pipelines
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V5I1P117Keywords:
Al-Assisted Governance, Rule-Based Automation, Analyst Augmentation, Decision Traceability, Predictive Compliance, Policy-Aware Apis, Metadata Enrichment, Governance PipelinesAbstract
This paper proposes a practical, analyst-centric framework for transforming fragmented manual data governance controls into Automated Governance Pipelines. Rather than replacing humans, the approach focuses on AI-assisted governance and analyst augmentation, where machine intelligence continuously monitors data assets, enforces policies, and surfaces prioritized risks while humans retain oversight of design, exceptions, and approvals. The framework integrates a Rule-Based Automation with learning-based elements to adopt predictive compliance, where past incidences, metadata signals and policy context are used to identify the likely occurrence of violation prior to its actualization. One of the key design features is good decision traceability, which makes sure that all control executions are connected with the policy, rule, and model signal that led to their creation to align with audit and regulatory requirements. The architecture focuses on Policy-Aware API Integration to ensure that the governance logic is integrated directly into data platforms, ETL tools and analytical processes with the support of rich metadata enrichment and classification, lineage, and impact analysis cataloging. The application of least-privilege access, masking, and immutable logging is applied to the End-to-End Governance Lifecycle, addressing the requirements of security and privacy, in the policy authoring to the continuous improvement stages. The guidelines on implementation and quantitative outcomes indicate better accuracy, less false positives, reduced workload on the analysts and significant cost savings as compared to entirely manual controls. The framework can therefore provide data governance analysts with a roadmap to transition out of the spreadsheet-based governance practices to scalable, explainable, and resilient AI-powered governance operations
References
1. Kumar, A., Boehm, M., & Yang, J. (2017, May). Data management in machine learning: Challenges, techniques, and systems. In Proceedings of the 2017 ACM International Conference on Management of Data (pp. 1717-1722).
2. Laure, B. E., Angela, B., & Tova, M. (2018, April). Machine learning to data management: A round trip. In 2018 IEEE 34th International Conference on Data Engineering (ICDE) (pp. 1735-1738). IEEE.
3. Terry, N. P. (2017). Regulatory disruption and arbitrage in health-care data protection. Yale J. Health Pol'y L. & Ethics, 17, 143.
4. Sarker, I. H. (2021). Machine learning: Algorithms, real-world applications and research directions. SN computer science, 2(3), 160.
5. Data Go Data Governance: F ernance: Frameworks and Appr ameworks and Approaches in the Curr oaches in the Current Marketplace, Iowa State University Digital Repository. 2021. Online. https://dr.lib.iastate.edu/server/api/core/bitstreams/0c7959d6-9298-4076-ac3d-b2c15d2a48c8/content
6. De Haes, S., & Van Grembergen, W. (2012). Analysing the impact of enterprise governance of IT practices on business performance. In Business Strategy and Applications in Enterprise IT Governance (pp. 14-36). IGI Global Scientific Publishing.
7. Khatri, V., & Brown, C. V. (2010). Designing data governance. Communications of the ACM, 53(1), 148-152.
8. Yamany, H. F. E., Capretz, M. A., & Allison, D. S. (2010). Intelligent security and access control framework for service-oriented architecture. Information and Software Technology, 52(2), 220-236.
9. Gaaloul, K., El Kharbili, M., & Proper, H. A. (2013, November). Secure governance in enterprise architecture—Access control perspective. In 2013 3rd International Symposium ISKO-Maghreb (pp. 1-6). IEEE.
10. Polyzotis, N., Roy, S., Whang, S. E., & Zinkevich, M. (2017, May). Data management challenges in production machine learning. In Proceedings of the 2017 ACM international conference on management of data (pp. 1723-1726).
11. Governing data, The World Bank, 2021. online. https://wdr2021.worldbank.org/stories/governing-data/
12. Benantar, M. (2006). Access control systems: security, identity management and trust models. Boston, MA: Springer US.
13. Viswanathan, Venkatraman. "AI-Augmented Decision Intelligence for Enterprise Systems: Integrating Cognitive Analytics for Resource and Talent Optimization." (2023).
14. Laszewski, T., Arora, K., Farr, E., & Zonooz, P. (2018). Cloud Native Architectures: Design high-availability and cost-effective applications for the cloud. Packt Publishing Ltd.
15. Michael, J. B., Ong, V. L., & Rowe, N. C. (2001, July). Natural-language processing support for developing policy-governed software systems. In Proceedings 39th International Conference and Exhibition on Technology of Object-Oriented Languages and Systems. TOOLS 39 (pp. 263-274). IEEE.
16. Dogo, E. M., Nwulu, N. I., Twala, B., & Aigbavboa, C. (2019). A survey of machine learning methods applied to anomaly detection on drinking-water quality data. Urban Water Journal, 16(3), 235-248.
17. Ko, T., Lee, J. H., Cho, H., Cho, S., Lee, W., & Lee, M. (2017). Machine learning-based anomaly detection via integration of manufacturing, inspection and after-sales service data. Industrial Management & Data Systems, 117(5), 927-945.
18. Arul, U., & Prakash, S. (2020). Toward automatic web service composition based on multilevel workflow orchestration and semantic web service discovery. International Journal of Business Information Systems, 34(1), 128-156.
19. Hanafy, M., Said, H., & Wahba, A. M. (2015, May). Complete properties extraction from simulation traces for assertions auto-generation. In 2015 IEEE 24th North Atlantic Test Workshop (pp. 1-6). IEEE.
20. Raschka, S., Patterson, J., & Nolet, C. (2020). Machine learning in python: Main developments and technology trends in data science, machine learning, and artificial intelligence. Information, 11(4), 193.
21. Salama, D. M., & El-Gohary, N. M. (2016). Semantic text classification for supporting automated compliance checking in construction. Journal of Computing in Civil Engineering, 30(1), 04014106.
