Copyright Status and Rights Chain: What is Protected in AI Images – and What is Not

Brief Overview: AI images are created quickly, but their legal implications are complex. Three key areas are crucial: Firstly, copyright (human authorship, legal barriers, chains of rights), secondly, labeling and transparency (AI Act, platform and product obligations), and thirdly, publication practices (alt text, accessibility, SEO, metadata). By properly integrating publication, licenses, labeling, and technical provenance, you can reduce the risk of warnings and enhance discoverability.

Under copyright law, the concept of a work presupposes a personal intellectual creation, as defined in Section 2 (2) UrhG. The author must be a natural person, as specified in Section 7 UrhG. This implies that purely, completely automatically generated images, without any formative human contribution, typically do not achieve work status.

However, protection can arise if the human contribution reaches a personal level of design. This might include targeted prompt sequences, curated variants, compositing, retouching, and an overall creative concept. Borderline cases are evaluated individually.

For publishing practice, this means:

If there is no human creative contribution, the result carries no copyright. This does not mean the use is "right-free"; trademark, design, competition, personality, and contract law (e.g., model terms and conditions of the generators) remain relevant.
If human authorship exists, classic rules apply. These include moral rights (e.g., attribution), the transfer of rights of use, and the doctrine of achievement of purpose (Section 31 (5) UrhG). When working in a team, co-authorship and shares should be clearly regulated.
Integrating third-party elements (stock images, logos, purchased licenses) requires a clean chain of rights. This involves rights clearance for works and ancillary copyrights, editing rights (Section 23 UrhG), and observing exemptions in contracts.

Limitations such as quotation (§ 51 UrhG) and caricature/parody/pastiche (§ 51a UrhG) remain important corrective measures. Therefore, a meme or montage can be permissible, but only to a very limited extent and for a specific purpose. For portraits of real people, the right to one’s own image also applies (§§ 22 ff. KUG), typically requiring consent with narrow exceptions.

Practical Tip: For external creatives, record in briefings how the human creative contribution is made. This includes prompt documentation, selection decisions, and versioning. This facilitates justifying the copyright status later or, conversely, clearly classifying the result as copyright-free.

Labeling Obligations and Transparency: What Will Be Required in 2025

The AI Act applies throughout the EU, introducing transparency obligations for synthetic or manipulated content. Users must be able to recognize that content is artificially generated or modified. Providers of general AI models face additional obligations concerning copyright compliance and the aggregation of training content.

The Digital Services Act (DSA) also establishes platform processes, including reporting channels, risk mitigation, and transparency reports. Currently, no broad national standards for an "AI label" exist. Thus, AI Act transparency, industry standards, and platform requirements are decisive.

Concrete implementation ideas include:

Labeling in the Front End: For editorial or advertising content, clearly indicate "artificially generated/AI image" in the immediate vicinity of the image (e.g., caption or info icon with tooltip).
Metadata Labeling: Utilize content credentials (C2PA) for tamper-proof origin and processing documentation. This ensures that proof is retained even if the content is reused, provided metadata is not removed.
Internal Policy: Define product areas where labeling is mandatory (e.g., category lead, newsletter, social ads). Implement stricter defaults for sensitive contexts like politics or health.
Observe Platform Rules: Large platforms are increasingly checking for synthetic content and demanding labels. Standardized, reusable references in asset management can save rework.

Labeling is not an admission of guilt. It fulfills transparency obligations, reduces moderation and reputational risks, and strengthens evidence in case of conflict.

Publishing Practices: Alt Text, SEO, Accessibility, and Metadata

Alt texts are not mere decorations but a necessity. They make images accessible to screen readers and search engines. Effective alt texts are precise, describe the motif and function of the image within the page’s context, and avoid keyword stuffing. For more extensive motifs, an extended description (long desc) should be added.

SEO benefits also come from meaningful file names, structured data where appropriate, surrounding context, and image sitemaps.

Here is a checklist for image delivery:

Alt Text: Short, precise, and contextual. For decorative elements, use alt="".
Extended Description: For complex diagrams, store it in the immediate context.
File Name: Descriptive and consistent (avoid cryptic hashes in the delivery level).
Image Sitemaps: Maintain relevant attributes, as media-rich pages benefit significantly.
IPTC Fields: Fields such as Creator/Credit, Copyright Notice, DigitalSourceType, Rights-Statements, and Keywords should be maintained centrally in the Digital Asset Management (DAM) system. Dedicated accessibility fields (Alt Text/Extended Description) have been part of the IPTC specification since 2021/2024, facilitating consistent transfer to CMS/CDN.
C2PA/Content Credentials: Bind the provenance manifest to the asset, including creation tools, processing steps, camera attestation if necessary, and a seal/timestamp. For sensitive areas, use additional robust watermarks or "durable credentials" to reconstruct evidence even if metadata is lost.

Accessibility extends beyond alt text. It encompasses contrasts, zoom capability, keyboard navigation, focus visibility, and sufficient tap targets. For images, this means avoiding pure color communication without text repetition and refraining from placing essential information exclusively within the image.

Risk Matrix for AI Images: Typologies Leading to Disputes in Practice

Portraits of Real People

Portraits of real people are problematic without consent. Even synthetically created "photoclones" can infringe general personal rights. For commissioned work with real models, a written model release is crucial, defining scope (advertising/editorial), territories, duration, and AI specifics (e.g., prohibiting training/face-swaps). For generative "lookalikes," trademark, naming, and competition law must be carefully considered.

Logos, Brands, and Designs

Images originating from AI can infringe trademark rights if used to indicate origin or exploit reputation. In advertising, unfair exploitation of reputation must also be considered. Proprietary brands should be used consciously in generative workflows, requiring approval processes and adherence to brand protection guidelines.

Stock and Third-Party Licenses

Many image generators include license clauses on output usage, ranging from very liberal to restrictive. In mixed works (AI + stock), the most restrictive license conditions apply. AI training materials and copyright must be considered separately: permitted training does not necessarily imply permissibility of subsequent output in specific uses, such as logo use in advertising.

Reference Styles and Artist Names

Style imitation is rarely justiciable under copyright law as an independent object of protection. However, it can be tricky under unfair competition law or naming law, potentially leading to misleading impressions about origin or unlawful exploitation of reputation. In campaign communication, clear distancing is advisable (e.g., "inspired by," no deceptive impression).

Sensitive Contexts

For sensitive contexts, such as politics, health, or children, stricter internal approvals apply. Plan and document the labeling of synthetic image elements early. Standardize provenance assurance for election campaign or crisis communication using C2PA and qualified timestamps.

Workflows Enabling Legally Compliant Publications

A. Rights and Document Management

Create Project File: Document prompt history, intermediate steps, final selection, and processing steps.
Record Rights Chain: Detail own contributions, purchased elements, releases, license IDs, and output rights according to tool terms and conditions.
Document Barrier Check: If quotation, parody, or pastiche is used, record the purpose and scope.

B. Labeling and Metadata

Define Front-End Labeling: Specify location, wording, and icon.
Maintain IPTC Fields: Use accessibility fields (Alt-Text/Extended Description) in the DAM.
Generate C2PA Manifest: Where possible, link with eIDAS timestamp/seal to increase evidential value.

C. Review & Release

Legal Review List: Check for copyright status, personal rights, trademarks/design, and general terms and conditions conflicts.
Editorial Four-Eyes Principle: Apply for sensitive motifs.
Log Approval: Note regional specifics for social ads.

D. Operation & Incident Response

Provide Takedown Path: Establish counter-notification processes.
Address Objectionable Images: Take immediate action (depublishing/streaming), clarify facts, gather evidence from the project file, and document the decision.
Incorporate Lessons Learned: Update image policies, e.g., with additional labeling or blacklists for sensitive motifs.

FAQ for Daily Practice

Is every AI image to be labeled?

Not every AI image, but wherever its origin is significant or confusion with real recordings is likely, labeling is recommended. The AI Act requires transparency for artificially created/manipulated content, and many platforms require labels anyway. In editorial contexts, the classification (photojournalism vs. illustration) is decisive.

Can an AI image be used "freely" without copyright status?

Caution is advised. Even copyright-free results can affect the rights of third parties, such as trademarks, designs, personal rights, or contract law. If there are no exclusive rights of your own, the exclusivity of the motif is also not guaranteed.

How is the alt text formulated?

Alt text should be task-oriented and context-related: What does the image show and why is it here? Avoid keyword chains and redundant phrases. For decorative images, use alt="". For complex image information, provide an extended description in the body text or via a linked detailed description.

How do I secure the evidence?

Bind C2PA manifests to the file, archive hashes/logs in the DAM, and, where possible, provide them with a qualified timestamp/seal. This substantiates the creation, processing, and publication in a court of law.

Sample Policy (Short Version) for Teams

Definitions: "AI image" means created in whole or in part using generative processes; "synthetic part" refers to image information without a real template.
Labeling: Mandatory for media, political, health, and advertising environments; otherwise, it depends on the context. Placement should be close to the image, with short and clear wording.
Rights: Check the rights chain before publication; portraits only with release; logos/trademarks only with release.
Metadata: Maintain IPTC fields completely, alt text is mandatory, and use image sitemaps.
Provenance: C2PA is mandatory for in-house productions in editorial/advertising environments; for additional purchases, providers with content credentials are preferred.
Review: Sensitive motifs must be released by legal/editors.
Incident Response: Establish a takedown path, correction instructions, and documentation obligations.

Conclusion

Legally compliant publishing of AI images means accurately classifying human authorship, securing rights chains, applying transparent labels, and properly implementing accessibility. Alt text, IPTC metadata, and C2PA provenance are not merely "nice-to-have extras" but form the backbone of scalable image governance. By standardizing these building blocks, you can publish faster, with greater certainty, and increased visibility.