Data Governance and Security for AI Environments

This guide discusses the challenges and best practices for data governance and security in the context of artificial intelligence (AI). It outlines the increasing complexities faced by data managers as AI technologies evolve, emphasizing the necessity for effective data governance to support AI initiatives. The document highlights findings from a Drexel University survey, revealing that a significant percentage of organizations struggle with data governance, which is crucial for ensuring data quality and compliance. Key elements of a robust data governance framework are presented, including the importance of data lineage, access controls, and the management of personally identifiable information. The guide also addresses the implications of third-party data management and the need for safeguards against unauthorized use of AI models. Furthermore, it emphasizes the role of human oversight in data governance processes, ensuring that AI systems operate within legal and financial constraints. Overall, the document serves as a resource for organizations aiming to modernize their data governance and security practices in the age of AI.