Local AI for companies: Using LLMs securely

AI has arrived in the German economy: According to the latest Bitkom study ‘Artificial Intelligence in Germany’, 36% of companies already use AI – almost twice as many as a year earlier (20%). Another 47% are planning or discussing its use. At the same time, many companies name clear reasons that hold them back:

One contradiction stands out: 93% of companies would prefer to use AI from Germany. In practice, however, 70% of companies that use generative AI rely on ChatGPT – European providers hardly play a role so far.

These figures highlight that the widespread adoption of AI is not just a matter of technical feasibility but also of awareness and trust-building. In this article, we aim to address some of these concerns and demonstrate how companies can harness the potential of AI technologies securely and in compliance with data protection regulations by deploying locally hosted LLMs (Large Language Models). Many of the cited challenges already have practical solutions – especially when AI solutions are operated locally or within privacy-compliant infrastructures in Germany.

An LLM (Large Language Model) is an AI system that has been specifically trained to understand and generate language. It draws on extensive text datasets to recognize patterns, structures, and meanings in language. With the help of deep learning, the model learns to establish connections between words and sentences so that it can provide precise, context-related responses to inputs. We speak of a local LLM when the model runs on your own infrastructure, for example built into your own software.

Local LLMs have a wide range of applications: for example, they support chatbots, automated translation systems, text generation, and complex analyses of large amounts of text. Despite their capabilities, LLMs also have their limitations, such as when it comes to interpreting ambiguities or avoiding biases from the training data.

Thanks to freely available open-source LLMs, companies can build their own local AI. This allows them to retain control over their own data when using AI: even sensitive internal company data can be entered into this proprietary local AI.

There are a few characteristics that apply to all LLMs, regardless of whether the model is run locally or used as part of a public AI service:

• LLMs have a cut-off date and do not have access to real-time information.
• LLMs can hallucinate – sometimes in a very convincing way.
• LLMs are trained on publicly available information and can only process what they have learned from these sources. (Unless they are implemented locally using RAG – more on that below.)

The fact that LLMs can generate incorrect or misleading information means that AI should not be used without a basic level of knowledge within the team. Awareness of both the potential and the limitations of language models is essential—this includes data protection concerns as well as the possibility of inaccurate outputs. With proper team training, careful usage, and a privacy-friendly technical setup, there is nothing standing in the way of successfully integrating language models into a business.

There are now many large language models developed by various organizations and research institutions. The best known is OpenAI’s GPT series, which powers ChatGPT. In addition, there are numerous models whose trained weights are freely available – such as Meta’s Llama, Google’s Gemma, the models from Mistral, Alibaba’s Qwen or DeepSeek. Even OpenAI has now released open models with gpt-oss. Companies can run such models on their own infrastructure and use them for their own purposes.

Data protection and legal security are key aspects that companies must consider when implementing AI technologies. Simply entering trade secrets or personal data into services like ChatGPT is not a good idea. This is particularly relevant in Germany and Europe, where the General Data Protection Regulation (GDPR) imposes strict requirements. Businesses must ensure that any personal data processed by AI systems is protected in accordance with legal regulations.

One of the most effective ways to address concerns around data protection and security is by using locally implemented AI solutions. Running AI systems on in-house servers or within GDPR-compliant hosting services in Germany allows companies to maintain full control over their data. This not only reduces legal risks but also enhances data security and fosters trust.

That being said, the most powerful commercial models are still ahead of locally operated open-source models in some disciplines. For many concrete business tasks, however – such as extracting information from documents, drafting texts or answering questions based on internal records – local LLMs are powerful enough. Our client projects show what this looks like in practice.

For companies looking to deploy local AI solutions, careful planning is essential. A well-thought-out strategy should consider existing processes, security requirements, and cultural shifts within the organization. Successful implementation relies on close collaboration between IT teams, business departments, and external specialists. Below are some best practices to guide this process:

• Choosing the right model
  Select a language model that can be adapted to internal requirements. Opt for providers that offer transparent training processes and GDPR-compliant solutions.
• Secure infrastructure
  Build an IT environment that meets the highest security standards, ideally in compliance with German and European data protection laws.
• Seamless integration into existing workflows
  Ensure that AI solutions are smoothly embedded into business processes. Close collaboration between IT teams, domain experts, and external partners is key to success.
• Training and change management
  Support AI adoption with comprehensive training for employees. This helps unlock the full potential of the technology while reducing resistance and uncertainty.

To fully leverage an LLM’s potential, integrating internal, company-specific data is often essential. With a local LLM, this is easily achievable.

By incorporating RAG (Retrieval Augmented Generation) with proprietary data – such as internal documents, emails, or best practices – an LLM becomes a true knowledge repository, tailored to your company’s specific terminology and needs. With a local deployment, all data remains within the company, ensuring that sensitive information – such as trade secrets or customer data – is securely processed without exposure to external cloud environments.

Integrating internal data not only improves response accuracy but also enables the LLM to address industry-specific challenges and complex business inquiries. Companies benefit from a custom AI solution that is finely tuned to their exact requirements.

The key to success in the digital future lies in the intelligent use of data – while maintaining data privacy and legal security. Local LLMs provide an ideal solution by combining AI technology with full control over company data. Businesses that invest early in privacy-compliant AI solutions not only gain a valuable competitive edge but also position themselves as industry leaders in the long run. Additionally, targeted AI applications can help bridge talent shortages and automate administrative processes efficiently.

Since every industry and company has unique requirements, tailored local AI solutions enable precise responses to specific challenges.

We invite you to explore the potential of local AI solutions with us – whether in a potential analysis of your current processes or through personalized consulting. Let’s find out how our AI solutions can help you overcome challenges and take the next step toward a secure, data-driven future.