Copyright & AI — What Applies to Your Content

Copyright and AI-Generated Content: The Legal Starting Point

The ongoing development of artificial intelligence raises concrete legal questions for content managers. The creation of AI-generated content presents traditional copyright law with new challenges.

Autonomous systems produce texts, images, and audiovisual media of high technical quality. For publishers and companies, the question arises as to who owns the copyright to these works.

It must also be clarified how the legal protection of one's own painstakingly created content can be ensured against uncontrolled access by third parties.

The Concept of a Work and Human Creative Input

Copyright law is historically closely linked to the concept of personal intellectual creation. It protects works by authors that achieve a certain level of creative input and show individual character.

When the creative performance is not provided by a human, but by artificial intelligence, these traditional standards often fall short. The central question is whether AI-generated content qualifies as “works” in the legal sense.

According to § 2 para. 2 of the German Copyright Act (UrhG) (Source: Federal Ministry of Justice, UrhG), only personal intellectual creations are protected. The prevailing legal opinion tends to deny this protection to purely machine-generated content.

The reason is simple: an AI lacks legal personality. An algorithm cannot be an author under current law, which is why the pure output of a language model initially enjoys no copyright protection.

Human Intervention as the Key to Legal Protection

An exception may exist if a human uses the AI as a mere tool. However, this requires more than just entering a simple command.

Through detailed instructions (prompting), the targeted selection of training materials, or extensive editorial post-processing, one's own creative performance can be brought in. In such cases, the human can be considered the author of the end product.

For companies, this means in practice that unedited AI texts are generally in the public domain. They could theoretically be adopted and reused by third parties without license fees.

Those who want to protect their corporate content should therefore establish editorial processes that ensure demonstrable human revision and refinement of the machine-generated drafts.

Training Data and Text and Data Mining (TDM) in the EU

AI systems learn from large amounts of existing data, which is often itself protected by copyright. The use of this data for training AI models requires careful consideration of licensing.

In addition, there is the potential infringement of existing copyrights if web content flows into the vector databases of AI providers without the explicit consent of the authors.

In the European Union, the EU Copyright Directive (DSM Directive) provides the legal framework for these processes. It attempts to strike a balance between the interests of rights holders and the promotion of innovation in the AI sector.

The Machine-Readable Reservation of Rights in Practice

Article 4 of the EU Copyright Directive (Directive (EU) 2019/790) (Source: Official Journal of the European Union) regulates what is known as Text and Data Mining (TDM). According to this, lawfully accessible works may be used for TDM.

However, this only applies as long as the rights holders have not expressly reserved this use. This reservation of use (opt-out) must be in machine-readable form for online accessible content.

A machine-readable reservation of rights signals to crawlers that the content should not be used for AI training. Technically, this is often implemented via the robots.txt file.

Alternatively, specific meta tags in the HTML header or HTTP header instructions are used. If such a reservation is technically correctly implemented, reputable AI providers should respect it and exclude the data from their training sets.

Limits of Opt-outs and Legal Enforcement

However, a missing reservation of rights does not mean that publishers completely waive their rights. It merely facilitates automated extraction for training purposes within the scope of legal limitations.

The practical enforcement of this reservation remains a challenge. While large providers like OpenAI or Google offer mechanisms for respecting robots.txt, smaller or less regulated scrapers sometimes ignore these instructions.

Furthermore, an opt-out usually only applies to future training runs. Data that has already been processed in older model versions is difficult to remove from neural networks retrospectively.

Generative Engine Optimization (GEO): How AI Search Systems Select Sources

When users search for information, AI-powered systems such as Perplexity, ChatGPT Search, or Google AI Overviews are increasingly being used. These platforms change the way answers are prepared and sources are cited.

Many of these AI search systems work with retrieval mechanisms that retrieve external sources, evaluate passages, and use them as context for the answer. The selection of sources is not random.

It is based on the relevance, semantic structure, and informational density of the documents. Generative Engine Optimization (GEO) deals precisely with the question of how content should be prepared to be considered as a source in these new search environments.

Retrieval Mechanisms and Citation Probability

According to the Princeton study on Generative Engine Optimization (GEO) (Source: Princeton University, "GEO: Generative Engine Optimization", 2023), clear structuring increases the likelihood that a text will be used as a source by language models.

The inclusion of citations and a high density of facts also play an important role in the benchmark study. AI systems can process content more easily if it is logically structured and broken down into semantic units.

The Google Search Central documentation (Source: Google Search Central) indicates that systems rely on indexable, visible, and helpful content. There is no special schema markup to be included in AI Overviews.

Rather, it is crucial that the content precisely serves the search intent and is technically accessible to the Googlebot. A well-founded preparation of the content increases the chance that systems will more easily consider the text as a source and link to it in the generated answer.

Multi-Engine Framing: More Than Just Google

When optimizing for AI systems, a pure focus on Google falls short. Google AI Overviews, Perplexity, ChatGPT Search, voice assistants, and internal enterprise searches operate on an equal footing.

Each of these systems uses different models, link sets, and query fan-out strategies to break down search queries into sub-questions and process them in parallel.

Therefore, it is important to prepare content in such a universal and factually dense way that it can be equally well captured by various retrieval pipelines and classified as a relevant source.

Optimize Content Structure and Topic Coverage with SEOlyze

To prepare content in such a way that it is optimally understandable for both human readers and AI systems, a data-driven approach is advisable.

Precise topic coverage signals to retrieval systems that a document offers comprehensive answers to a search query. Observations from practice show that the complete coverage of relevant sub-topics is related to visibility in search engines.

The more accurately a text covers the entities and technical terms of a topic area, the more likely it is to be used by algorithms as a factually sound source.

Data-Driven Analysis and Structuring

Where manual analysis often reaches its limits, SEOlyze helps you extract user questions directly from current SERP data. This allows you to identify exactly which aspects your target audience is truly interested in.

In addition, with SEOlyze, you can identify missing terms and topic areas in your texts. The system compares your draft with the top results and shows you which semantic gaps still need to be closed.

Use SEOlyze to plan your content structure based on data and to check in a fair competitive comparison which topic areas still need to be covered to be cited as a relevant source.

Before-and-After Example: Content Refinement

A concrete before-and-after example shows how the depth of content changes through targeted optimization and how the probability of citation can be increased:

Before (without optimization): AI content is legally difficult. You often don't know who owns the text. Copyright is not yet entirely clear here. If you use AI, you should be careful not to violate any rights.

After (optimized with SEOlyze): The legal assessment of AI-generated content requires a precise examination of the creative input according to § 2 UrhG. A machine-readable reservation of rights is essential to control automated scraping by AI bots. Only through substantial human post-processing can a copyrighted work be created that remains protected from unlicensed use.

By using SEOlyze, you can score your AI draft and enhance its content so that it reaches the required professional level. SEOlyze also supports you in creating precise alt texts for images to make the visual context machine-readable for multimodal systems.

Technical Control: Crawler Management for GPTBot, ClaudeBot, and Co.

The technical accessibility of a website determines whether AI systems can even capture the content. Without clean crawling, even the best text remains invisible to algorithms.

While classic search engine crawlers like Googlebot or Bingbot have been known for years, the landscape of user agents has expanded significantly.

Real AI bots like GPTBot, OAI-SearchBot, PerplexityBot, and ClaudeBot specifically search the web for training data or retrieve real-time information for specific search queries.

Differentiation of Bot Types

The OpenAI documentation on web crawlers (Source: OpenAI Platform Docs) specifies, for example, that GPTBot is primarily used for collecting training data.

The OAI-SearchBot, on the other hand, is explicitly used for search functions (like ChatGPT Search). ChatGPT Search also relies on third-party search partners like Bing depending on the query.

Those who want to make their content discoverable for AI searches should not block these crawlers universally in robots.txt. Blocking Bingbot or OAI-SearchBot can lead to one's own page no longer being considered as a source in ChatGPT Search's answers.

Logfile Analysis as an Early Indicator

Accesses by these bots in the server logfiles serve as a technical early indicator that a page is accessible and being crawled. They show that the technical infrastructure allows retrieval.

However, bot access is not a guarantee of visibility or usage. It does not prove that the content is actually cited in an AI answer.

Logfile data should always be evaluated in combination with citation monitoring and referral traffic. Only then can the actual influence of AI systems on one's own traffic be realistically measured and controlled.

Structured Data: Foundation for Machine-Readable Context

Structured data according to the Schema.org vocabulary helps machines to grasp the entities and relationships on a website more quickly. They translate human language into a standardized, machine-readable format.

They are not a guarantee that an AI system will select a page as a source. Which sources an AI system specifically chooses remains system- and query-dependent.

Nevertheless, structured data forms a strong foundation, as it makes information easier to verify and process. It reduces semantic ambiguity for algorithms.

Article Markup Instead of Isolated FAQ Snippets

In the past, the FAQPage schema was often used to achieve prominent rich results in Google Search. According to current Schema.org specifications and Google guidelines (Source: Schema.org / Google Search Central Blog), FAQ rich results are no longer displayed for most pages in classic search.

However, this does not mean that the schema type should no longer be used. FAQPage continues to serve as a structured data source that helps AI systems semantically correctly assign question-answer pairs, even if it no longer functions as a primary Google rich result lever.

Aligning Visibility and Markup

For guide texts, blog posts, and journalistic content, Article or BlogPosting markup is primarily recommended. These types best describe the core content of the page.

Visible FAQ content should be cleanly integrated into the main text and provided with the appropriate markup. Structured data must necessarily match the visible text; hidden content that only exists in JSON-LD can be devalued by systems.

If AI systems can validate the context of an article more quickly through clean markup, this can support its processing as a reliable source of information and increase the likelihood of citation.

Practical Recommendations for Content Managers

Given the evolving legal situation and technical requirements, it is crucial for companies to take proactive measures.

A forward-looking strategy minimizes legal risks when using AI-generated content. At the same time, it prepares one's own publications for the complex requirements of AI search systems.

It is no longer enough to write texts only for classic search engines. Content must be legally secure and technically optimized for retrieval pipelines.

Transparency Obligations under the EU AI Act

The EU AI Act (Artificial Intelligence Act) (Source: European Parliament, EU AI Act) provides, among other things, concrete transparency obligations. Providers and users of AI systems must ensure that AI-generated content is recognizable as such.

This builds trust with users and meets upcoming legal standards. The following recommendations should be observed in practice:

• Transparency and Labeling: Clearly indicate which text passages or images were generated by an AI. Open communication strengthens credibility.
• License Management for Training Data: Check the license terms of the sources you use for internal RAG systems or fine-tuning models. Pay attention to existing reservations of use.
• Human Intervention: To claim copyright protection for your own content, substantial human involvement is required. Use AI for research or structuring, but contribute your own creative input to the final text.
• Check Reservation of Rights: Strategically decide whether you want to prevent the scraping of your content for AI training purposes via robots.txt or meta tags to protect your intellectual property.
• Documentation: Maintain detailed documentation of the content creation process to be able to prove human authorship and creative input in case of doubt.

A future-proof approach to content requires continuous adaptation to new circumstances. Those who respect the legal framework and technically prepare their content cleanly for retrieval systems create the best conditions to be perceived as a relevant source in the age of AI search.