LLM assistants or chatbots are very good at connecting the dots, which is exactly why they can be dangerous in multi-team organizations. A PM from team A asks, ‘Why did the churn rate spike last Wednesday?’ The assistant retrieves and displays an answer written by Team B, which includes customer names and contact details. Even if you block the final answer, the leak may have already occurred through retried snippets, intermediate summaries, cached results, etc.
If your retrieval layer isn’t permission-aware end-to-end, the model can pull context from other teams’ documents, tickets, dashboards, or embeddings. This is not just about blocking access. In reality, leaks happen during retrieval, summarization, inside tool traces/logs, or via shared embedding stores.
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