Google Gemini and ChatGPT, both powerful LLMs, approach data subject requests with varying capabilities depending on their underlying data governance and model architecture. For tasks like identifying personal data mentions within large text corpuses, both can perform reasonably well, flagging specific entities or categories. However, their efficacy in truly fulfilling access requests, rectification requests, or erasure requests is primarily limited by their training data and real-time data access. ChatGPT, being a general-purpose model, typically lacks direct access to user-specific application data, making it unsuitable for autonomously fulfilling direct GDPR/CCPA requests without external system integration. Google Gemini, especially within Google's ecosystem, might offer potential advantages if integrated with Google's broader data management tools, potentially enabling more robust capabilities for internal data management related to data subject rights. Ultimately, neither model can autonomously fulfill a complete data subject request lifecycle; they serve better as analytical tools to assist in processing such requests, like summarizing relevant information or identifying data points for human review, rather than acting as a full compliance engine.