Security Architecture for AI
Overview
Secure AI architecture applies defense-in-depth principles to the entire ML lifecycle — from data ingestion through model serving. Traditional security architecture (network segmentation, access control, monitoring) still applies, but AI adds new components that need specific controls.
Architecture Layers
| Layer | Components | Key Controls |
|---|---|---|
| Data | Training data, fine-tuning data, RAG knowledge base, vector DB | Encryption, access control, provenance, quality gates |
| Model | Weights, configuration, system prompts, adapters | Signing, versioning, integrity verification, access control |
| Compute | GPU clusters, inference servers, training infrastructure | Network segmentation, resource limits, monitoring |
| Application | API gateway, input/output filters, tool integrations | Authentication, rate limiting, filtering, logging |
| User | Developers, end users, administrators | RBAC, MFA, audit trails, training |