Data governance in the age of artificial intelligence: Challenges, best practices and regulatory compliance

Abstract

The growing use of artificial intelligence (AI) across businesses has created serious issues concerning data governance and privacy. As AI technologies rely significantly on massive datasets to learn, adapt and make choices, enterprises must provide strong data governance to protect data integrity, provide security and ensure compliance. Data governance encompasses the policies, procedures and standards that guarantee data are accurate, accessible and properly used throughout their life cycle. Data governance frameworks are becoming important in the context of AI, as AI systems handle and analyse vast amounts of data, frequently containing sensitive or personal information. This research paper examines the importance of data governance in the age of AI, stressing both the benefits and problems it provides. Effective data governance frameworks can assist firms in making better decisions, ensuring regulatory compliance and protecting user privacy. Traditional governance systems face substantial challenges from issues such as data bias, data quality and the complexity of managing AI-driven datasets. To reduce these dangers, best practices in data governance are highlighted, such as data classification, metadata management and the introduction of AI-specific governance standards. As AI technologies advance, the significance of adaptable and transparent data governance frameworks cannot be overemphasised. This study adds to our understanding of how corporations should reconcile the rapid advances in AI with the importance of strong data governance and privacy regulations.