Local AI: How companies use AI securely with sensitive data

Engineering data, inspection reports, contracts or patient records do not belong in Copilot, ChatGPT and the like. Most companies rightly draw this line. On top of that, there is a second problem: general-purpose AI assistants sound convincing, but they know neither your processes nor your documents. For tasks where every detail matters, that is not enough. This is why companies are investing in their own AI that works with their data and does not let that data leave the organization.

According to a Bitkom survey from September 2026 (in German), 57 percent of companies in Germany use AI, up from 36 percent a year earlier. Two thirds of the companies that already use AI name data protection requirements as the biggest obstacle.

Local AI resolves this conflict. The language models run on your own servers or in a data center in Germany, and your data never leaves this environment. This lets you use AI for sensitive tasks as well. And because the AI accesses your own documents, its answers are based on your knowledge and can be traced back to the source. In this article, you will learn what local AI is, how it differs from cloud AI, what makes it interesting for businesses, where its limits are and how to get started.

Local AI is an AI solution that runs on a company's own infrastructure or in a dedicated, GDPR-compliant data center. The company keeps full control over its data, the model and operations.

Similar terms are enterprise AI, your own AI, private AI or sovereign AI. They are not used consistently, and some providers also use them for cloud offerings with data centers in the EU. In this article, local AI always means that the company runs the model itself or has it run exclusively for its own use.

At its core, local AI consists of a language model (large language model, or LLM) that does not run at a provider such as OpenAI or Microsoft, but on servers the company operates itself or rents exclusively. Prompts, documents and answers never leave this environment. They are not used to further train a third party's model either.

This is possible because there are now powerful open-source models that anyone may download and run, for example from Mistral, Qwen or Google (Gemma). For many everyday business tasks, such as summarizing documents, searching knowledge or filling in templates, these models are good enough. Read more in our article on local LLMs in organisations.

Between “everything with a US provider” and “everything on your own servers” there are several options. The following overview shows the three common operating models.

The sovereign cloud is a middle ground: the data stays in the EU, but the model still belongs to a large provider. For some applications this is the right choice, for example when very large numbers of requests have to be processed. It is not local AI in the strict sense, though. We show what such a setup looks like in our article Azure OpenAI: Running GPT Models in Compliance with the GDPR.

How Microsoft 365 Copilot, Google Gemini, ChatGPT and Claude differ from a local solution in detail is shown in our article Enterprise AI: Cloud vs. On-Premises Solutions Compared.

Many companies respond to ChatGPT with bans or usage rules that, in the worst case, are not followed in everyday work. The result is shadow AI: company data ends up in private accounts, and nobody can trace what happens to it. According to a Bitkom survey from 2025 (in German), 4 out of 10 companies assume that employees use private AI tools for work. Local AI turns this situation around. IT provides a tool that is similarly convenient in everyday use but runs on the company's own servers. Employees may use it for confidential documents as well, and the company keeps track of who uses which data. Our guide to secure AI for businesses shows which rules, training and safeguards are also part of this.

With cloud AI, prompts are processed in data centers that are often outside the scope of the GDPR. A data processing agreement governs how personal data is handled, but it does not automatically protect trade secrets or confidential knowledge. With local AI, this question does not arise, because the data never leaves the company. This also makes it easier to reach agreement with data protection officers and the works council. Read more in our article AI and data protection.

Cloud AI is usually billed per user or per request, and the provider sets the prices. Your own local AI mainly incurs costs for initial development, hardware or hosting, and operations. These costs can be planned and do not rise with every additional request. Whether it pays off depends on the number of users and how intensively they use it. Our cost calculator for hardware and operations gives you a first estimate.

If you build your processes on a single provider's model, you depend on its prices, terms of use and product decisions. With local AI, you can replace the language model as soon as a better one becomes available. The application you build on top of it remains your property. This desire for independence is widespread: according to a Bitkom survey from November 2026 (in German), 96 percent of companies would prefer to use AI applications from German providers.

ChatGPT is available to every company. Your design history, your inspection reports and decades of project experience, on the other hand, belong to your company alone. An AI that can access this knowledge answers questions a general-purpose model cannot. The knowledge of experienced employees remains available even after they retire. A data protection issue thus becomes a competitive advantage.

To let the AI work with manuals, inspection reports, contracts or email history, a method called retrieval-augmented generation (RAG) has become the standard approach. For every question, the AI retrieves the relevant documents and bases its answer on them. The documents are not trained into the model and always stay up to date. For sensitive documents in particular, it makes sense to implement this access locally. We explain how it works technically in our article LLM with RAG.

Local AI is not the best choice for every task. The largest commercial models are still ahead of open-source models when it comes to complex reasoning and very long texts. For research across the entire internet or for creative tasks without sensitive data, cloud AI is the better option.

In addition, someone has to run the infrastructure. That includes servers, updates, monitoring and security, as well as quality assurance for the answers. Language models can state false information very convincingly. That is why every AI application in a company needs testing and clear rules on when a person checks the result. If you do not want to carry this effort yourself, you should plan the hosting from the start. Many AI projects get stuck in the pilot phase because these very questions are only asked once the prototype is finished. If you think about operations, testing and integration from the outset, the solution makes it into everyday work.

This section is aimed at IT managers who want to assess the effort involved.

For business use, open-source models are an option, for example from Mistral (such as Ministral), Qwen or Google (Gemma). Which model fits depends on the language, the task and the available hardware. Smaller models respond faster and need less computing power, while larger ones deliver better results on demanding tasks. It has proven useful to test several models with real data from the use case before deciding.

Language models run best on graphics processing units (GPUs) with enough video memory. For a pilot with a smaller model, a single server with a powerful GPU is often enough. If many employees are to work with it at the same time or larger models are to run, the requirements grow accordingly. You do not have to buy the hardware: it can also be rented as a dedicated server in a data center.

Ollama is widely used to run the models. Under very high load, specialized runtimes such as vLLM are an option. For RAG, you also need an embedding model and a vector database, for example PostgreSQL with the pgvector extension. To run AI locally, you have two hosting options: in your own data center (on-premises) or on dedicated servers in a German data center. Our page on local AI technology shows which technologies we use.

Local AI pays off above all where employees spend a lot of time with documents and the content is confidential:

• Finding company knowledge: Employees ask questions in natural language and get answers from internal manuals, policies and project documents, with a reference to the source.
• Analyzing documents: The AI reads inspection reports, contracts or invoices and turns the relevant information into structured data.
• Filling in templates: Recurring documents such as acceptance reports or status reports are generated automatically from existing data and only need to be checked.
• Preparing communication: Drafts of replies to customers or of internal reports, approved by a person.

Local AI combines the capabilities of large language models with control over your own data. For companies that work with trade secrets, personal data or regulated processes, it is often the prerequisite for using AI securely and productively at all. It allows what used to be forbidden, and it turns the knowledge in your company into a tool that no competitor can copy. Getting there requires planning around model selection, hardware and operations. The result is an enterprise AI that belongs to you and that you can develop further independently.