Aurimas Griciūnas

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𝗧𝗲𝗻𝗮𝗻𝘁 𝗜𝘀𝗼𝗹𝗮𝘁𝗶𝗼𝗻 in 𝗔𝗶 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 is not just a checkbox, it is a fundamental architecture requirement. Here is why 👇

When you move from a local RAG prototype to a production SaaS, the way you handle data retrieval changes completely. You are no longer just querying a database; you are managing trust.

𝟭. 𝗟𝗼𝗴𝗶𝗰𝗮𝗹 𝗦𝗲𝗽𝗮𝗿𝗮𝘁𝗶𝗼𝗻: Ensuring that the LLM context window never contains data from two different customers simultaneously.

𝟮. 𝗩𝗲𝗰𝘁𝗼𝗿 𝗗𝗕 𝗜𝗻𝗱𝗲𝘅𝗶𝗻𝗴: Using metadata filtering to restrict ANN lookups to specific tenant IDs.

𝟭. The user query arrives with a JWT containing the 𝘁𝗲𝗻𝗮𝗻𝘁_𝗶𝗱.

𝟮. The embedding is generated for the query.

✅ Metadata filters are applied at the database level so the search space is restricted before the first vector is compared.

𝟯. The retrieved context is passed to the LLM with a system prompt that reinforces the boundary.

❗️ The mistake most engineers make is relying on the LLM to "ignore" other tenant data. Models are not security guards; your infrastructure is.

ℹ️ Using tools like 𝗔𝗪𝗦 𝗜𝗔𝗠 or 𝗞𝘂𝗯𝗲𝗿𝗻𝗲𝘁𝗲𝘀 𝗡𝗮𝗺𝗲𝘀𝗽𝗮𝗰𝗲𝘀 can provide the physical isolation layers needed for high-compliance industries.

Are you building multi-tenant AI apps? What has been your biggest challenge with data leakage? Let me know in the comments 👇

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