What is private artificial intelligence and how does it work?
Private artificial intelligence means using AI models within infrastructure controlled by the company. Instead of processing queries exclusively through public services, these models can run on the company's own servers, in dedicated data centres or in private cloud environments. This way, the organisation can set its own operating conditions and adapt the tools to its needs.
For example, a manufacturing company can use private AI to look up technical manuals, production procedures or documentation about its products. Its employees can ask questions and receive answers based on those documents without having to search manually through hundreds of files. If the system is configured correctly, that information can be processed without sending it to external artificial intelligence providers.
There is no need to build a model from scratch. There are solutions that can be installed and connected to the company's applications. This makes it possible to create tools adapted to different departments and to make use of the knowledge the organisation already has, without forcing staff to keep switching between applications.
What happens when we use ChatGPT, Claude or Gemini with business data?
ChatGPT, from OpenAI; Claude, from Anthropic; Gemini, from Google; and Microsoft Copilot are tools for drafting documents, analysing information and answering questions. Their ease of use has led many professionals to bring them into their daily work. However, when they are used with business information, it is important to know what data is being shared with the service provider.
Imagine a sales manager pasting a proposal with confidential prices into ChatGPT, or someone in the legal department using Claude to review a contract. In both cases, the information is sent to systems managed by third parties. That is a risk when the information is confidential.
In August 2026 the Spanish Data Protection Agency (AEPD) warned that the prompts entered into generative AI tools can contain personal data, trade secrets or sensitive documentation. Even a short query can reveal customer names, financial figures or information about internal projects.
These platforms also offer business versions with specific privacy measures. For example, OpenAI states that data from its business products is not used by default to train its models. Even so, the terms vary depending on the product, so it is worth reviewing them before bringing these tools into the company's processes.
Private AI versus external platforms: what are the differences?
The most important difference lies in how the service is delivered. ChatGPT, Claude, Gemini and Copilot run on platforms managed by their providers, whereas a private AI solution can run on the company's own or dedicated infrastructure. This affects the options for configuration, maintenance and integration with business systems.
Commercial platforms give access to advanced models without having to install servers or manage how they run. A private solution, on the other hand, requires suitable infrastructure and administration, but allows the system to be adapted to specific needs. For example, an organisation can choose a model specialised in analysing technical documentation or in working with particular languages.
There are also differences in technological continuity. When a business application depends on a single provider, changes to its services can affect how the tools work. An architecture that supports different models makes it easier to bring in alternatives, although it also requires technical planning.
The CNIL, the French data protection authority, recommends giving particular consideration to solutions installed in the organisation's own environments when working with personal data or strategic documentation. However, not every company needs fully private infrastructure. In many cases, combining private models with external services can be the right option.
What can private artificial intelligence be used for in a company?
One of its most useful applications is making internal documentation easier to consult. Many organisations store information across different folders, servers and applications, which makes it hard to find answers quickly. An AI assistant connected to those sources can let employees ask questions and get information based on authorised documents.
This capability can be built with technologies such as RAG, which search documents for information before generating an answer. A company can therefore create assistants that consult its manuals, procedures or knowledge bases without having to train a new model on all of its documentation.
Applications vary by department. In production, AI can help locate maintenance instructions or technical specifications. In sales, it can make it easier to consult catalogues and terms of sale. In human resources, it can answer questions about internal procedures, while in administration it can help organise and analyse documents.
Another option is to integrate AI with business tools to automate certain activities, such as classifying requests, preparing draft reports or retrieving information from different systems. These functions should be configured to the needs of each process and reviewed when they may affect important decisions.
What should a company consider before adopting private AI?
The first step is to identify the problem to be solved. Not every task requires a private model, and not every company needs the same infrastructure. Before investing, it is worth defining what information the tool will use, how many people will work with it and what results are expected.
It is also important to assess the technical requirements. Some models can run on relatively modest servers, while others require specialised hardware. The choice should take into account performance, maintenance costs, ease of updating and the ability to scale the system as usage grows.
Security remains a fundamental part of the project. The General Data Protection Regulation (GDPR) requires appropriate measures whenever personal data is processed. Private AI does not by itself guarantee regulatory compliance: permissions also have to be managed, systems protected and procedures established for using information correctly.
Finally, it is advisable to start with a specific project and evaluate how it works before scaling it up. An assistant for consulting internal documentation can be used to check the quality of the answers, identify improvements and learn how employees use the tool. That experience makes it possible to decide on future applications with better judgement.
LurNova: private artificial intelligence solutions for businesses
At LurNova we help companies bring artificial intelligence into their work processes, combining technology, security and an understanding of their needs. Our principle, "If artificial intelligence isn't secure, it isn't intelligent", is part of our approach to developing business solutions that can be used responsibly.
With LurNova MIA, we offer a corporate artificial intelligence platform for creating specialised assistants, consulting internal documentation and making the organisation's knowledge easier to access. The platform includes management of users, groups and permissions, as well as features that show the sources used to generate certain answers.
LurNova AI Model Hub, for its part, makes it possible to manage different artificial intelligence models from a single environment. This makes it easier to work with private models and external services according to the needs of each application, with built-in oversight and filtering mechanisms. We also offer private model deployment for companies that need to run artificial intelligence on specific infrastructure.
From the Basque Country, we support organisations that want to move from individual use of tools such as ChatGPT or Copilot to AI that is integrated into their business processes. Our goal is for every company to have a solution suited to its activity, without taking on costs or complexity it does not need.
Private artificial intelligence is not meant to replace every external tool, but to offer an alternative when the information or the business needs call for a different environment. Choosing the right technology, defining its applications and setting an adoption strategy are the steps that turn AI into a genuinely useful tool for the company.
CEO of LurNova, with extensive experience in technology and digital transformation. Involved in developing technology projects and applying artificial intelligence in organisations, he combines technical knowledge with business vision.
