The New Architecture of AI Decision Defensibility

An Executive Brief for Medical Governance Boards and Risk Underwriters

Executive Summary
As medical AI moves from experimental pilots to enterprise oversight, the industry is grappling with a quiet but fundamental tension. The primary concern for health systems in 2026 is no longer just model accuracy; it is decision defensibility.

Most current platforms rely on conversational scripts (system prompts) to guide behavior. However, under the scrutiny of a regulatory audit or a multi-million dollar liability claim, a "script" is not a control. It is an unenforceable suggestion. This paper identifies AI Interaction Integrity as the core requirement for medical governance. By moving safety from the "prompt" to a deterministic safety substrate, organizations can establish the Named Decision Authority and Contemporaneous Evidence required for defensible AI operations.

In a CHAI/RUAIH and NIST AI RMF world, failure to prove who owned a decision and under which controls, will be treated as a governance failure, not a technical bug.

The 2026 Governance Landscape: CHAI, NIST, and SaMD

In the current regulatory environment, "experimentation" has been replaced by "enterprise oversight." To establish instant credibility with a CMIO or Risk Lead, governance must align with three non-negotiable frameworks:

• CHAI (Coalition for Health AI): The gold standard for healthcare. Following the Responsible Use of AI in Healthcare (RUAIH) framework, developed in partnership with The Joint Commission, is now a baseline expectation for hospital accreditation. RUAIH sets expectations around governance, monitoring, bias, and documentation that most health systems are now treating as a de‑facto accreditation checklist.
• NIST AI RMF: The federal benchmark for risk management. Governance must move through the four core functions: Govern, Map, Measure, and Manage. A safety substrate operationalizes Govern/Measure/Manage by giving boards a single place to assign authority, monitor drift, and enforce incident response
• FDA SaMD (Software as a Medical Device): For high-stakes AI, "medical-grade" validation is required. While low-risk administrative tools face lighter oversight, this actually increases a hospital’s internal responsibility to govern them, creating a massive need for a universal safety layer. FDA’s latest CDS guidance stresses transparency and independent review to reduce automation bias, which is impossible if you cannot reconstruct how a recommendation was generated.

Pillar 1: Architectural Enforcement (Model-Agnostic Safety)

The primary question for any AI Safety Committee is: "Where does the buck stop?" Underwriters don’t price ‘vibes’, they price deterministic controls. A substrate is that control layer.

In a script-based system, safety lives inside the prompt. If the prompt is bypassed or the model drifts, safety fails. In a Substrate-Based Architecture, safety decisions are moved to an external, deterministic layer.

• Deterministic vs. Probabilistic: Unlike probabilistic LLMs, a safety substrate uses rule-based arbitration. If a system presents an ambiguous signal, the substrate enforces a safety-first outcome every time.
• The Hard Safety Layer: Critical actions—such as HIPAA-aligned PII redaction or mandatory clinical escalation—are enforced as non-bypassable "deadman switches." They are hard-coded into the operating model and cannot be modified by the conversational agent.

Pillar 2: Forensic Reconstruction and the AI Alibi

Underwriters and claims adjusters know that decisions don't disappear after execution. They resurface months or years later during audits, litigation, or regulatory reviews. At that point, the question is rarely "What did the model predict?" but rather: Who held decision authority, what influenced the action, and what contemporaneous evidence exists?

Think of this as a tamper‑resistant ‘flight data recorder’ for each AI‑assisted interaction.

Capturing Evidence at the Moment of Action

A substrate architecture produces an immutable SASIEnvelope for every interaction. This provides the "AI Alibi" through:

• Action Rationale: A stable, machine-readable ID for every safety intervention (e.g., clinical_escalation_triggered).
• Policy Pinning: An immutable hash of the exact configuration and thresholds active during that turn. This proves to auditors that the system was operating under an approved governance protocol at the moment of action.

Pillar 3: Interaction Integrity and Disclosure Resistance

Safety committees must ensure that AI systems do not "narrate" their own design. Prompt-embedded systems often leak their own safety logic when probed, revealing vulnerabilities to anyone looking to bypass them.

• The Disclosure Guard: A substrate uses a pre-LLM interceptor to detect meta-questions about architecture. Instead of the AI "guessing" how to explain its filters, the substrate routes the probe to an approved, non-revealing template.
• Algorithmic Drift and Bias: Governance boards now require evidence that the AI performs equitably across races, ages, and genders. A substrate provides the continuous, turn-by-turn monitoring needed to detect drift and bias before they become a liability.

Pillar 4: Operating Model Redesign (Named Ownership)

The shift from scripts to substrates is more than a technical upgrade; it is an operating model redesign. Most hospitals have playbooks for sentinel events and data breaches, but almost none have an AI sentinel‑event playbook.

• Named Decision Ownership: By decoupling the conversational agent (the LLM) from the safety authority (the substrate), organizations can explicitly define intervention rights.
• Incident Response: Most hospitals have a plan for a data breach, but few have an AI Incident Response Plan. A substrate provides the "kill switch" needed to lock a model and the audit trail needed to notify affected patients if an AI misinterprets a result.

Conclusion: The Path to Underwritable AI

For medical governance boards and insurers, the standard of care has shifted from subjective trust to objective verification. A therapeutic sounding script is not a substitute for a deterministic safety architecture.

Organizations must demand a Safety Substrate that provides the contemporaneous evidence and clear decision authority required to defend AI-influenced actions. Without these forensic primitives, AI remains an unmanageable liability.

If you cannot answer ‘yes’ to at least eight of the ten questions in the SASI Governance Maturity Diagnostic, your AI program is likely under‑writable only on paper.

SASI AI Governance Maturity Diagnostic (10-Question Tool)

Boards and carriers can use the following ten questions as a quick underwriting and readiness screen for any vendor claiming ‘medical‑grade AI.

Phase 1: Governance & Framework Alignment

1. Framework Adherence: Does your current AI deployment follow a recognized clinical framework, such as CHAI (Coalition for Health AI) or the NIST AI RMF, to ensure accreditation readiness with The Joint Commission?

2. Named Decision Authority: In your current AI vendor contracts, is it explicitly documented who holds the "Decision Authority" at the moment of a clinical recommendation? Is this authority human-in-the-loop by policy or by technical enforcement?

Phase 2: Architectural Integrity & Enforcement

3. Model-Agnostic Stability: If your underlying LLM vendor (e.g., OpenAI or Microsoft) pushes a silent model update tonight, do you have an external safety layer to prevent "silent regression" or changes in safety behavior?

4. Hard Safety Guardrails: Are your "Safety Tier" requirements (e.g., PII redaction, crisis escalation) enforced through a deterministic substrate, or are they dependent on the conversational "prompt" which can be bypassed via injection or drift?

Phase 3: Forensic Reconstruction (The "AI Alibi")

5. Contemporaneous Evidence: If a patient dispute arises regarding an AI interaction from six months ago, can you produce a "Forensic Snapshot" showing exactly which safety configuration and thresholds were active at that specific second?

6. Action Rationale Logging: Does your system record a stable, machine-readable ID for every safety intervention (e.g., a "why" behind an escalation), or are you relying on opaque post-hoc transcripts for audits?

Phase 4: Interaction Integrity & Monitoring

7. Disclosure Resistance: If a curious user probes your AI about its internal safety logic ("How do you decide what to filter?"), does the system narrate its design, or is there a "Disclosure Guard" that intercepts these probes before they leak architectural intent?

8. Algorithmic Drift & Bias Detection: Do you have a "Canary" or drift monitor that provides turn-by-turn alerts if the AI begins to show performance degradation or bias across specific patient demographics?

Phase 5: Incident Response & Liability

9. AI Incident Response Plan: Do you have a documented procedure for "Locking the Model" and notifying affected patients within 24 hours if an AI is found to have misinterpreted high-stakes clinical data?

10. Liability Underwriting: Has your malpractice carrier reviewed your AI architecture to confirm it provides sufficient "Audit Trails" and "Decision Traceability" to support a defense against a multi-million dollar liability claim?

The SASI system provides the independent safety substrate that enforces these governance requirements in real time and produces the audit-ready evidence boards, regulators, and underwriters now expect.