Insurance Compliance Guide
Runtime Enforcement for Third-Party AI Models in Insurance Compliance Programs
Most insurer third-party AI oversight programs rely on point-in-time vendor attestations, questionnaires, and periodic audits that describe intended model design rather than actual production behavior. Runtime policy enforcement closes this gap by evaluating model inputs and outputs continuously, as they occur, independent of vendor cooperation. Insurers that maintain only onboarding documentation cannot demonstrate ongoing oversight between review cycles.
What Runtime Enforcement Means for Third-Party AI Models
Third-party AI model governance in insurance covers the controls an insurer applies to AI systems it does not build or fully control, including vendor-supplied underwriting engines, claims triage models, and pricing algorithms. Most current programs rely on static due diligence: vendor questionnaires, model cards, SOC-type reports, and periodic re-certification completed at onboarding and renewed on a fixed schedule, typically annually.
Runtime policy enforcement is a different control category. It evaluates a model's actual inputs and outputs while the model is operating in production, rather than relying solely on documentation describing how the model is intended to behave. Enforcement can intercept, log, or constrain data sent to a vendor model and the decisions or scores returned, independent of whether the vendor has changed anything internally.
The distinction matters because a vendor's underlying model, including any embedded large language model or scoring logic, can be retrained, updated, or reconfigured after an insurer's due diligence cycle is complete. Static attestations describe the model as it existed at review time. They do not confirm that the same model, unchanged, is what continues to run in production.
Where Static Vendor Due Diligence Falls Short
Vendor questionnaires and point-in-time audits remain a reasonable starting control, but they have a structural limitation: they measure the model at a single moment and rely on the vendor's own representations. An insurer typically does not control the vendor's training data, model updates, or internal logic, which makes vendor self-certification an incomplete substitute for the insurer's own operational visibility into how the model behaves once integrated into underwriting, claims, or pricing workflows.
This creates a temporal gap. If a vendor updates a model between annual reviews, and the update changes how the model scores risk or evaluates a claim, the insurer's most recent attestation no longer reflects current production behavior. Static due diligence has no built-in mechanism to detect that change unless the vendor proactively discloses it or the insurer independently monitors production output.
The practical question for risk leaders is not whether vendor attestations are useful, but whether they are sufficient on their own to demonstrate ongoing oversight between review cycles.
NAIC Accountability Expectations Heading Into the Summer 2026 Meeting
NAIC's Model Bulletin on AI Systems and the work of its Big Data and Artificial Intelligence Working Group place accountability for an AI system's outcomes on the insurer using the system, including when the AI is vendor-supplied. That framing shifts the oversight burden toward the insurer rather than treating vendor documentation as a complete discharge of responsibility.
AI governance and vendor oversight have remained active discussion items across recent NAIC meeting cycles, spanning the Innovation, Cybersecurity, and Technology (H) Committee and related working group activity. Risk leaders should treat the upcoming meeting as a checkpoint for confirming how examiners expect insurers to document ongoing oversight of third-party AI, not as the origin of the underlying accountability expectation, which is already established through the Model Bulletin and working group activity.
For compliance purposes, the operative question in an examination is likely to be whether an insurer can distinguish, with evidence, between documentation-based vendor due diligence and evidence of continuous monitoring of actual model behavior in production.
Static Vendor Due Diligence vs. Runtime Policy Enforcement
The two approaches are not mutually exclusive, but they answer different questions. The table below summarizes how each addresses oversight of a third-party model over its lifecycle.
| Capability | Runtime policy enforcement | Static vendor attestation | Key difference |
|---|---|---|---|
| Evaluation timing | Continuous, as the model operates in production | Point-in-time, renewed on a fixed annual cycle | Detects behavior changes between review cycles |
| Dependency | Independent of vendor cooperation or disclosure | Relies on vendor self-certification and voluntary disclosure | Removes reliance on vendor reporting |
| Scope of visibility | Actual inputs and outputs the model processes | Documentation describing intended model design | Confirms behavior, not just stated intent |
| Detecting model changes | Surfaces shifts in scoring or output as they occur | No built-in mechanism between review cycles | Closes the temporal gap between attestations |
| Regulatory evidence | Continuous logs suitable for examination | Onboarding documentation only | Supports evidence of ongoing oversight |
Where Vendor AI Oversight Breaks Down
The gap between due diligence and production behavior tends to form in a predictable sequence.
Point-in-time attestation
Vendor questionnaires and audits captured at onboarding, renewed on a fixed cycle.
Vendor model updates
Underlying models can change between attestation cycles without insurer notice.
Runtime visibility gap
Static documentation does not confirm current production behavior.
Insurer accountability
NAIC's stated framework places outcome accountability on the insurer, including for vendor-supplied models.
Technical Mechanisms for Runtime Enforcement of Vendor Models
These mechanisms describe how runtime enforcement is applied to third-party vendor models without requiring changes to the vendor's underlying system.
- 1
Integration-layer enforcement
Runtime controls applied at the API or integration layer between the insurer's systems and the vendor model, so enforcement does not require access to or modification of the vendor's internal architecture.
- 2
Bidirectional evaluation
Enforcement points that evaluate both outbound data sent to the vendor model and inbound decisions or scores returned, rather than checking only one direction.
- 3
Insurer-controlled policy logic
Policy rules that the insurer can update without requiring vendor cooperation or model retraining, so oversight is not dependent on vendor responsiveness.
- 4
Evidentiary logging
Runtime logs that create a record suitable for regulatory examination, distinct from vendor-supplied documentation, showing what the model actually received and returned over time.
- 5
Cross-vendor consistency
A control layer capable of operating across multiple third-party models and vendors, supporting a consistent enterprise-wide standard rather than a separate point solution per vendor.
Questions Risk Leaders Should Be Able to Answer
Use these as a self-assessment for the maturity of a third-party AI oversight program.
- Does our third-party AI vendor oversight program rely primarily on point-in-time attestations, and how would we detect a behavior change between review cycles?
- Can we monitor or enforce policy controls on vendor-supplied model inputs and outputs at runtime, independent of the vendor's own controls?
- Who within our organization is accountable for verifying that a vendor model's production behavior matches what was represented during due diligence?
- What audit trail can we produce to a state regulator showing continuous oversight of a third-party model, not just onboarding documentation?
- How quickly would we know if a vendor updated or retrained a model used for underwriting, claims, or pricing decisions?
Close the Gap Between Vendor Attestations and Production Behavior
Runtime governance gives risk and compliance teams continuous visibility into how third-party AI models actually behave in production, beyond what vendor documentation can confirm.
Request a Runtime Governance Demo