Governance Verification Without Clinical Data: A Synthetic Adversarial Framework for Healthcare AI Substrates

Abstract

Current approaches to healthcare AI governance verification assume institutional data access as a prerequisite — requiring research partnerships, IRB approval, and exposure to protected health information before a governance substrate can be validated against real failure modes. This concept note identifies this assumption as a structural bottleneck and proposes a synthetic adversarial verification framework as the missing layer. The framework enables pre-deployment governance verification without clinical data exposure by composing four components: a failure mode library derived from known regulatory and clinical failure classes, a deterministic constraint map linking each failure mode to its governing evaluator and expected evidence output, an adversarial test harness generating synthetic prompts and context envelopes, and an evidence bundle generator producing admissibility-grade audit records. This framework is sector-portable across healthcare and financial services and is complementary to — not competitive with — emerging safety infrastructure initiatives. The proposed framework addresses the verification gap identified by Lakhani (2026) and the accountability infrastructure problem described in FINRA's 2026 Annual Regulatory Oversight Report. This work extends the substrate governance and APR-Lite frameworks previously developed by the author (Soft Armor Labs, 2024–2026).

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