Sealed AI Governance Runtime
Reference Architecture & Requirements for 2026
Who this is for: regulators, insurers, risk, procurement, and technical evaluators.
How to use it: treat this as a reference pattern and copy-paste the MUST/SHOULD requirements and clauses into your own policies, RFPs, and guidance.
Reuse license:
you may reuse and adapt the language on this page.
1. What Problem This Solves
Modern AI systems can now draft, decide, and trigger actions that touch:
- client and consumer data
- courts and regulators
- payments, approvals, and records systems
Most governance today focuses on models and data:
- who can access which data
- which models are allowed
- how those models are monitored
What’s missing is a decision layer at the execution gate.
Before a filing is submitted, an approval is recorded, or a payment moves, organizations need a reliable way to answer:
Is this specific person or system allowed to take this specific action, in this matter, under this authority, right now?
A
sealed AI governance runtime (SEAL-style) solves that gap.
It does not replace your models or policies. It
enforces your rules at the moment of action and produces sealed evidence of what was allowed or refused.
2. Core Pattern: Pre-Execution Governance Runtime
A pre-execution governance runtime sits between AI tools / applications and high-risk actions.
It receives requests to act, evaluates them against client-owned rules, and returns one of three outcomes:
✅ Approve – the action may proceed under the declared authority.
❌ Refuse – the action is blocked under current rules.
🟧 Supervised Override – the action may proceed only with an identified human decision-maker attached.
The runtime operates on metadata and context, not on model internals:
- Who is acting (identity, role, group)
- Where they are acting (matter, case, account, jurisdiction, environment)
- What they are trying to do (action type, motion type, operation)
- How fast it must happen (urgency / turnaround)
- Consent & constraints (client instructions, supervision requirements, risk posture)
For every decision, the runtime produces a sealed artifact that can later be shown to:
- courts and regulators
- auditors and internal oversight
- insurers and reinsurers
This artifact records what the gate decided and why, without exposing underlying client content or proprietary policy logic.
3. Separation of Powers
A healthy AI governance stack maintains clear separation of powers:
- Formation / Model Layer
- Data controls, DLP, and classification
- Approved AI endpoints and LLM gateways
- Model governance and monitoring
2. Execution / Governance Runtime Layer (this pattern)
- Pre-execution authority gate
- Approve / refuse / supervised override decisions
- Sealed artifacts and audit trail
3. Tenant Router & Oversight Layer
- Routing of artifacts into tenant audit stores
- Internal oversight, risk, and ethics review
- Reporting to courts, regulators, and insurers
The governance runtime does not:
- draft or file documents
- provide legal or professional advice
- decide what the policies should be
It does:
- apply the tenant’s existing policies consistently
- block out-of-policy actions by default
- create an immutable, tenant-controlled record of every governed attempt.
4. Non-Negotiable Guarantees
Requirements for a Compliant Pre-Execution Governance Runtime
A system advertised as a sealed AI governance runtime SHOULD meet at least the following requirements.
A runtime that claims compliance with this pattern MUST:
- Fail closed by default.
If policy, identity, or context is missing or ambiguous, the runtime returns Refuse, not “best effort”. - Run before irreversible actions.
The gate evaluates the request before filings, submissions, approvals, or payments are executed. - Operate independently of any single model.
Decisions are based on identity, policy, and context – not on model prompts, embeddings, or provider-specific features. - Use tenant-owned policies and identity.
Policy rules, risk posture, and identity data (roles, groups, org chart) come from client-controlled systems. - Never train on tenant decision data.
Runtime decision data and artifacts MUST NOT be used to train foundation models or cross-tenant systems. - Emit sealed, tamper-evident artifacts for every governed attempt.
Each approve / refuse / override result yields an append-only, tenant-scoped record with identifiers, timestamps, and decision metadata. - Expose clear refusal and override codes.
Refusals and supervised overrides are governance outcomes, not generic technical errors. - Support supervised override with explicit accountability.
Overrides require an identified decision-maker and produce enhanced artifacts that show who accepted the risk. - Avoid full-text inspection of sensitive content unless necessary.
Where possible, the runtime should rely on labels, classifications, and metadata, not raw client content. - Integrate with existing GRC, IAM, and matter systems.
The runtime consumes policy, identity, and matter context from systems of record, not ad-hoc configuration. - Provide tenant-controlled storage for artifacts.
Sealed artifacts can be stored in tenant-owned audit stores (e.g., dedicated buckets, legal records systems) under the client’s retention policies. - Support observation-only / quiet modes.
Organizations can initially run the runtime in observe-only mode to see what would have been blocked, before enforcing. - Offer transparent, auditable configuration.
Policy configurations and rule versions are traceable, versioned, and explainable to regulators and auditors. - Be non-bypassable for governed workflows.
For high-risk workflows that declare governance by this runtime, there MUST NOT be a side-door path around the gate.
5. Key Concepts Glossary
Pre-Execution Governance Runtime
A dedicated layer that decides, before an action runs, whether it is authorized to proceed under tenant policies and risk posture.
Refusal Infrastructure
The mechanisms, codes, and artifacts used when the runtime blocks or escalates a request instead of approving it.
Sealed Artifact
A tamper-evident record of a governance decision, including context and identifiers, stored under tenant control and engineered for audit and evidentiary use.
Tenant-Owned Artifact Store
The system or storage location where sealed artifacts live under the client’s retention, access, and jurisdictional rules.
Supervised Override
A governed path where a blocked action may proceed only when a named human decision-maker explicitly accepts responsibility in the runtime.
Anchors (Who / Where / What / How Fast / Consent)
The minimum decision context: actor identity, matter/context, action type, urgency, and client or supervisory instructions.
Execution Stack / Action Governance Gate
The layer between AI-assisted artifacts and real-world actions (e.g., file, send, approve, move money) that decides whether execution is allowed.
Observe-Only Mode
A configuration where the runtime evaluates actions and records what it would have done, without enforcing approvals or refusals.
6. Integration Requirements & Evaluation Checklist
This section is written for risk, engineering, and procurement teams evaluating whether a deployment conforms to this pattern.
✅ Requirement 1 – Identification of High-Risk Actions
A compliant deployment SHOULD be able to demonstrate that it has:
- Identified where AI or automation can:
- file or submit documents to courts, regulators, or counterparties
- approve or record binding decisions
- move money, change limits, or modify critical records
- Designated these workflows as governed actions subject to the pre-execution runtime.
✅ Requirement 2 – Governance Runtime in Front of High-Risk Actions
A compliant deployment SHOULD:
- Ensure the final “execute” call (e.g., submit, file, approve, transfer) for each governed workflow is evaluated by the governance runtime before it runs.
- Demonstrate that there is no alternate execution path that bypasses the gate for governed actions.
✅ Requirement 3 – Use of Policy, Identity, and Matter Systems as Sources of Truth
A compliant deployment SHOULD:
- Integrate the runtime with:
- GRC / policy systems for rules and risk posture
- Identity providers (IdP) for users, roles, and groups
- Matter / case / account systems for context and deadlines
- DLP / classification systems for data labels (confidential, public, restricted, etc.)
- Treat these systems as authoritative sources of truth, rather than attempting to replace them with ad-hoc configurations inside the runtime.
✅ Requirement 4 – Governance Rules and Refusal Codes
A compliant deployment SHOULD:
- Encode rules that cover, at a minimum:
- which roles may perform which actions in which matters or accounts
- when supervision or partner / senior sign-off is required
- how data classifications constrain destinations or channels
- how urgency or turnaround affects enforcement (including any emergency override paths)
- Define refusal codes and override codes that align with the organization’s policy framework and risk taxonomy, and that can be surfaced in sealed artifacts.
✅ Requirement 5 – Observe-Only Mode Prior to Enforcement
A compliant deployment SHOULD be able to show that it has:
- Operated the runtime in quiet / observe-only mode to collect artifacts without blocking execution.
- Analyzed refusal patterns and false positives with relevant teams (risk, engineering, operations).
- Transitioned to active enforcement for high-risk workflows only after this calibration.
✅ Requirement 6 – Oversight, Reporting, and Use of Artifacts
A compliant deployment SHOULD:
- Store sealed artifacts in tenant-controlled audit stores under appropriate retention and access controls.
- Route relevant artifacts and metrics to internal audit, risk, and ethics committees or equivalent functions.
- For regulated industries, be able to incorporate selected artifacts into regulatory filings, examinations, incident reports, or supervisory requests as evidence of control.
7. Copyable Clause Snippets
You may adapt the following example language in
RFPs, contracts, and internal policies.
(Organizations should tailor wording to their jurisdiction, regulatory obligations, and legal advice.)
7.1 RFP / Vendor Requirements
Pre-Execution Governance Runtime
The Vendor SHALL front all high-risk AI-enabled workflows with a pre-execution governance runtime that:
- evaluates who is acting, on what, in which matter or account, and under which authority, before execution;
- returns explicit approve / refuse / supervised override decisions; and
- records each decision as a sealed, tamper-evident artifact under the Customer’s control.
Training and Data Use
The Vendor MUST NOT use any runtime decision data, refusal artifacts, or customer governance metadata for the training or fine-tuning of foundation models, or for cross-tenant analytics, without the Customer’s explicit written consent.
Fail-Closed Behavior
In cases of missing policy, ambiguous identity, or inconsistent context, the governance runtime MUST default to Refuse, not “best-effort allow”.
Non-Bypassability
For workflows designated as governed, the Vendor MUST ensure there is no technical path that bypasses the governance runtime to execute high-risk actions.
7.2 Internal Policy / Risk Committee Language
All AI-assisted workflows that can submit filings, approve decisions, or move client or customer assets SHALL be integrated with a sealed pre-execution governance runtime.
This runtime SHALL:
- enforce firm-approved policies derived from GRC, identity, and matter systems;
- fail closed by default when policy or context is insufficient; and
- produce sealed, tenant-owned artifacts for every approve, refuse, and supervised override decision.
Sealed artifacts MAY be used in:
- internal audit and ethics reviews,
- incident reconstruction and root-cause analysis, and
- communications with courts, regulators, and insurers, where appropriate.
8. FAQ – Common Questions
Q1. How is this different from normal AI guardrails or model policies?
A. Guardrails and model policies govern what a model is allowed to say or generate. A pre-execution governance runtime governs what may actually execute in the real world (file, send, approve, move money) under your authority.
Q2. Does this require us to centralize all our data?
A. No. The runtime can operate primarily on metadata and labels from your existing systems. Under this pattern, data, models, and policies stay in your environment; the runtime is a gate that consumes signals and returns decisions.
Q3. Who is responsible if the policy encoded in the runtime is wrong?
A. Policy ownership remains with the tenant organization (e.g., firm leadership, risk committee, GC). The runtime enforces the policy as configured and records what happened; it does not create the policy or make professional judgments.
Q4. Can we start without blocking anything?
A. Yes. This pattern explicitly supports observe-only modes, where the runtime records what it would have approved or refused. Many organizations start here to calibrate rules before enforcing.
Q5. Does this replace our existing GRC, identity, or matter management tools?
A. No. Those systems remain your sources of truth. The governance runtime simply connects to them and applies their outputs in real time at the execution gate.
Q6. How does this help with regulators and insurers?
A. By fronting high-risk AI workflows with a sealed governance runtime, organizations can show evidence of control: who was authorized to act, which rules applied, what decision was made, and when. Sealed artifacts give regulators and insurers a verifiable trail instead of a reconstruction exercise.
“This page is intended as open governance reference material. You may quote or adapt with attribution ‘Thinking OS™’.” but it’s not required.