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

Grace Annie Chintoh 1, *, Osinachi Deborah Segun-Falade 2, Chinekwu Somtochukwu Odionu 3 and Amazing Hope Ekeh 4

1 Gulfstream Aerospace Corporation.
2 TD Bank, Toronto Canada.
3 Independent Researcher, Texas, USA.
4 Boston University, MA, USA.
 
Review
International Journal of Frontline Research in Multidisciplinary Studies, 2024, 05(01), 001-009.
Article DOI: 10.56355/ijfrms.2025.5.1.0036
Publication history: 
Received on 14 October 2024; revised on 05 December 2024; accepted on 08 December 2024
 
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.
 
Keywords: 
Artificial Intelligence; Privacy Risk Assessments; Data Protection Compliance; Dynamic Data Protection Models; Algorithmic Transparency; Regulatory Adaptation
 
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Copyright information: 
Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0