Legal Ethics of Generative Artificial Intelligence Training Data Usage in Large Language Models: Copyright and Personal Data Perspective

Abstract

This research originates from security and privacy risks associated with Generative AI Large Language Models (LLMs), especially concerning data used for training in relation to Intellectual Property Rights (IPR) and Personal Data Protection (PDP). The study seeks to examine existing regulatory guidance and develop a national framework that protects user privacy and IPR. The method employs normative legal research using statutory and conceptual approaches, by reviewing the legal frameworks of Indonesia, South Korea, the European Union (EU), and Japan, along with direct testing of LLM prompts. The findings show that LLMs continuously absorb users’ input prompts, data, and corrections to support deeper model training, thereby revealing significant security vulnerabilities under PDP laws. Moreover, in the absence of strong human creative control and predictability, each incoming data item may potentially breach privacy and copyright laws and may also damage original creators. Therefore, legal governance should move toward explicit licensing, fair compensation, and technical verification standards such as provenance labeling. The study concludes that mandatory human-centric supervision, explicit user consent, and freedom of choice are essential legal limits. Ultimately, effective AI governance depends on a shared responsibility model: software engineers must implement technical transparency, expert oversight, and responsive complaint mechanisms, while end users must select platforms lawfully, comply strictly with output licensing requirements, and actively monitor the system.