Design a secure, privacy-aware multi-tenant AI assistant.

Combines threat modeling, injection containment, data minimization, tenant isolation, tools, audit, incident response, and governance.

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2CompareCompare with Core answer

3RefineRefine with Stronger and Advanced

TL;DR

I authenticate the user, derive tenant and resource scope from trusted application identity, retrieve only authorized minimal context, and label external content as untrusted. The model can suggest typed operations, but trusted services validate arguments, authorize resources, enforce budgets, and require approval for consequential actions. Outputs are validated for their destination, and success is verified from authoritative state.

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Practice focus

End-to-end AI security architecture, privacy lifecycle, governance, operations, and proportional user experience.

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Answer path

Core answer first, then checkpoints toward a stronger answer.

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Core answer
Start here. This is the direct spoken answer to practice first.
Overview
A secure assistant is a constrained application architecture around a model, not a promise that the model cannot be manipulated.
I authenticate the user, derive tenant and resource scope from trusted application identity, retrieve only authorized minimal context, and label external content as untrusted. The model can suggest typed operations, but trusted services validate arguments, authorize resources, enforce budgets, and require approval for consequential actions. Outputs are validated for their destination, and success is verified from authoritative state.
Checkpoint
Stronger answer
Optional upgrade with examples, trade-offs, and sharper phrasing.
The design separates sensitive sources from unrestricted sinks, keeps credentials in narrow executors, versions prompts and dependencies, and records provenance through retrieval and tools. Retention and deletion cover provider state, files, embeddings, caches, traces, and evaluations. Content safety, prompt-injection detection, egress restrictions, rate limits, tenant-aware caches, and human escalation provide layered containment rather than one universal filter.

Review signals

Use these to catch weak phrasing, shallow reasoning, and risky claims.

Common mistakesCandidateCommon mistakesMistakes that weaken the answer or signal shallow understanding.Open details

Details

Relying on the system prompt to preserve tenant boundaries.
Sending complete customer records for convenience.
Launching broad tool access before adversarial evaluation.

Design a secure, privacy-aware multi-tenant AI assistant.

Practice focus

Answer path

Overview

Review signals

Follow-up prompts

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