How to Keep Data Private When Using AI

Can you use AI without leaking your data?

Yes — keeping data private while using AI comes down to controlling what you send, where it goes, and who can read it afterwards. The risk is not that AI is inherently insecure; it is that staff paste sensitive information into consumer chatbots, or apps log prompts containing personal data indefinitely. With a few deliberate choices about minimisation, vendor terms, and retention, you get the benefit of AI without the exposure.

The starting point is to stop thinking of "the AI" as one thing. A consumer chat tool, a business API with a no-training agreement, and a self-hosted open model have completely different privacy properties. Matching the tool to the sensitivity of the data is most of the job, and it is a decision you can make once and apply consistently.

Send less in the first place

The cheapest privacy control is data minimisation: do not send what the model does not need. Before a request leaves your systems, strip or mask identifiers that are irrelevant to the task. If you want a model to draft a reply, it rarely needs the customer's full account number, address, and ID — a redacted version usually produces the same quality answer with far less risk.

• Redact or tokenise personal identifiers before the prompt is sent.
• Summarise or extract only the fields the task requires instead of pasting whole records.
• Block sensitive categories — health, financial, credentials — from going to external models at all.

Minimisation has a pleasant side effect: smaller prompts are cheaper and often produce sharper answers, because the model is not distracted by irrelevant detail.

Read the vendor terms like a contract

Where your data goes after it leaves you depends entirely on the provider's terms. Business and enterprise tiers from major providers typically commit not to train on your inputs and to delete data after a defined window — but the consumer tiers of the same products often do not. Check three things: whether your data trains their models, how long they retain it, and where it is processed geographically. Use a paid business agreement for anything that matters, and get a data processing agreement in place. The free tier of a tool is rarely the right home for customer data, however convenient it is.

The question is never "is AI private?" It is "what did we send, under what contract, and how long does it live?" Answer those and privacy becomes a design decision rather than a leak.

Keep the most sensitive workloads in-house

For the data you cannot send anywhere, the answer is to bring the model to the data. Open-weight models you can run on your own infrastructure mean prompts never leave your environment. This costs more in setup and hardware, so reserve it for genuinely sensitive workloads rather than everything. A common pattern is a tiered approach: public models for low-risk drafting, business-tier APIs for internal data under contract, and self-hosted models for regulated or confidential material. Each tier matches a level of sensitivity, so you are neither overspending on routine tasks nor exposing the data that truly cannot leave.

Control access and retention on your side

Privacy does not end at the provider. Your own logs, vector databases, and caches can quietly accumulate sensitive prompts. Set retention limits, encrypt stored data, restrict who can read the logs, and make sure an AI feature respects the same access controls as the rest of your app. A retrieval system that surfaces a document to a user who should not see it is a privacy failure regardless of how good the model is. Treat the data your AI stack stores with the same care you give your primary database, because to an attacker or a regulator it is exactly the same kind of liability.

Prefer it handled for you?

Mapping which data can go where, choosing the right tier of model, and wiring up redaction and retention is exactly the kind of work that is easy to get wrong. Talk to us — talk to BSH Technologies and let our cybersecurity services design a privacy-respecting AI setup that fits your obligations.