In the process of massive data collection and in-depth mining by generative artificial intelligence (AI). A large amount of personal information can be identified, posing numerous challenges in the field of personal information protection. Specifically, these challenges manifest as improper collection and abuse of personal information, increased risk of leakage, and expanded crises in group information security. Existing personal information protection rules struggle to adapt to the technical characteristics of generative AI, facing dilemmas such as the emasculation of the informed consent rule, difficulties in implementing the principle of data minimization, insufficient protection of personal information subjects’ rights, and blocked remedies. To address these issues, this paper proposes path optimization solutions based on a risk-based governance framework: (1) strengthening the full-process application and supervision of Personal Information Protection Impact Assessments (PIPIA), and constructing a scenario-based hierarchical risk governance system; (2) optimizing existing rules in combination with technical realities, improving the hierarchical consent mechanism, and promoting the risk-oriented interpretation of the data minimization principle; giving play to the risk prevention role of procuratorial public interest litigation, and clarifying applicable standards and initiation conditions. The research aims to balance the technological innovation of generative AI with the protection of personal information rights and interests, provide theoretical support for the improvement of relevant rules and promote the healthy and orderly development of generative AI.
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Share and Cite
Qu, Y. (2025) Personal Information Protection Dilemmas and Regulatory Adaptation in Generative Artificial Intelligence Applications. Journal of Social Development and History, 1(4), 88-98.