Cloud-based Solutions for AI-Enhanced Data Governance and Assurance

Authors

  • Dillep Kumar Pentyala Sr. Data Reliability Engineer, Farmers Insurance, 6303 Owensmouth Ave, woodland Hills, CA 9136, United States

The recent increase in the use of big data and analytics in various global industries has created pressure on industries to develop reliable and flexible frameworks for data governance. Historically used approaches can be ineffective in addressing the problem of increasing data flow, variety, and compliance expectations. This paper seeks to examine the use of cloud solutions with an aspect of Artificial Intelligence as an innovative development in data management and reporting. Cloud-based platforms offer more appropriate possibilities: scalability, flexibility, and accessibility of solution; AI enriches data governance activities performing automation of processes, detection of anomalies, and predictive analysis.

This paper presents a clear agenda of how AI models should be incorporated to work within the cloud environments to improve data quality, meet regulatory requirements and streamline operations. The use of supervised and unsupervised learning, NLP, and real-time monitoring make the proposed architecture able to enforce policies, detect anomalies and check compliance. In addition, the research also compares these solutions with conventional approaches to identify enhanced economic and precise performance and the ability to address constantly changing regulations.

Challenges such as security, privacy and ethical implication relating to the use of AI are identified and possible solutions are provided. This part of the work describes how it is possible to apply these solutions in healthcare, finance and logistics industries. The results highlighted a need for adopting AI-enhanced cloud-based governance system that can address the needs of today’s data-oriented organizations.