Zero Trust for AI Execution
Definition
A security model that applies zero trust principles to AI agent execution. No agent action is trusted by default — every tool call, database write, and API request must be verified through a deterministic policy engine before execution. Inspired by zero trust networking, where no device or user is trusted by default.
Why It Matters
As AI agents gain tool-use capabilities, they can modify production systems — databases, APIs, billing records. Without zero trust enforcement, a single hallucinated function call can cause data loss, unauthorized access, or regulatory violations. The gap between AI reasoning and tool execution is where catastrophic failures occur.
How Exogram Addresses This
Exogram implements Zero Trust for AI Execution through 8 deterministic policy rules evaluated in 0.07ms with zero LLM inference. Every agent action passes through the execution boundary before reaching production systems. Same input → same output → every time.
Related Terms
Key Takeaways
- → Zero Trust for AI means no agent action is trusted by default — all must be verified
- → Governance happens at the execution boundary, between reasoning and tool use
- → Deterministic enforcement (code) is fundamentally different from probabilistic validation (LLM)
- → 0.07ms evaluation latency means governance doesn't bottleneck agent performance
Comparison
| Approach | Mechanism | Error Rate | Speed |
|---|---|---|---|
| Zero Trust (Exogram) | Deterministic code gates | 0% | 0.07ms |
| LLM-as-Judge | Probabilistic inference | 1-10% | 50-200ms |
| Content Filtering | Pattern matching on output | Varies | 5-50ms |
| No Governance | Direct execution | N/A | 0ms |
Governance Checklist
0/6 — VulnerableQuick Assessment
1. What does "Zero Trust for AI" verify?
2. What happens when Exogram encounters an error during evaluation?