Challenges and conceptualizing ai-powered privacy risk assessments: Legal models for U.S. data protection compliance

Abstract

The rapid evolution of artificial intelligence (AI) has transformed privacy risk assessments, offering innovative tools to address complex compliance challenges in the United States. However, the integration of AI into privacy risk management raises significant issues, including algorithmic transparency, bias, and adaptability to dynamic regulatory landscapes such as those shaped by the California Consumer Privacy Act (CCPA) and the Gramm-Leach-Bliley Act (GLBA). This paper explores these challenges and proposes a conceptual framework for AI-powered dynamic data protection models. The proposed framework emphasizes real-time risk monitoring, scalability across industries, and mechanisms for ensuring algorithmic accountability. It also examines legal models that align with the framework, integrating existing U.S. data protection laws and harmonizing with international standards such as the General Data Protection Regulation (GDPR). The paper concludes with actionable recommendations for regulators, organizations, and AI developers to foster ethical and adaptive approaches to data protection, ensuring compliance and trust in a rapidly evolving regulatory environment.