The definition in one sentence
Private AI is artificial intelligence that runs entirely within an environment your organisation controls. The model, the data and the interactions stay under your control. No prompts, documents or answers travel to external AI providers. That makes private AI fundamentally different from public tools like ChatGPT, Gemini or Copilot, where every interaction runs through a shared cloud.
It is the logical choice for organisations that want to apply AI to confidential or regulated data — patient records, financial transactions, legal case files, R&D or strategic plans. And it is the only model that is legally, ethically and operationally sustainable for European knowledge businesses long-term.
Which components make up private AI?
A working private AI stack has four aligned layers. Each layer is independently replaceable, which prevents vendor lock-in and simplifies future upgrades.
- Language model — usually open source (Llama, Mistral, DeepSeek) running locally or in private cloud.
- RAG pipeline — Retrieval-Augmented Generation searches your documents at query time and returns sourced answers.
- Governance layer — role-based access, audit logging, content filters and compliance controls.
- Integrations — connectors to Microsoft 365, SharePoint, CRM, DMS or line-of-business apps.
Private AI vs. private LLM — what's the difference?
A private LLM is the language model — the engine. Private AI is the full car: engine plus retrieval, plus access control, plus dashboard, plus integrations with your existing software. Without those other layers a language model only talks — it doesn't work on your knowledge.
Private AI vs. public AI — the real difference
Public AI services like ChatGPT, Copilot or Gemini are shared cloud services. Every prompt, every uploaded document and every name you mention is processed by the provider on infrastructure you don't oversee. Fine for trivial tasks — untenable once client names, contract values, medical information or IP are involved.
- Public AI: shared infrastructure, US jurisdiction, limited auditability.
- Private AI: dedicated infrastructure, EU hosting, full per-prompt audit trail.
- Public AI: price and policy dictated by the provider.
- Private AI: you choose the model, data, roles and integrations.
- Public AI: data can end up in training corpora.
- Private AI: your data is never shared or used for training.
How does private AI work technically?
Private AI uses Retrieval-Augmented Generation (RAG). Instead of retraining the model on your data — expensive, slow and risky — the system searches your documents at query time. Relevant passages are retrieved, passed to the language model, and used to produce an answer with source citations.
Benefits: your data stays put, you can add or remove documents any time without retraining, and every answer cites its source — auditable and explainable to regulators and auditors.
Who is private AI for?
Private AI fits any organisation working with confidential or regulated data. In practice it's mostly knowledge-intensive organisations — healthcare, finance, legal, accounting, consulting, government, R&D and HR — that switch first. Also technical mid-market with IP or competitive-sensitive data.
Private AI — frequently asked questions
Bronnen en achtergrondinformatie
- EU AI Act — Official Text - European Commission
- Retrieval-Augmented Generation Overview - NIST
