Watermarking Without Standards Is Not AI Governance

TL;DR

This paper critiques current watermarking techniques for AI content, highlighting their limitations in governance and proposing a comprehensive framework to improve accountability and oversight.

Contribution

It identifies gaps between policy expectations and technical capabilities, and proposes a three-layer framework to enhance AI content attribution and governance.

Findings

Current watermarking schemes are often symbolic and lack enforceability.

Incentive structures discourage robust, auditable watermarking implementations.

A three-layer framework can improve AI governance and accountability.

Abstract

Watermarking has emerged as a leading technical proposal for attributing generative AI content and is increasingly cited in global governance frameworks. This position paper argues that current implementations risk serving as symbolic compliance rather than delivering effective oversight. We identify a growing gap between regulatory expectations and the technical limitations of existing watermarking schemes. Through analysis of policy proposals and industry practices, we show how incentive structures disincentivize robust, auditable deployments. To realign watermarking with governance goals, we propose a three-layer framework encompassing technical standards, audit infrastructure, and enforcement mechanisms. Without enforceable requirements and independent verification, watermarking will remain inadequate for accountability and ultimately undermine broader efforts in AI safety and regulation.