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Core
Multi-Tenancy and Isolation
When one AI system serves many customers (tenants), the cardinal rule is that no tenant can see another's data, ever. In RAG this means every retrieval is filtered by tenant so the vector search cannot return another tenant's documents; it extends to caches, logs, fine-tunes, and rate limits. The dangerous failure is a cross-tenant leak. Applied-AI interviews probe it because enterprise deployments are multi-tenant, and a leak between customers is a catastrophic, trust-destroying breach.
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RELATED CONCEPTS
PRACTICE THIS IN REAL QUESTIONS
RAG & Agent System DesignDesign multi-tenancy and access control for a RAG system serving many enterprise customers.→AI Security, Privacy & GovernanceYour RAG system can surface documents a user shouldn't see. How do you enforce authorization in retrieval?→RAG & Agent System DesignHow do you let an AI agent execute code safely (sandboxing)?→AI Security, Privacy & GovernanceWhat is indirect prompt injection, and why is it so dangerous for RAG and agents?→AI Security, Privacy & GovernanceDesign data isolation for a multi-tenant AI SaaS so one customer's data can never leak to another.→AI Security, Privacy & GovernanceThreat-model an LLM application from scratch. What is the attack surface and how do you reason about it?→
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