Data Governance in Generative AI: A Framework for Transparency, Compliance, and Ethical Practice

Abstract

Data governance in generative artificial intelligence presents unique challenges that distinguish these systems from traditional machine learning applications. As generative AI continues to proliferate across industries, establishing robust governance mechanisms becomes essential for sustainable innovation. This document addresses critical yet often overlooked implications of current generative AI development practices, particularly regarding data provenance, licensing structures, and regulatory alignment. The proposed Training Data Declarations framework introduces a standardized approach for documenting and verifying the legal and ethical status of training datasets. Through a tiered classification system categorizing data sources according to legal acceptability, coupled with a comprehensive metadata schema and systematic audit procedures, the framework enables enhanced transparency without compromising competitive advantages. Implementation experience demonstrates significant improvements in compliance metrics, reduction in legal exposure, and accelerated regulatory processes. The multidimensional risk taxonomy further supports targeted governance strategies tailored to specific content sensitivity levels, jurisdictional requirements, and application contexts. By balancing innovation needs with appropriate safeguards, this governance approach fosters a generative AI landscape characterized by transparency, respect for intellectual property rights, and ethical data stewardship.