§ Solutions — Regulated Enterprise

Your AI will be examined. Decide what it can say.

Finance, healthcare, and legal teams are deploying AI into decisions that a regulator, an examiner, or opposing counsel will eventually take apart. The organizations in the failure record below all had logs, dashboards, and human sign-off. What they could not do was produce proof.

§ 01

The failure record is already written

From the Decision Failure Atlas — documented incidents, each mapped to the control that would have prevented it. None of these organizations lacked oversight. They lacked evidence.

Legal · Mata v. Avianca

The sanctioned brief

Attorneys filed a brief citing six federal cases that did not exist, generated by an AI assistant and trusted without verification. The court sanctioned them. The control that was missing: a provenance chain requiring every citation to resolve to a real source before the work product leaves the building.

Healthcare · NEDA, 2023

The withdrawn chatbot

A health organization's chatbot dispensed fabricated medical guidance and had to be pulled from service. The control that was missing: an admissibility gate that blocks outputs whose claims cannot be traced to verified sources.

Regulated industry

The 60% audit gap

An organization audited on its AI-assisted decisions could not explain or reproduce sixty percent of them. Outputs were logged — but logging is not evidence, and nothing could be replayed. The result was fines, remediation, and suspended operations.

The common thread: "good enough" governance — logs plus dashboards plus a human signature — fails precisely at the moment it exists for. The full catalog is published in the Decision Failure Atlas.

§ 02

What examination-grade looks like

Summit attaches proof to the decision itself. Every AI-assisted decision in an instrumented workflow becomes reviewable, reproducible, and defensible — properties of the system, not promises in a policy binder.

Prove

Decision Receipts→

A signed, replayable evidence package for every decision: sources considered, hashes verified, policy rules evaluated, verdict recorded. When the examiner asks why, you don't reconstruct the past — you produce it.

Govern

Agent Governance→

As AI moves from drafting to acting, default-deny policy gates what agents can touch, memory carries trust states so it cannot be quietly poisoned, and every run replays deterministically for after-action review.

SUM-RCPT-v1-2c8d…41fbADMIT

decision: credit-adverse-action.117
evidence: 5 sources · hashes verified
policy: 12/12 rules evaluated · 0 violations
reviewer: second-line risk · approved
replay: deterministic · bit-identical
signature: ed25519:4e72…a90c · valid

fig. 1 — the artifact your examiner actually wants.

§ 03

Where it lands first

The pattern is the same in every sector: find the workflow where AI output meets external scrutiny, and instrument that one first.

Sector

Financial services

Credit decisions, suitability assessments, surveillance dispositions, model-assisted research. Examination regimes already require audit trails — receipts make the AI portion of the trail reproducible instead of merely logged.

Sector

Healthcare

Clinical documentation, utilization review, patient-facing guidance. Every claim in an AI-assisted output traces to a verified source, or the admissibility gate holds it for human review.

Sector

Legal & compliance

AI-assisted research, drafting, and investigation work product that opposing counsel or a regulator will probe. The provenance chain is the difference between an efficiency story and a sanctions hearing.

If a specific framework brought you here, see NIST AI RMF, ISO/IEC 42001, or the EU AI Act.

Instrument the workflow your examiner will ask about.

The 10-Day Decision Assurance Pilot receipts every decision in one real workflow and finishes with a written governance findings memo — at least one finding your human review missed, or documentation of why your process is already sound.

Start a Pilot How receipts work