This research explores how watermarking techniques can protect against copyright infringement in Large Language Models while analyzing potential security implications.

• Watermarking significantly reduces LLMs' ability to reproduce copyrighted content
• Watermarked models maintain quality output on non-copyrighted material
• Watermarks provide a verifiable approach to copyright compliance
• Research evaluates susceptibility to membership inference attacks that could expose training data

For security professionals and AI developers, this work offers practical strategies to balance regulatory compliance with model performance, potentially shaping future AI governance frameworks.

Can Watermarking Large Language Models Prevent Copyrighted Text Generation and Hide Training Data?