Sealed AI 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 (an append-only, tenant-scoped decision record with integrity controls) that can later be shown to:

• courts and regulators,
• auditors and internal oversight,
• and insurers/reinsurers.

Sealed artifacts are designed to support evidentiary and audit review under tenant control, while allowing vendors to keep proprietary implementation details private.

Requirements for a Compliant Pre-Execution Governance Runtime

A system advertised as a sealed AI governance runtime is expected to meet at least the following requirements.

A runtime that claims compliance with this pattern MUST:

1. Fail closed by default.
   If policy, identity, or required context is missing or ambiguous, the runtime MUST NOT return Approve. It MUST return Refuse or Supervised Override according to tenant policy (never “best-effort allow”).
2. Run before irreversible actions.
   The gate evaluates the request before filings, submissions, approvals, or payments are executed.
3. Operate independently of any single model.
   Decisions are based on identity, policy, and context – not on model prompts, embeddings, or provider-specific features.
4. Use tenant-owned policies and identity.
   Policy rules, risk posture, and identity data (roles, groups, org chart) come from client-controlled systems.
5. No model training on tenant decision data by default.
   Runtime decision data and artifacts MUST NOT be used to train or fine-tune foundation models, or for cross-tenant analytics, without explicit written tenant opt-in.
6. Emit integrity-verifiable artifacts for every governed attempt.
   Each approve / refuse / override result yields an append-only, tenant-scoped decision record with identifiers, timestamps, and decision metadata, designed to support integrity verification and audit review.
7. Expose clear refusal and override codes.
   Refusals and supervised overrides are governance outcomes, not generic technical errors.
8. Support supervised override with explicit accountability.
   Overrides require an authenticated, identified decision-maker and produce enhanced artifacts that record who accepted the risk. Overrides SHOULD be scope-bounded (action + context + time window) according to tenant policy.
9. Avoid full-text inspection of sensitive content unless necessary.
   Where possible, the runtime should rely on labels, classifications, and metadata, not raw client content.
10. Integrate with existing GRC, IAM, and matter systems.
    The runtime consumes policy, identity, and matter context from systems of record, not ad-hoc configuration.
11. 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.
12. Support observation-only / quiet modes.
    Organizations can initially run the runtime in observe-only mode to see what would have been blocked, before enforcing.
13. Offer transparent, auditable configuration.
    Policy configurations and rule versions are traceable, versioned, and explainable to regulators and auditors.
14. 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.

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.