Episode 03Foundation
The 5-Layer Security Architecture for Enterprise AI
Reference ArchitectureDownload PDF
Video coming soon
The 5-Layer Security Architecture for Enterprise AI
Enterprise AI infrastructure spans physical hardware, networking, identity, pipelines, runtime, and monitoring — but there is no widely adopted reference architecture that integrates security across all of these layers specifically for AI workloads.
“I built a complete security architecture for enterprise AI. Five layers. From GPU network segmentation at the bottom to AI-specific SIEM rules at the top. This is the reference architecture I wish existed when I started.”
Architecture Diagrams
Full 5-layer stack diagram with control categories per layer
Network segmentation architecture (3-zone topology)
Identity and access architecture showing role hierarchy
Build Notes
- Deep-dive into all 5 layers with control tables per layer
- Layer 1: Three-zone network topology, VLAN architecture, InfiniBand isolation
- Layer 3: Dependency scanning, model signing with Sigstore, container hardening
- Layer 5: AI-specific SIEM integration, model drift monitoring, IR playbooks
Lessons Learned
- The biggest mistake is trying to secure AI infrastructure with only perimeter controls
- Identity architecture for AI systems is fundamentally different — models, datasets, and pipelines are first-class principals
- Container security for ML workloads requires different base images and scanning profiles than traditional microservices
- AI-specific SIEM rules don’t exist out of the box — you have to build them
Discussion
Which of these 5 layers does your organization have the biggest gap in today? I’d guess monitoring and detection — most teams are blind to AI-specific threats at the runtime level.