Data Governance for AI: Key Principles, Challenges, and What Regulated Industries Must Implement

Data governance for AI involves a comprehensive set of policies, processes, and technical controls that oversee the collection, preparation, use, and monitoring of data throughout the AI lifecycle. This discipline, often overlooked until an audit or breach occurs, differs from traditional data governance by addressing the unique challenges posed by machine learning models, which absorb training data into their weights, making it difficult to trace, audit, or reverse. The framework extends traditional data governance to include aspects like data lineage and provenance tracking, consent verification, and bias auditing, especially critical in regulated industries such as healthcare, finance, and government. These sectors operate under stringent regulations like HIPAA, SR 11-7, and data classification regimes, which demand rigorous documentation, consent management, and privacy-preserving technologies to ensure compliance. Furthermore, the EU AI Act imposes additional data governance requirements for high-risk AI systems, necessitating frameworks that can satisfy both EU and US regulatory standards. Privacy-enhancing technologies, such as fully homomorphic encryption and multi-party computation, provide robust solutions for training models on sensitive data without exposing it, offering technical guarantees that surpass traditional policy-and-audit methods.

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