Penumbra: Sample-Efficient Adversarial Search for Regulatory Obligations
Penumbra uses adversarial search to find regulatory obligation violations by AI agents that leave no lexical trace — targeting omissions in finance, healthcare, and legal workflows.
Summary written by editorial AI · Source link below
arXiv:2610.04693v3 Announce Type: replace-cross Abstract: Agents are entering finance, healthcare and law, sectors where a violation leaves no lexical signature and carries real penalties. Whether an omission is material, or a disclosure sufficient, depends on what the response left out. Probing such an obligation means finding responses one minimal edit from flipping compliance, and every probe costs a generation and two adjudications, so the binding constraint on regulatory red-teaming is sam
Editorial Analysis
AI agents in regulated industries can violate obligations through omission rather than action, and standard monitoring won't catch it — adversarial probing is needed to surface these risks before deployment.
Add adversarial regulatory-obligation testing to your AI agent deployment checklist for finance, healthcare, and legal use cases.
AI agents can violate regulations by omission with no visible trace — targeted adversarial testing is needed before deploying agents in regulated sectors.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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