EU Code of Practice on Marking and Labelling of AI-Generated Content
EU-AIGC-CoP · European Commission
The European Commission published a first draft of a voluntary code of practice establishing common standards for marking and labelling AI-generated content across the EU. It targets organisations that produce, distribute, or deploy AI systems capable of generating text, images, audio, or video. The code aims to improve transparency and consumer awareness by requiring identifiable disclosure when content is AI-generated.
Overview
The EU Code of Practice on Marking and Labelling of AI-Generated Content is a voluntary instrument developed under the broader EU AI Act ecosystem, intended to operationalise transparency obligations related to synthetic and AI-generated material. The first draft, published in early 2026, sets out common technical and procedural standards for how AI-generated content should be marked, labelled, or otherwise disclosed to end users and the public. The code applies to a wide range of actors including AI developers, platform operators, media distributors, and enterprise deployers who use generative AI tools in commercial or public-facing contexts. Although voluntary in formal status, adherence to the code is expected to function as a compliance signal under the AI Act's transparency requirements, particularly for providers of general-purpose AI models and systems used in high-volume content generation. The European Commission is overseeing a multi-stakeholder drafting process, with the final version expected to be adopted following a consultation period. Non-participating organisations may face greater regulatory scrutiny when transparency-related provisions of the AI Act are enforced by national market surveillance authorities.
Key Requirements
- •Disclose to end users, via visible and accessible labelling, that content has been generated or substantially modified by an AI system
- •Apply machine-readable metadata or watermarking standards to AI-generated text, images, audio, and video where technically feasible
- •Maintain records of labelling practices to demonstrate compliance upon request by regulators or auditors
- •Implement internal policies and staff training to ensure consistent application of marking standards across content production workflows
- •Align labelling practices with any technical specifications issued by the European Commission or relevant standardisation bodies under the AI Act framework
- •Review and update marking practices when new versions of the code or associated technical standards are published