Robust Data Governance Framework for Artificial Intelligence Applications

Abstract

This article comprehensively analyzes AI governance frameworks and their implementation across various sectors, particularly on emerging generative AI technologies where adoption has grown from 28% to 40% in the past year. The article examines the evolution from traditional data governance to AI-specific frameworks, highlighting the challenges and solutions in managing modern AI systems, including the unique complexities of governing generative AI models. Through extensive analysis of global implementations across 245 organizations in 57 countries, the article demonstrates that structured governance approaches achieve 73% higher success rates in AI deployments and a 32.4% increase in return on AI investments. The framework proposed addresses critical aspects, including data quality management (improving accuracy by 71%), security controls (reducing breaches by 87%), ethical AI practices (enhancing bias detection by 89%), and continuous monitoring mechanisms (achieving 96% early issue detection). Implementation results across different sectors show that organizations achieve an average 84.6% increase in stakeholder trust, 77% reduction in compliance-related issues, and 82% improvement in model reliability. The article provides valuable insights for organizations seeking to establish robust AI governance structures while adapting to emerging technological challenges, particularly in managing generative AI applications, where 65% of implementations require substantial modifications to existing governance frameworks.