Legal aspects of self-hosted LLMs: own use vs. service offering

The implementation and use of self-hosted Large Language Models (LLMs) opens up a wide range of possibilities, but also poses considerable legal challenges. These vary significantly depending on the application scenario and require a differentiated approach. The following section discusses the key legal aspects both for in-house use and for offering as a service to third parties. It becomes clear that the legal implications go far beyond superficial considerations and make a well-founded legal analysis indispensable. The complexity of the matter underlines the need for professional legal support in order to minimize potential risks and ensure compliance.

Own use of self-hosted LLMs

When using a self-hosted LLM exclusively yourself, the legal situation is initially relatively straightforward. Nevertheless, there are various legal pitfalls that require careful consideration. The following aspects should be given particular consideration:

1. License terms:
   It is essential to carefully examine the license terms of the LLM used. These models are often subject to restrictive terms of use that exclude or limit commercial exploitation. Disregarding these provisions can have serious legal consequences, including potential claims for damages or injunctive relief. It is therefore advisable to subject the license agreements to a detailed legal analysis and, if necessary, to consult with the licensor.
2. Data protection aspects:
   Compliance with data protection regulations is also essential for personal use. This applies in particular to the processing of personal data that may occur in prompts or outputs. The implementation of technical and organizational measures to ensure data security is of central importance here. In addition, data processing procedures should be documented in order to be able to prove compliance with data protection regulations if necessary.
3. Copyright implications:
   The content generated by the LLM may contain elements protected by copyright. Careful examination before further use is therefore essential in order to avoid potential copyright infringements. This includes analyzing the output for protected work elements as well as observing possible property rights to the LLM training data. In case of doubt, a copyright assessment should be carried out by a specialist copyright lawyer.
4. Liability risks:
   When using LLM-generated content for business decisions, the potential liability risks must be carefully weighed up. The reliability and accuracy of AI-generated information should be critically scrutinized. It is advisable to establish internal guidelines for dealing with LLM outputs and to document decision-making processes. Liability insurance that explicitly covers damage caused by AI systems should also be considered.
5. Compliance requirements:
   Depending on the industry and intended use, specific compliance requirements may apply, which must also be observed for own use. This may relate to regulatory requirements in the financial sector or healthcare, for example. A comprehensive compliance check, taking into account the industry-specific regulations, is therefore essential. Implementing a robust compliance management system can help to minimize regulatory risks.
6. IT security:
   The implementation of appropriate security measures is also of central importance when it comes to in-house use. This includes not only technical aspects such as firewalls and encryption, but also organizational measures such as access controls and employee training. A comprehensive IT security concept should be developed and regularly reviewed for its effectiveness. In particular, the specific risks arising from the use of AI systems must be taken into account.
7. Documentation and traceability:
   Detailed documentation of LLM use is strongly recommended, especially if the generated content is used for important decisions. This not only serves internal traceability, but can also be of decisive importance in the event of legal disputes. Logs should be kept of the type of use, the prompts used and the outputs generated. It is also advisable to implement version management for the LLM in order to be able to track changes in system behavior.
8. Ethical considerations:
   Although there is no direct legal obligation, ethical aspects should be taken into account when using AI systems. This can help to minimize risks in the long term and promote acceptance of the technology. The development of internal ethical guidelines for dealing with AI can be helpful here. In addition, LLM outputs should be regularly reviewed for possible bias or discriminatory content.

Offer as a service to third parties

The provision of a self-hosted LLM as a service for third parties significantly increases the legal requirements and requires comprehensive legal consideration. The following aspects are of particular relevance here:

1. General Data Protection Regulation (GDPR):
   As a provider of an AI service, you become a controller within the meaning of the GDPR, which entails far-reaching obligations. This includes the creation of comprehensive data protection declarations, the maintenance of processing directories and, if necessary, the performance of data protection impact assessments. In addition, technical and organizational measures must be implemented to ensure the security of the processed data. The appointment of a data protection officer may be necessary. It is advisable to establish a comprehensive data protection management system and carry out regular external audits.
2. Contract design:
   The drafting of precise contractual agreements with users is of central importance. These should define the scope of services in detail, clearly formulate limitations of liability and set out comprehensive terms of use. Particular attention should be paid to the regulation of warranty claims and the definition of service level agreements. The contracts should also contain clauses on data processing, intellectual property and confidentiality. It is essential that contracts are regularly reviewed and adapted to changing legal conditions.
3. Liability risks:
   The liability risk in the provision of AI services is considerable and requires careful risk analysis. The implementation of a robust risk management system is strongly recommended. This includes the identification of potential damage scenarios, the development of preventive measures and the preparation of contingency plans. Consideration should be given to taking out specialized liability insurance that explicitly covers damage caused by AI systems. It is also advisable to set up an internal monitoring system to identify potential liability risks at an early stage.
4. Copyright aspects:
   The copyright situation for AI-generated content is complex and still partially unclear. It must be ensured that the use and dissemination of the content generated by the LLM is permitted under copyright law. This requires careful examination of the LLM’s training materials and clear contractual regulations regarding the rights to the generated outputs. It may be useful to implement technical measures to identify potentially copyrighted content in the LLM’s output. In addition, clear guidelines should be drawn up for users regarding copyright responsibilities.
5. IT security and data protection:
   The implementation of comprehensive security measures is essential to protect user data and prevent unauthorized access. This includes technical measures such as encryption and firewalls as well as organizational precautions such as access controls and employee training. The development of a comprehensive information security management system (ISMS) in accordance with ISO 27001 should be considered. Regular security audits and penetration tests should be carried out to check the effectiveness of the protective measures. In addition, an incident response plan should be established in the event of data breaches or security incidents.
6. Transparency and information obligations:
   There is a need to provide clear and comprehensible information about the fact that this is an AI system. Users must be informed about the limitations and risks of the technology. This includes information about possible sources of error, biases in the results and the limits of the system’s reliability. It is advisable to develop a comprehensive communication strategy that takes into account both legal and ethical aspects. Regular updates and training for users can help to improve understanding of the possibilities and limitations of the system.
7. Quality assurance and system monitoring:
   The establishment of a robust quality management system is essential to ensure the reliability and safety of the service. This includes regular reviews and updates of the system and the implementation of feedback mechanisms for continuous improvement. The development of key performance indicators (KPIs) to measure system performance and quality is advisable. In addition, a monitoring system should be implemented that detects anomalies in system behavior at an early stage and triggers automated alarm mechanisms. Setting up a dedicated team for the continuous monitoring and optimization of LLM can be useful.
8. Industry-specific compliance:
   Depending on the use case and target group, there may be additional regulatory requirements that need to be met. For example, this may relate to specific requirements for the financial sector, healthcare or public administration. A comprehensive analysis of the regulatory environment and the development of a tailored compliance program are essential. Cooperation with industry associations and regulatory authorities can be helpful in identifying and addressing emerging regulatory trends at an early stage. The implementation of a compliance management system that is regularly reviewed for its effectiveness is strongly recommended.

Conclusion and recommendation for action

The use of self-hosted LLMs, whether for personal use or as a service for third parties, opens up a wide range of opportunities, but also poses considerable legal challenges. The complexity of the matter and the constantly evolving legal situation require continuous legal support and adaptation of compliance strategies. It is highly advisable to seek expert legal advice at an early stage in order to identify potential risks and implement suitable protective measures. A proactive approach to legal structuring can not only ensure compliance, but also provide a competitive advantage. Developing a holistic strategy that integrates technical, organizational and legal aspects is key to the successful and legally compliant implementation of LLM.

Author: Marian Härtel

Marian Härtel ist Rechtsanwalt und Fachanwalt für IT-Recht mit einer über 25-jährigen Erfahrung als Unternehmer und Berater in den Bereichen Games, E-Sport, Blockchain, SaaS und Künstliche Intelligenz. Seine Beratungsschwerpunkte umfassen neben dem IT-Recht insbesondere das Urheberrecht, Medienrecht sowie Wettbewerbsrecht. Er betreut schwerpunktmäßig Start-ups, Agenturen und Influencer, die er in strategischen Fragen, komplexen Vertragsangelegenheiten sowie bei Investitionsprojekten begleitet. Dabei zeichnet sich seine Beratung durch einen interdisziplinären Ansatz aus, der juristische Expertise und langjährige unternehmerische Erfahrung miteinander verbindet. Ziel seiner Tätigkeit ist stets, Mandanten praxisorientierte Lösungen anzubieten und rechtlich fundierte Unterstützung bei der Umsetzung innovativer Geschäftsmodelle zu gewährleisten.