AI in Zero Trust Environments

Zero Trust Principles Applied to AI

Never Trust, Always Verify

Traditional ZT	AI Application
Don't trust the network	Don't trust the model's input — validate everything
Don't trust the user	Don't trust the user's prompt — filter for injection
Don't trust the device	Don't trust external data sources — verify RAG content
Verify continuously	Monitor model behavior continuously, not just at deployment

Least Privilege

Models access only the data they need for the current request
Tool permissions scoped to minimum required capabilities
API keys scoped to specific models and operations
User access to AI features based on role

Assume Breach

Design for the scenario where the model has been compromised via injection
Output filters operate independently from the model
Monitor for data exfiltration even from "trusted" AI components
Segment AI infrastructure from crown jewel systems

Microsegmentation for AI

[User] ←→ [API Gateway + Auth]
              ↓
[Input Filter] ←→ [Injection Detection Service]
              ↓
[Model Inference] ←→ [Tool Sandbox (isolated)]
              ↓                    ↓
[Output Filter]          [External APIs (restricted)]
              ↓
[Response to User]

Each component runs in its own trust boundary. The model can't directly access external APIs — tool calls go through a sandboxed intermediary. The output filter is separate from the model and can't be bypassed via prompt injection.

Practical Implementation

Deploy input and output filters as separate microservices
Use service mesh for mTLS between AI pipeline components
Implement per-request authorization for tool use
Network-level isolation between AI inference and data stores
Separate credentials for AI services vs. human access

AI Security Book