In addition to concerns about data protection and confidentiality, the fear of copyright infringement is the second major obstacle for many when using artificial intelligence. In practice, however, the risk is not very high, at least for those who use prefabricated AI models – if a few rules are observed. We look at this in part 14 of our AI blog series.

First of all, a brief overview of where the problem of third-party rights to content may arise, especially when using generative artificial intelligence. This is not only copyright law, but also unfair competition law and possibly special legal provisions. For example, depending on the legal system, it may be prohibited to copy and use the commercial results of the work of others without a reasonable effort on your part (Art. 5 of the Swiss Unfair Competition Act), or to copy entire or large parts of third-party databases, even if they are not protected by copyright (EU Directive on the Legal Protection of Databases). In part 10 of our AI blog series, we discussed in detail the responsibilities of AI providers and users when it comes to third-party rights.

Seven challenges

When copyright and similar topics are at issue, users of AI systems that rely on prefabricated AI models will face challenges in three areas:

• Model: The AI model used "contains" copyrighted third-party content because it has been trained with such content without a sufficient legal basis. Several court cases are currently underway to clarify this issue. The user will generally not know, or not know exactly, how the AI model he is using has been trained, i.e. there is little he can do about it, and he will receive little information that could help him choose a model.
• Input: The input that feeds an AI system contains copyrighted third-party content for which there is no adequate legal basis for its intended use. This use can consist of either the generation of outputs (the input is then also the "prompt") or the training or fine-tuning of the AI model (the input is used to adjust the parameters of the AI model so that outputs generated with it later become "better").  
• Output: The output generated by the AI system contains copyrighted third-party content. This can happen for four main reasons: First, the AI system may generate such content because its model recognises it as a result of its training, even if the user does not want it to. Depending on the AI technique, this will only happen in practice if the content in question has been presented to the model often during training, and there is therefore a high probability that the model will consider this content to be the correct response to the corresponding prompt (more on this below). Second, such an output can be generated based on the user's input, either because the AI system was given the content in question with the prompt, or because the prompt contains an instruction to "recreate" the third-party work. Third, an AI system may in fact generate content that corresponds in relevant parts to an existing work, but this is nevertheless a random result (more on this below). Fourth, the provider of an AI system (or the service provider offering it as a service) may claim the output as its own work; although it will not normally be able to rely on copyright law to do so if the work is entirely machine-generated, it may be able to secure rights in the work vis-à-vis the user by contract and, if necessary, by other laws such as unfair competition law.

Here is a graphic overview of the seven challenges:

---

This article is part of a series on the responsible use of AI in companies:

• Part 1: AI at work: The three most important points for employees
• Part 2: Popular AI tools: What about data protection?
• Part 3: AI: 11 principles – and an AI policy for employees
• Part 4: How to assess the risks of both small and large AI projects
• Part 5: AI governance in our company – who is responsible?
• Part 6: The flip side of the coin: Where we need to protect AI from attackers
• Part 7: The EU AI Act - what it means in practice for most companies
• Part 8: AI in financial institutions – opportunities and challenges
• Part 9: AI in marketing: 4 unfair competition law pitfalls
• Part 10: Copyright and AI: Responsibility of providers and users
• Part 11: Fair play with AI: How sports organisations may use athlete data
• Part 12: Feeding AI: How to commercialize data sets
• Part 13: AI in Human Resources
• Part 14: Copyright and AI: How to protect yourself in practice
• Part 15: What you should legally consider when retaining AI providers
• Part 16: How companies can ensure transparency for their use of AI
• Part 17: What is inside an AI model and how it works
• Part 18: The most important compliance requirements for AI
• Part 19: Language models with and without personal data
• Part 20: Risk-based approach: Not all AI applications harbour high risks
• Part 21: The right of access to large language models
• Part 22: Guidelines for using artificial intelligence in financial institutions (FINMA) - VISCHER
• Part 23: Switzerland: What to expect in terms of AI regulation
• Part 24: Five trigger questions for more AI compliance
• Part 25: AI Tools – What about data protection?
• Part 26: AI training: How to Handle Copyright, Data Protection And Other Restrictions
• Part 27: How small and medium-sized enterprises can use AI in a compliant manner
• Part 28: When Media Houses and AI Providers clash – the Pitfalls of AI Regulation
• Part 29: The Challenges of Achieving AI User Adoption
• Part 30: Overview of Key EU Digital Regulations
• Part 31: AI Tools – What about data protection? (2026)
• to be continued...

We support you with all legal and ethical issues relating to the use of artificial intelligence. We don't just talk about AI, we also use it ourselves. You can find more of our resources and publications on this topic here.