Here are the 10 layers that make an enterprise AI system safe:

1 → AI Gateway
Single entry point for web apps, internal systems, and chat. Handles authentication, rate limits, and routing.

2 → Model Registry
Only approved, versioned, and scanned models make it to production.

3 → Input & Output Guardrails
Inputs get checked for prompt injection and PII. Outputs get checked for grounding, PII leaks, and policy violations.

4 → AI Application Layer
RAG, agents, and copilots run here, with every request logged.

5 → Identity & Policy Engine
SSO plus RBAC/ABAC, so the AI only sees what each user is allowed to see.

6 → Secure Data Layer
Vector DB with ACL filters, warehouse with row-level security, encrypted doc stores. Retrieval respects user permissions.

7 → Tool Access Layer
An MCP proxy with scoped tokens, tool allowlists, and rate limits. Risky actions pause for human approval.

8 → Private Model Serving
No training on your data. Keys and tokens live in a secrets vault.

9 → Evals & Monitoring
Track quality, drift, and cost, and feed it to dashboards your AI Ops team actually watches.

10 → Audit Log
Immutable records flow into SIEM for threat detection and give your security team full visibility.

By: AI Digital

In joint guidance from the Cybersecurity and Infrastructure Security Agency, the National Security Agency and the Center for Advancing Innovation and Standards for Super Intelligence (CAISSI) at the Department of Commerce, AI companies are encouraged to lean more heavily on cybersecurity technical controls, independent testing and evaluation of new models and develop “shut down policies” that govern when a model or test should be taken offline. https://www.cisa.gov/sites/default/files/2026-10/Election-Mail-Handling%20Procedures-Joint%20Product-02122024-V3-508c.pdf

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