The AI OSI Stack is an AI governance failsafe for leaders who carry fiduciary, regulatory, or public trust responsibility.

It turns every AI decision, from data intake to model output to public disclosure, into a tamper-evident evidence trail tied directly to your mandates, ethics, and risk controls.

For CEOs, boards, CISOs, and General Counsel, this means:

  • Decision Insurance: Every significant AI decision carries documented assumptions, constraints, and rationale.

  • Compliance by Architecture: The stack maps cleanly to EU AI Act, NIST AI RMF, and ISO/IEC 42001, with exportable artifacts for Annex IV and beyond.

  • Audit-Ready Oversight: Governance dossiers, Solomon Briefs, and disclosure packs are generated as part of normal operation, not bolted on later.

Bottom line: AI becomes governable infrastructure you can defend, to regulators, courts, investors, and the public.

My work so far in this area:

The Solution: Layered Integrity

The AI OSI Stack defines governance as a verifiable, nine-layer infrastructure, separating:

  • Mandate from ethics

  • Ethics from data

  • Data from models

  • Models from control

  • Control from public trust

This layered separation pinpoints risk precisely where it emerges and ensures every AI decision is accountable, reconstructable, and auditable.

The Challenge: Unauditable Risk

Modern AI systems operate at institutional scale, but governance remains fragmented—model cards here, privacy reviews there, and no common backbone to unify accountability.
This results in:

  • Opaque decision-making

  • Fragmented liability

  • Unverifiable provenance

  • Regulatory exposure

  • Organizational risk that leadership cannot mitigate or explain

I. Strategic Value for Leadership (NACD, C-Suite, and Fiduciary Duty)

Liability Insulation (“Decision Insurance”)

The stack mandates Decision Insurance: a procedural safeguard requiring systems to surface:

  • Assumptions

    Risks

  • Constraints

  • Rationale

before issuing any decision artifact. This produces demonstrable epistemic integrity and protects leadership from claims of negligence or reasoning failure.

Compliance Assurance Across Global Frameworks

The Stack is architecturally compatible with major global standards, including:

  • EU AI Act (Annex IV & IX)

  • ISO/IEC 42001

  • NIST AI RMF (Govern–Map–Measure–Manage)

Every requirement has a direct Stack artifact capable of evidentiary export, meaning compliance is no longer paperwork — it is generated by design.

Audit-Ready Governance Publication

Layer 7 mandates:

  • Stack-Aligned Reports (SARs)

  • Governance Disclosure Statements (GDS)

These consolidate evidence from Layers 0–6 into an audit-ready dossier that can be shown to:

  • Boards and committees

  • Auditors

  • Regulators

  • Public oversight bodies

Governance becomes portable proof, not a promise.

II. Layered Accountability: A Map for Managing Risk

Where executive risk is resolved across the AI OSI Stack:

Layer 0 / Layer 1: Civic Mandate / Ethical Charter — Establishes social license, scope of authority, prohibitions, and moral intent.

Layer 2: Data Stewardship — Privacy, provenance, consent, retention. Requires Consent & Context Manifests (CCMs).

Layer 3: Model Development — Reproducibility, alignment. Model Cards linked to lineage ensure evaluable ethics.

Layer 4: Instruction & Control — Persona governance, guardrails, refusal logic, human-in-the-loop.

Layer 5: Reasoning Exchange — Transparent reasoning protocol, authenticated audit trails.

Layer 6: Deployment — Monitoring, incidents, rollback, performance accountability.

Layer 7: Governance Publication — Converts internal evidence into public-facing disclosures (SARs, GDS, Solomon Briefs).

Layer 8: Participation — Civic feedback, appeals, and renewal mechanisms.

A simple tiered visualization of Layers L0–L8, with each layer’s primary function labeled. Purpose: To give executives an instant mental model of the architecture.

III. Executive Application Examples

1. Healthcare, Privacy, and HIPAA Compliance (Layers 2, 5, 7)

Verifiable Stewardship

Layer 2 enforces provable:

  • Data intake rules

  • Classification

  • Retention and deletion

  • Breach and handling procedures

Consent Traceability

Every operation requires CCMs (Consent & Context Manifests) so data is only used:

  • Within scope

  • With documented consent

  • For an explicit declared purpose

This satisfies HIPAA, GDPR, and other health privacy regimes.

Non-Surveillance Guarantee

Anchored by a core principle:

“Transparency must never become surveillance.”

This prevents AI infrastructure from becoming a monitoring apparatus, protecting patient rights and institutional trust.

2. Public Safety, Body Cams, & Security Systems (Layers 0, 4, 8)

Mandate First

Layer 0/1 require public-facing AI—such as security cameras or automated enforcement—to obtain explicit democratic authorization before deployment.

Persona Control (Persona Architecture)

Layer 4 assigns each AI a role-bound persona (e.g., “traffic monitor”, “incident analyst”)
→ Prevents mission creep
→ Prevents unauthorized behavioral simulation
→ Maintains narrow operational purpose

Appeal and Redress

Layer 8 provides public mechanisms to challenge system outputs and request human oversight.
This is the civic safety valve for high-impact applications.

3. Fiduciary Responsibility & Auditable Rationale (Layers 5, 7)

Epistemic Accountability via AEIP

The AI Epistemic Infrastructure Protocol ensures all reasoning flows:

  • are authenticated

  • are logged

  • include assumptions and trade-offs

  • have lineage

  • are exportable

This creates a tamper-evident cognitive history for every decision.

Executive Briefing Artifacts

Layer 7 produces:

  • Solomon Briefs (1-page rationales)

  • Stack-Aligned Reports (SARs)

  • Governance Disclosure Statements (GDS)

These allow boards, regulators, and executives to understand not only the outcome but the reasoning trajectory behind it.

© 2025 Daniel P. Madden. All right reserved. Source material released under CC BY-NC-ND 4.0