Architecture · Technical

Secure Multi-Tenancy for AI Platforms: Isolation, Logging and Cost Attribution

Amestris — Boutique AI & Technology Consultancy

Multi-tenancy is where enterprise AI gets difficult. It’s not just about isolating users—it’s about isolating data flows across prompts, retrieval, logs, caches, and tool access. If any of those layers leak, the platform is not safe.

Start with explicit tenant boundaries

Define what “tenant” means: business unit, customer, region, or regulated entity. Then enforce that boundary consistently across identity, retrieval, logging, caches, and tool integrations.

Don’t forget the “hidden” layers: logs and caches

Most cross-tenant exposures happen through supporting systems:

  • Logs. Ensure prompts are redacted and tenant-tagged (see retention).
  • Caches. Never share caches across tenants; include tenant in cache keys (see caching).

Secure multi-tenancy must be designed into every layer where AI data can flow.

Quick answers

What does this article cover?

How to design multi-tenant AI platforms with strong isolation across data, retrieval indexes, logs, caches, and budgets.

Who is this for?

Platform architects building shared AI capabilities across business units or customers who require strict data separation.

If this topic is relevant to an initiative you are considering, Amestris can provide independent advice or architecture support. Contact hello@amestris.com.au.