AI & analytics
AI on your company’s real data: where to start
How to connect AI to your company’s real data — with permissions, context and privacy — to query in natural language and generate real value.
Read articleKey takeaways
- Applying LLMs to private data lets you query company information in natural language.
- Data governance and access control are essential.
- Techniques like RAG avoid retraining the model with sensitive data.
- The biggest risk is exposing data to those who should not see it.
- An LLM is only as secure as the data layer feeding it.
Language models (LLMs) promise to transform access to information: ask the company in natural language and get answers instantly. But applying them to private data without the right caveats is a source of security and privacy risks worth understanding first.
What it means
Applying an LLM to private data means connecting a language model to internal information to query it in natural language, usually via techniques like RAG that retrieve the data at answer time.
The risks to control
Data leaks
Reveal towrong user
Hallucinations
False answersas facts
Privacy
No legalbasis
Traceability
Unknownsource
- Data leaks: the model revealing information to unauthorised users.
- Invented answers: "hallucinations" presented as facts.
- Privacy: processing personal data without a legal basis.
- Traceability: not being able to explain where an answer comes from.
How to do it securely
The secure approach combines several practices: use RAG so the model relies on retrieved, citable data instead of retraining on sensitive information; apply access control so each user only gets answers on permitted data; and log queries for audit. The AI Act and GDPR frame these obligations.
The role of data governance
An LLM on private data is only as secure as the data layer feeding it. If data is well governed — permissions, quality, traceability — the model inherits those guarantees; if it is in disorder, the LLM amplifies the risk.
An LLM on private data is only as secure as the data layer feeding it.
In summary
Applying LLMs to private data enables natural-language queries but demands caveats: control leaks, hallucinations, privacy and traceability. The secure approach combines RAG (no retraining on sensitive data), per-user access control and query logging — all resting on a governed data layer.
Sources & further reading
Frequently asked questions
Is it safe to use an LLM with my company’s data?
It is if you apply data governance, access control and techniques like RAG. Without those caveats, there is a risk of leaks and unreliable answers.
Do I have to retrain the model with my data?
Not necessarily. RAG lets the model rely on data retrieved at answer time, without retraining it on sensitive information.
How do I stop someone seeing data they should not?
By applying access control so each LLM query only reaches data that user is authorised to see.
What is the biggest risk of an LLM on private data?
Exposing data to those who should not see it. That is why access control and data governance are essential before deploying.
What are hallucinations?
Plausible but false answers a model presents as facts. RAG reduces them by anchoring answers in real, citable data.
What regulation frames its use?
The EU AI Act and the GDPR: the first governs AI systems by risk; the second, the processing of any personal data the LLM handles.
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