AI-Augmented Data Governance: Enabling Intelligent Access, Lineage, and Compliance across Hybrid Clouds
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V3I6P104Keywords:
AI-Augmented Governance, Data Lineage, Hybrid Cloud, Intelligent Compliance, Data Access Control, Machine Learning, Metadata Management, Cloud Security, Regulatory Audits, Data FabricAbstract
In today's data ecosystem, businesses are increasingly relying on their hybrid cloud environments to store & manage their information assets. However, this flexibility means that data governance has to be more thorough & smart. AI-enhanced data governance changes the way we think about data governance by adding these smart technologies like machine learning, natural language processing (NLP) & advanced metadata analytics. These tools work together to automate & improve basic governance tasks like keeping an eye on where information comes from, controlling who can access it & making sure the rules are followed. They do all of this while keeping in mind how flexible their hybrid infrastructures are. AI is a better approach to look at these types of hazards, find new issues & enforce rules in actual time, rather than only relying on their human supervision or rule-based systems. Machine learning (ML) can discover abnormal access patterns that might indicate a security breach & natural language processing (NLP) can look at policy papers that aren't organized to determine how they fit with the company's internal controls. Metadata analytics makes this ecosystem better by giving these organizations deep insights into data flows. This makes it easy for them to answer questions like "Where did this data come from?" or "Who changed it and when?" Actual world examples from healthcare, finance & retail show that AI-driven governance not only makes it easier to follow the rules and lowers the risk of data misuse, but it also gives data stewards and business users useful information that helps them make better decisions. The outcome is a governance architecture that is proactive, intelligent & scalable, rather than reactive and laborious. This meets the needs of modern corporate data strategy. In the end, AI-enhanced data governance builds a culture of trust, flexibility & accountability in these hybrid clouds. This makes operations safer, speeds up innovation, and helps people make better, more data-driven decisions.
References
[1] Vishnubhatla, S. (2020). Deep Learning Pipelines for Financial Compliance: Scalable Document Intelligence in Regulated Environments. European Journal of Advances in Engineering and Technology, 7(8), 126-131.
[2] Thota, M. R. (2017). From data centers to cloud platforms: A scalable framework for database and big data migration. Journal of Scientific and Engineering Research.
[3] Jain, A. (2018). Pervasive intelligence now: Enabling game-changing outcomes in the age of exponential data. John Wiley & Sons.
[4] GAFFAR, O., SIKIRU, A. O., OTUNBA, M., & ADENUGA, A. A. (2020). Autonomous Data Warehousing for Financial Institutions: Architectures for Continuous Integration, Scalability, and Regulatory Compliance.
[5] Anderson, M. (2018). AI-Augmented Data Quality Monitoring in Real-Time Data Pipelines on AWS. International Journal of Data Engineering and Intelligent Computing, 1(1), 01-14.
[6] Muppaneni, R. K. (2020). Retail Reimagined: How Dynamics 365 Commerce Is Driving Omnichannel Experiences. International Journal of AI, BigData, Computational and Management Studies, 1(1), 49-59. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V1I1P106Nair, A. (2019). Unlocking performance: Optimizing hybrid infrastructure with Oracle Enterprise Linux and Red Hat. International Journal of Scientific Research in Engineering and Technology, 5(4), 65-72.
[7] D’Souza, M. (2016). Mastering QlikView and Qlik Sense: A Comprehensive Guide to Data Visualization and Advanced Analysis.
[8] Oloke, K. (2019). Architecting autonomous financial decision engines through federated learning and hybrid cloud frameworks. Int J Appl Res, 5(6), 500-510.
[9] 10. Seethala, S. R. (2018). A unified hybrid data architecture framework for enterprise-scale data integration, govssss ernance, and analytical workloads across Oracle-based systems and cloud environments. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 3(6), 722-740.
[10] Srigadde, B. R., & Talakola, S. (2020). How to Open a Modal Using Quick Action on the Record Detail Page. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 1(2), 43-51. https://doi.org/10.63282/3050-9262.IJAIDSML-V1I2P105
[11] Garcia, R., & Chow, C. E. (2015, January). Identity considerations for public sector hybrid cloud computing solutions. In 2015 International Conference on Computer Communication and Informatics (ICCCI) (pp. 1-8). IEEE.
[12] Bhaskaran, S. V. (2019). Enterprise data architectures into a unified and secure platform: Strategies for redundancy mitigation and optimized access governance. International Journal of Advanced Cybersecurity Systems, Technologies, and Applications, 3(10), 1-15.
[13] Hurwitz, J. S., Kaufman, M., Halper, F., & Kirsch, D. (2012). Hybrid cloud for dummies. John Wiley & Sons. Journal of Emerging Research in Engineering and Technology, vol. 1, no. 2, June 2020, pp. 69-78
[14] Srigadde, B. R. (2020). When Force Is With You but Not Lightning Component. American International Journal of Computer Science and Technology, 2(1), 23-33. https://doi.org/10.63282/3117-5481/AIJCST-V2I1P103
[15] Trakadas, P., Nomikos, N., Michailidis, E. T., Zahariadis, T., Facca, F. M., Breitgand, D., & Gkonis, P. (2019). Hybrid clouds for data-intensive, 5G-enabled IoT applications: An overview, key issues and relevant architecture. Sensors, 19(16), 3591.
[16] Parepalli, S. (2017). Metadata intelligence for automated data lineage in distributed enterprise systems. system, 5, 5.
[17] Ferreira, J. J., Santos, M., Rodrıguez, A., & Perez, L. (2020). A Hybrid Cloud Framework for Regulatory Compliance in Enterprise Information Systems. The Artificial Intelligence Journal, 1(4).
