Healthcare and FINTECH domains are rapidly digitalizing operations. The domains are taking bigger strides to address the intense demand for data-driven, intelligent, and reliable processes complying with regulations. These industries manage extremely sensitive content, including personal information of individuals and financial details. Conventional compliance methods struggle to keep pace with the dynamic data management regulations due to substantial dependency on human intervention. This paper attempts to develop a framework for integrating artificial intelligence (AI) based data governance into dataflow as an initiative to implement regulatory compliance into an initiative-taking approach rather than addressing incidents after their occurrence. The process involves exploring AI capabilities to flag random events automatically by monitoring compliance as an ongoing process to make transparent data-driven decisions and demonstrate scalability in enforcing policies. Metadata-supported dataflows, lineage, and frameworks are added to support operations to leverage AI capabilities. Integration of intelligent data management frameworks into advanced data analytics technology processes comprehensively enables agile operations, thereby entrusting stakeholders strategically. The approach alters data strategy use from a good to have to a check-box level for enhancing digital integrity and enterprise to an organizational level commitment in this digital era.
Mani Kanta Pothuri (2025) studied this question.
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