# Using AI in a Privacy-Friendly Way in Daily Work [Skip to content](#lm-inhoud)Network/[NL](/en/privacyvriendelijk-ai)EN[Hubhub.llmnet.nlCompare models on task, language, cost and license.](https://hub.llmnet.nl/en/)[Communitycommunity.llmnet.nlPrompt techniques, patterns and system prompts.](https://community.llmnet.nl/en/)[APIapi.llmnet.nlLLMs in production: rate limits, routing, structured output.](https://api.llmnet.nl/en/)[Consultancyconsultancy.llmnet.nlRolling out AI in an organization, pilot to production.](https://consultancy.llmnet.nl/en/)[Newsnieuws.llmnet.nlAI developments, explained for the Netherlands.](https://nieuws.llmnet.nl/en/)[Benchmarkbenchmark.llmnet.nlMeasure AI quality yourself, on your own tasks.](https://benchmark.llmnet.nl/en/)[Careersvacatures.llmnet.nlAI roles, salaries and career paths in the Netherlands.](https://vacatures.llmnet.nl/en/)[Learnleren.llmnet.nlAI concepts in plain language, beginner to builder.](https://leren.llmnet.nl/en/)[Guidegids.llmnet.nlRun AI privately on your own Mac, PC, NAS or home server.](https://gids.llmnet.nl/en/)[Directorydirectory.llmnet.nlMapping the AI ecosystem: tools, models, companies.](https://directory.llmnet.nl/en/)[Radarradar.llmnet.nlSignals from X, research and communities for indie developers.](https://radar.llmnet.nl/en/)[Appsapps.llmnet.nlReviews of AI apps and open-source repos, with tips for builders.](https://apps.llmnet.nl/en/)[llmnet.nl — main site](https://llmnet.nl/en/)[](https://x.com/intent/post?url=https%3A%2F%2Fgids.llmnet.nl%2Fen%2Fprivacyvriendelijk-ai&text=Using%20AI%20in%20a%20Privacy-Friendly%20Way%20in%20Daily%20Work)[](https://www.linkedin.com/sharing/share-offsite/?url=https%3A%2F%2Fgids.llmnet.nl%2Fen%2Fprivacyvriendelijk-ai)[](https://www.reddit.com/submit?url=https%3A%2F%2Fgids.llmnet.nl%2Fen%2Fprivacyvriendelijk-ai&title=Using%20AI%20in%20a%20Privacy-Friendly%20Way%20in%20Daily%20Work)[](#)[](https://x.com/intent/post?url=https%3A%2F%2Fgids.llmnet.nl%2Fen%2Fprivacyvriendelijk-ai&text=Using%20AI%20in%20a%20Privacy-Friendly%20Way%20in%20Daily%20Work)[](https://www.linkedin.com/sharing/share-offsite/?url=https%3A%2F%2Fgids.llmnet.nl%2Fen%2Fprivacyvriendelijk-ai)[](https://www.reddit.com/submit?url=https%3A%2F%2Fgids.llmnet.nl%2Fen%2Fprivacyvriendelijk-ai&title=Using%20AI%20in%20a%20Privacy-Friendly%20Way%20in%20Daily%20Work)[](#)By Ivo Donker — created with AI assistance (Claude & Gemini) · Last updated: July 27, 2026 # Using AI in a Privacy-Friendly Way A practical guide for safe AI use in the workplace The deployment of Large Language Models (LLMs) offers huge efficiency benefits. However, the way these models process data often poses a risk. How do you ensure you benefit from the power of AI without violating the privacy of your customers or your trade secrets? This guide will help you get started. ## 1. What data should you share and what should you not? The golden rule is simple: treat public AI chatbots as if you were talking to a random passerby on the street. Data that ends up in the cloud can theoretically be used to train the model further, meaning your data could end up with competitors or hackers. ### Never enter into public models: - PII (Personally Identifiable Information): Names, national identification numbers, medical records, or customer email addresses. - Trade secrets: Unannounced products, quarterly financial figures, or strategic plans. - Security data: API keys, raw passwords, or internal server architectures. Safe to share: Having generic marketing texts edited, explaining programming concepts, or structuring anonymized datasets (from which all identifiable factors have been removed). ## 2. The Trade-off: Local vs. Cloud The AI landscape can be roughly divided into two storage/processing methods. Your choice here determines your privacy standard. ### Cloud (Public models) Think of ChatGPT, Claude, or Gemini (via the web interface). You have fast, smart models at your disposal, but data processing takes place on the AI company's servers. Easily accessible, but with a higher privacy risk unless strict Enterprise contracts have been signed. ### Local (On-Premises) By running models (such as Llama 3 or Mistral) on your own hardware or local network, the data never leaves your premises. You don't pay per token, and there are no 'Terms of Service' reading along. However, it does require a suitable computer (often with a powerful graphics card). ## 3. Essential Settings in the Cloud Are you using cloud solutions anyway? Then adjust your settings immediately. Most providers now offer the option to keep your data out of the 'training loop'. - Turn off chat history: In systems like ChatGPT, you can disable the 'Chat history & training' option in the Settings > Data Controls menu. - Use the API: Almost all major LLM providers state in their API terms that data submitted via the API is not used for training. Therefore, consider using a frontend that works via the API instead of the consumer website. ## 4. Free Privacy-Conscious Tools by Name Want to get started more securely right away? Consider these specific and free-to-use tools that put privacy first: - Ollama: An open-source tool that allows you to run powerful AI models entirely locally (on your own Mac, Windows, or Linux machine) with a single command. Absolute privacy. - LM Studio: Just like Ollama, but with a visual and user-friendly interface. You download models, run them locally, and chat with them without an internet connection. - DuckDuckGo AI Chat: Provides access to popular models but acts as an anonymous shield. DuckDuckGo guarantees that chats are not logged and are not used for training. Do you need help setting up a secure and local AI infrastructure at the office? [Discover our AI Consultancy & Implementation services →](https://consultancy.llmnet.nl/en/) © 2026 LLMnet - The Local LLM Guide for the Netherlands. Built for speed and privacy.