Data can leak through several different channels.
First, input-side leakage. User or company data sent to a third-party model API may be retained, logged, or used for training depending on the provider's terms. So you must check data processing agreements and disable training on your data where applicable.
Second, output-side leakage. The model can echo personal data from the context into responses seen by the wrong person. Reveal one user's data to another through shared context or caching bugs, or emit memorized training data.
Third, retrieval leakage: a RAG pipeline that ignores document permissions will happily summarize files the requesting user should never see.
Fourth, system prompt leakage of any sensitive configuration placed in instructions.
Fifth, logging and observability. Prompt and completion logs are a rich personal data store that needs the same protection as production databases, including retention limits and access controls.
Mitigations map channel by channel.
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