Cross-Jurisdictional Data Privacy Compliance in the U.S.: Developing a New Model for Managing AI Data across State and Federal Laws

The fragmented landscape of data privacy laws in the United States poses significant challenges for organizations utilizing artificial intelligence (AI) systems that process sensitive and large-scale data. Variations in state laws and the absence of a comprehensive federal framework exacerbate compliance complexities, limiting AI innovation and creating legal uncertainties. This paper proposes a conceptual model to harmonize privacy compliance across U.S. jurisdictions, integrating key interoperability principles, consistency, transparency, and scalability. The framework emphasizes standardized practices for data classification, consent management, risk assessment, and enforcement mechanisms supported by technological enablers such as privacy-enhancing technologies and AI compliance tools. Through case studies in healthcare, e-commerce, and finance, the paper demonstrates the framework’s practical application and effectiveness in resolving multi-jurisdictional compliance challenges. Actionable recommendations for policymakers, organizations, and AI developers are provided to facilitate implementation alongside future research directions to refine the model and address emerging privacy risks. This study offers a roadmap for navigating the complexities of U.S. privacy laws, promoting trust, accountability, and responsible AI innovation.