The problem: government can't deploy AI it can't defend
The gap between "the model works in a demo" and "the model is allowed to touch a record of consequence" is almost never about accuracy. It is about trust — and trust, in a government system, is an engineering property you build in, not a slide you add at the end.
Fail-closed AI is a design rule, not a disclaimer. It is the property that turns "impressive" into "deployable."
What trusted-AI evaluation & assurance includes
- AI evaluation & governance aligned to the NIST AI Risk Management Framework (map · measure · manage · govern).
- Confidence-tiered decisions — the model may say "I don't know," and that answer is treated as valid.
- Explainability & replay — every decision reconstructable months later for an auditor or program manager.
- Drift monitoring & fail-closed gating — stale or ambiguous input blocks the action rather than degrading silently.
- Human-in-the-loop oversight — humans stay embedded so AI recommendations are validated and contextualized.
- Auditability by construction — lineage, access, and every automated decision logged as architecture.
NIST AI RMFAI evaluationDrift monitoringExplainabilityHuman-in-the-loopFail-closed
Aligned with where VA and federal AI policy are going
VA has stated plainly that human-in-the-loop oversight is essential — an AI decision can be technically accurate yet unsafe without business context. Federal guidance is incorporating AI security controls into risk-management frameworks (NIST SP 800-53). Our whole approach — fail-closed, human-gated, auditable, evidence-producing — is built for that direction, and for the assurance evidence agencies need to authorize AI-enabled systems.
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