What Is a Pre-Execution AI Governance Runtime?
Short version:
A pre-execution AI governance runtime is a gate that sits in front of high-risk actions (file, submit, approve, move money, change records) and decides:
“Is this specific person or system allowed to take this specific action, in this matter, under this authority, right now?”
It doesn’t write content. It doesn’t run the model.
It
governs what actually executes in the real world — and it leaves behind evidence you can audit.
For the full spec and copy-pasteable clauses, see:
“Sealed AI Governance Runtime: Reference Architecture & Requirements”
1. Plain-Language Definition
A pre-execution AI governance runtime (often shortened to “pre-execution governance runtime” or “sealed AI governance runtime”) is:
A dedicated system that evaluates a requested action before it runs and returns approve / refuse / supervised override, based on the organization’s own policies, identity, and context — while producing a sealed record of that decision.
In practical terms:
- It sits between AI tools / applications and the systems that do things:
- court / regulator portals
- payment rails
- core banking / claims / policy admin
- internal record systems
- It receives a request to act, with metadata like:
- who is acting (user, role, group, service account)
- what they’re trying to do (file, approve, transfer, update)
- where (matter/case/account, jurisdiction, environment)
- how urgent (normal vs emergency)
- under which constraints (client instructions, risk posture, supervision requirements)
- It applies the organization’s governance rules and returns one of three outcomes:
- ✅ Approve – action may proceed
- ❌ Refuse – action is blocked under current rules
- 🟧 Supervised Override – action may proceed only if a named human decision-maker accepts responsibility
For each decision, it produces a sealed artifact — a tamper-evident, tenant-controlled record that shows what was decided and why, without exposing full client content or internal logic.
2. Why Pre-Execution Governance Exists
Most AI governance today is focused on:
- Data – who can access what, which datasets models see
- Models – which models are allowed, what guardrails they run with
- Monitoring – what the model is saying, drift, bias, etc.
All of that matters. But there’s a missing layer at the top:
The execution gate — the moment where an AI-assisted action actually becomes real.
Without a pre-execution runtime:
- An AI can generate a plausible filing and submit it to the wrong venue.
- A workflow can use AI to pre-approve a payment and push it straight through.
- After an incident, the organization may have no clean record of:
- who triggered the action
- what rules applied
- whether anyone explicitly approved it
- whether any system tried to refuse it
A pre-execution governance runtime exists to fill that gap. It doesn’t try to solve “Is this output good?” It solves:
“Should this action be allowed to execute, given who is asking and what our rules say?”
3. Core Properties of a Pre-Execution Governance Runtime
A system that claims to be a pre-execution AI governance runtime should have a few non-negotiable behaviors.
3.1 It runs before actions, not after
- It evaluates the final execute step:
- file / submit
- approve / commit
- transfer / move
- update / delete
- It does not just log what happened after the fact.
- It is in the critical path: no runtime decision → no action.
3.2 It uses your policies and identity, not its own
- Governance rules come from entity-owned sources:
- GRC / policy systems
- identity providers (IdP, roles, org structure)
- matter / case / account systems
- DLP / classification labels
- The runtime enforces your rules; it does not invent policy or give advice.
3.3 It fails closed by default
- If identity is unclear, policy is missing, or context conflicts:
- it returns Refuse, not “do your best.”
- This is a core difference from many application-level checks, which often fail open in edge cases.
3.4 It is non-bypassable for governed workflows
- For workflows that are declared governed:
- there should be no side door that lets actions execute without hitting the gate.
- Any exceptions (disaster recovery, manual emergency paths) should be:
- rare
- documented
- auditable
3.5 It emits sealed, tenant-owned artifacts
- Every approve / refuse / override yields a sealed artifact that:
- is tamper-evident
- is scoped to the tenant / entity
- does not expose model prompts or raw client content
- These artifacts are designed to be:
- usable in internal audit and ethics review
- shown to courts, regulators, and insurers as evidence of control
For a detailed list of MUST/SHOULD requirements, see:
“Sealed AI Governance Runtime: Reference Architecture & Requirements” → Sections 4 & 6.
4. How It Differs from Guardrails, Logging, and Normal Access Control
It’s easy to confuse a pre-execution governance runtime with other controls. The distinctions matter.
4.1 Not just guardrails
- Guardrails:
- Govern content (what a model can say / generate)
- Operate at the model / API level
- Pre-execution runtime:
- Governs actions (what can actually be filed, approved, or executed)
- Operates at the execution / workflow level
You can have perfect guardrails and still approve the wrong action.
4.2 More than logging
- Logs tell you what happened.
- A pre-execution runtime:
- actively decides what is allowed to happen, then logs that decision as a sealed artifact.
- Logging is about observation; the runtime is about control + evidence.
4.3 Distinct from IAM alone
- IAM (identity & access management) answers:
“Can this identity access this system or resource?”
- The runtime answers:
“Given this identity, context, and policy, can this specific high-risk action execute right now?”
IAM controls
entry to systems.
The runtime controls
commit in the workflow.
5. Mini-Checklist: Does This System Actually Qualify?
If you’re evaluating a vendor or internal build, you can quickly test whether it’s really a pre-execution governance runtime, or just “guardrails plus logging.”
Ask:
1. Placement
- Does this system sit in front of high-risk actions so that nothing executes without a decision?
2. Decision Types
- Does it return approve / refuse / supervised override, or just log and alert?
3. Default Behavior
- When policy, identity, or context are ambiguous, does it fail closed (refuse), or allow?
4. Bypass
- For workflows that claim to be governed by it, are there any execution paths that bypass this layer?
5. Evidence
- Does it generate sealed, tamper-evident artifacts for each decision, stored under the organization’s control?
6. Policy & Identity Origin
- Are policy and identity data owned by the entity (GRC, IdP, matter systems), or are they re-implemented in a vendor-specific way?
If the answer to several of these is “no,” you’re likely dealing with something else (guardrails, logging, or a simple policy engine), not a full pre-execution governance runtime.
For a more complete evaluation checklist, see:
“Sealed AI Governance Runtime: Reference Architecture & Requirements” → Integration Requirements & Evaluation Checklist.
6. How It Fits Existing Governance Frameworks
A pre-execution AI governance runtime doesn’t replace your current frameworks; it completes them:
- For model risk management:
- It connects model outputs to real-world actions under explicit control.
- For operational risk & resilience:
- It creates a clear, testable control point and a sealed trail for investigations.
- For conduct & accountability regimes:
- It ensures senior management can prove that rules were enforced at the moment of action, not just written down.
- For data protection & confidentiality:
- It can operate primarily on metadata and labels, preserving data minimization while still enforcing rules.
You can think of it as:
The missing top layer in the AI governance stack — the execution-time authority gate that ties your policies and identity to what AI-assisted systems are actually allowed to do.
7. Where to Go Next
If you’re:
- drafting guidance or rules,
- designing underwriting questionnaires, or
- writing procurement requirements,
you don’t need to invent this from scratch.
Use the public spec:
Sealed AI Governance Runtime: Reference Architecture & Requirements
- a concise definition of the pattern
- non-negotiable guarantees (MUST/SHOULD)
- an evaluation checklist for deployments
- copy-pasteable RFP and policy clauses
- FAQ language you can adapt
A pre-execution AI governance runtime is not just another feature.
It’s the system that answers — in a way you can prove later:
“Who allowed this AI-assisted action to go through, under which rules, and where is the record?”
