AI Watermarking: Key Updates on New Transparency Rules
As generative artificial intelligence (AI) continues to advance, it’s becoming increasingly difficult to distinguish human-created content from AI-generated material. In turn, regulators are requiring certain AI systems to disclose where and how content is created.
Two important examples are the European Union’s AI Act and California’s AI Transparency Act. While both of these laws address AI-generated content, neither requires businesses to use the same type of disclosure in every situation. Instead, the rules distinguish between different forms of transparency, including machine-readable markings that may not be visible to the human eye. These requirements may apply to both the provider of the AI system and the deployer, or the entity that makes the AI available to consumers.
The laws are falling into place, but how do they work in practice? For businesses developing or using generative AI, the challenge is not only whether AI-generated content should be watermarked, but how different types of media should carry that watermark.
The EU AI Act
Transparency requirements under Article 50 of the EU AI Act became generally applicable on August 2, 2026, with a grace to December 2, 2026, for certain products that were already on the market as of August 2, 2026.
Among other requirements, providers of AI systems that generate synthetic text, images, audio or video must generally ensure that the output can be detected as artificially generated or manipulated. This may include machine-readable markings built into the content rather than displayed as a visible layer.
The AI Act also creates separate disclosure requirements for certain users of AI-generated content. For example, deepfakes generally must be clearly disclosed as AI-generated or manipulated; AI-generated or manipulated text about matters of public interest may also require disclosure when it is published without human review.
While the law provides the end goal, it does not necessarily define how to achieve it. For example, the EU AI Act was accompanied by the Guidelines on Transparency Obligations for Providers and Deployers of Certain AI Systems. These guidelines provide definitions and explain how compliance with the AI Act’s transparency obligations may be demonstrated. But still, these guidelines defer to “providers and deployers [who] can determine adequate measures themselves, while taking into account these guidelines.” In short, this means the EU AI Act sets the standard but leaves the means of meeting it to the AI provider or deployer.
This means AI transparency can look different, both because of the media involved and because the provider or deployer can determine how best to meet these transparency standards. For example, a machine-readable watermark may help technology detect that AI was involved, while a visible or audible disclosure is meant to inform the person viewing or interacting with the content that it was AI- generated or altered. But the form and format of these transparency measures may largely be subject to the organisation’s discretion.
The California AI Transparency Act
California has also adopted AI-content transparency requirements through the California AI Transparency Act. Originally enacted through SB 942 and amended through AB 853, this law went into effect on August 2, 2026.
The law generally requires providers of any “large online platform” – that is, a publicly available generative AI platform with over 2 million unique monthly visitors over the preceding 12 months – to provide tools that can help users determine whether covered image, video or audio content was created or altered by an AI system.
The California AI Transparency Act distinguishes between two different types of disclosures: latent and manifest. A latent disclosure is built into the content, but it’s not readily visible to the average person. A manifest disclosure is one that a layperson can more easily see and understand.
California’s requirements are also evolving. Businesses subject to the law may need to monitor legislative changes as lawmakers revisit how disclosure and detection requirements need to operate.
What does AI watermarking look like?
Anthropic’s recent changes to Claude provide one example of how companies may approach these requirements.
In August 2026, Anthropic announced that the new Claude models would include machine-readable markings in response to the EU AI Act and other similar legislative requirements. For generated text, Claude uses an imperceptible watermark based on patterns in how the model selects words. According to Anthropic’s press release, the watermark is not a visible or hidden set of characters and does not identify the person or company who generated the content.
However, these tools are far from perfect. Anthropic explains that a text watermark can disappear or weaken; they describe it as not “foolproof” if content is heavily rewritten, translated or combined with other materials. File-based provenance information can also get lost if files are converted, resaved or captured by screenshot.
This means that businesses should not treat watermarking as a perfect way to determine whether something was created by AI. Instead, it may be one tool to provide more information about where content came from and whether AI was involved.
Can EU rules affect U.S. businesses?
United States companies may also feel the effects of the EU AI Act even when a particular use of an AI product occurs outside Europe.
For a company operating in multiple countries, creating different versions of the same product for each jurisdiction can become expensive and technically complicated. A business may instead choose and decide to build one version that satisfies the strictest applicable requirements and use that version more broadly.
For example, Anthropic has said that although its Claude watermarking changes were driven by the EU AI Act, it is applying them globally rather than limiting them to European users.
This can create a spillover, where a law passed in one jurisdiction changes how an AI product operates for users everywhere else. Similar regulatory concepts are also appearing in U.S. state AI laws. California and other states have adopted requirements involving transparency, disclosures, and AI governance that overlap with themes found in the EU AI Act, even though the laws differ in their scope and specific requirements.
As AI regulations continue to develop across different jurisdictions, companies may consider whether maintaining different disclosure mechanisms per jurisdiction makes sense. Alternatively, companies may comply with the most stringent regulations across all markets, setting a higher standard of compliance across the board.
What should businesses take away?
Businesses do not necessarily need to approach every type of AI generated content in the same way. However, companies developing or using generative AI may want to review how their systems identify AI generated content, and whether their disclosure practices meet the requirements that might apply to them. Some steps businesses may want to consider include:
- Identifying what types of AI-generated content the business uses or provides to consumers, including text, images, audio and video.
- Determining whether applicable laws require machine-readable markings or visible disclosures.
- Understanding how watermarks or provenance information may change if content is edited or redistributed.
- Checking if AI-generated content from outside vendors keeps its watermark or disclosure when the business edits, downloads, or republishes it into another product.
- Considering whether maintaining different product versions across jurisdictions is practical.
- Monitoring United States, EU, and other developing AI transparency requirements and laws.
AI watermarking is still rapidly developing both technically and legally. Businesses do not necessarily need to treat every AI-generated output the same way, but they may need to increasingly understand when AI-generated content should be identified and whether their current systems can provide that transparency. For businesses using AI-generated content in marketing, customer communications, and other business activities, understanding when and how that content must be identified can help reduce compliance risks as these rules continue to develop
