Security & Governance

Zero-Trust Architecture for AI Workloads: A 2026 Reference Design

4 min read

Zero-trust around AI workloads matters more than around traditional services. The blast radius is bigger and the threat surface includes prompts, training data, and model artifacts.

The Reference Design

  1. 01Identity-aware access at the model boundary

    OAuth + per-user rate limiting; never shared API keys.

  2. 02Prompt injection defenses (input sanitization)

    Strip system-prompt-like sequences from user input.

  3. 03Output filtering for PII and prompt leakage

    Regex + LLM-based filter on every response.

  4. 04Per-tenant network isolation for self-hosted models

    EKS namespaces + NetworkPolicy.

  5. 05Immutable audit log for every prompt + response

    S3 Object Lock or equivalent.

Production-Grade AI Agent ArchitectureThree layers that keep enterprise agents reliableInputStructured payloadLAYER 1Deterministic BoundarySchema-bounded LLM callLAYER 2Validation GateSchema · Range · Cross-refPASS→ Final actionFAIL→ Human reviewLAYER 3 · Audit TrailEvery decision logged: input → prompt → output → action
The 3-layer architecture pattern Ohveda uses to ship reliable, auditable enterprise AI agents to production.

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