Watermarking, Provenance, and Model Protection in Generative AI: Policy and Practice

Abishek Seethalakshmi Madhanan

Keywords: Generative AI, watermarking, provenance, model protection, trade secrets, AI governance.

TL;DR: Provenance tools enable transparency, but real protection in generative AI comes from defending models against extraction, misuse, and distillation.

Abstract: Watermarking, provenance, and model protection are increasingly important in the governance of generative AI systems, but they serve different purposes. Output watermarking and provenance mechanisms mainly support transparency, traceability, and accountability by helping identify whether content likely originated from a particular AI system. Model protection, in contrast, focuses on safeguarding underlying system assets such as model weights, prompts, orchestration logic, and retrieval pipelines from theft, extraction, or misuse. This paper examines both governance goals through the legal and policy frameworks of the United States and the European Union. It argues that watermarking should be treated as one part of a broader accountability system rather than as a standalone proof of ownership or authenticity. It also shows that model protection depends largely on trade secret law, access-control rules, and contract enforcement. Finally, the paper identifies key gaps in traceability, robustness, and operational governance in current generative AI practice.

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Submission Number: 3