How to Interact with AI Models Without Exposing Sensitive Data
Conversations with public AI models such as ChatGPT, Gemini, Claude and GigaChat are scanned by automated filters and may be reviewed manually by employees or contractors of the model providers. Corporate administrators and, in some cases, law-enforcement agencies can also access chat histories.
To reduce future exposure, users should first disable the option that allows the model to learn from their dialogues. In OpenAI settings this is done by turning off “Improve the model for everyone.” Google Gemini requires disabling chat history so that conversations are automatically deleted after 72 hours. Similar toggles exist for DeepSeek and Anthropic Claude, while Sber GigaChat offers no such control in its consumer interface.
Additional hygiene measures include replacing real names, phone numbers and other identifiers with placeholders such as {name} or {phone}. When large volumes of data must be processed, tools like Redacto or Гарда Маскирование can automate masking, although results still require manual verification.
Long-running chats accumulate contextual information; therefore old conversations should be deleted and new tasks started in fresh sessions. Custom GPTs or Gemini Gems can store persistent instructions so that behavior does not need to be re-explained each time.
Models downloaded from Hugging Face should be scanned for malicious payloads with utilities such as HiddenLayer Model Scanner. Prompt-injection risks can be mitigated by pasting any third-party prompt into a word processor and applying a uniform text color to reveal hidden commands.
The most private approach is to run models locally with Ollama. After installation, a small model such as llama3.2:1b can be pulled and executed entirely on the user’s hardware. Larger production models like qwen3-coder:30b require at least 40 GB of RAM. Running the service inside a Docker container further isolates it from the host system.
For hybrid use, the open-source client ChatBox can connect to both local Ollama instances and paid API endpoints such as Cloud.ru Evolution Foundation Models. Users paste their API key, select compatible models, configure embedding and reranker components for RAG, and upload documents to a knowledge base that the model consults during generation.
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