One Framework, Many Frontiers: How Data Governance Empowers Every Industry

Abstract

The swift advancement of Artificial Intelligence (AI) has necessitated the generation of novel opportunities and efficiency across several sectors. Simultaneously, the dependence on AI for personal data throughout its training process encompasses several concerns about privacy and data security. This research seeks to examine the current landscape of data privacy methodologies and challenges in AI design, encompassing business strategies and the implementation of legislative frameworks. The essay employs a hybrid methodology, integrating quantitative and qualitative methodologies through a survey of AI experts and a case study approach focused on AI startups. The thesis illustrates a framework of interconnections among technological, legal, and ethical concerns pertaining to data privacy. Key problems include ethical considerations about permission, privacy in data utilization, and
openness in AI decision-making. A study is conducted to assess the adequacy of rules such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) in addressing this AI privacy issue. These rules and principles establish a foundation for data protection; yet, they are insufficient in the realm of AI development.