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[>_] ROOM · Cybersecurity
Where Trust Boundaries Exist in AI Systems
Map the critical trust boundaries in AI architectures. Learn where untrusted data crosses into trusted systems and how to secure these seams.
Room tasks
1What is a Trust Boundary and Why It Matters
2User Input as an Untrusted Boundary
3Retrieved Content (RAG, Web, Documents) as an Untrusted Boundary
4Tool/Function Outputs as an Untrusted Boundary
Frequently Asked Questions
What is a trust boundary in an AI system?
It is a line where data moves between components with different trust levels, such as user input entering a model or model output reaching a database. Attacks often cross these lines.
Why do trust boundaries matter for LLM apps?
LLMs process untrusted text alongside trusted instructions, so the boundaries around input, retrieval, tools and output are exactly where validation and least privilege must be applied.
Can zero trust principles apply to AI?
Yes. Treating every input, retrieved document and model output as untrusted until verified is a zero trust approach. This beginner room is free on Codelivly and shows where to apply it.