Personal Information Security Protection and Regulation for Generative AI Applications——Integrating Sociological Empirical Research

Abstract

The rapid development and widespread adoption of generative artificial intelligence (AI) technologies have delivered substantial convenience while simultaneously revealing multiple risks: privacy policies often lack readability and salient disclosures; compliance gaps persist across the entire life cycle of personal information processing; there are risks of excessive and unlawful data collection; personal information is vulnerable to misuse; storage practices may result in data leakage; and data deletion may entail security risks. Personal information protection in this domain has therefore become an urgent challenge. Using privacy-policy analysis and sociological surveys as empirical methods, this article conducts an in-depth examination of these issues and proposes corresponding countermeasures, with a view to building a personal information protection system calibrated to the developmental characteristics of generative AI.