Insurance AI Governance
How to Prepare for a NAIC AI Systems Evaluation Tool Exam Interview
Preparation means mapping your documented AI governance program, covering board oversight, risk management, and internal controls across the AI lifecycle, to concrete technical evidence such as access management records, monitoring logs, and audit trails, so examiners can see the policy operating in practice rather than only described on paper.
Direct answer: Treat the interview as operational verification. Link written governance (accountability, risk framework, lifecycle controls) to living evidence examiners can inspect: access records, monitoring output, and audit trails.
Core readiness areas
Organize interview materials around four areas that consistently connect Model Bulletin expectations to what examiners ask in practice.
Governance accountability
Board and senior management ownership of the AI governance program, with named roles who can speak to decisions.
Risk management framework
A documented process for identifying and managing AI-related risk at acquisition, deployment, and in production.
Lifecycle internal controls
Controls spanning development, third-party acquisition, deployment approval, and ongoing monitoring.
Operational evidence
Access logs, monitoring records, and audit trails that show the written policy is enforced day to day.
Documentation to organize before the interview
Assemble a packet that lets you move from policy language to artifacts without hunting during the session.
- Current written AI governance policy with named accountable owners
- Risk management framework documentation, including how AI risk is assessed at acquisition and deployment
- Lifecycle control records covering development, third-party acquisition, deployment approval, and monitoring
- Access management and permission records for systems and personnel interacting with AI models
- Audit trail exports demonstrating ongoing monitoring activity
- Records of prior exam findings and evidence of remediation, if applicable
The regulatory basis for the interview
The NAIC's Model Bulletin on the use of artificial intelligence systems by insurers directs state insurance departments to expect a written AI governance program from every regulated entity. That program is expected to establish board or senior management accountability, a documented risk management framework, and internal controls that apply across the AI system lifecycle, from development and third-party acquisition through deployment and ongoing monitoring.
An exam interview under a NAIC-aligned evaluation process is, at its core, a test of whether that written program reflects what the organization actually does. Compliance leaders should treat the interview as an operational verification exercise rather than a review of policy language alone. Before building interview materials, confirm with your state insurance department whether a specific evaluation tool, checklist, or scoring instrument will be used, and obtain the actual criteria list rather than relying on general descriptions of NAIC expectations.
What examiners typically probe
Regulators conducting AI governance interviews generally focus on whether accountability, risk management, and controls are demonstrable rather than aspirational. Expect questions about who owns AI governance at the senior management or board level, how risk is assessed before a model or third-party AI tool is adopted, and how that assessment changes once the system is in production.
Access management, monitoring, audit logging, and model oversight are consistently referenced control areas in AI governance guidance, and examiners are likely to ask how each is implemented technically, not just how each is described in policy. Because no confirmed source specifies a single required logging standard or scoring rubric, insurers should be prepared to explain their own control design and rationale clearly, and to show that the design is applied consistently across the systems in scope for the exam.
| Focus area | What to be ready to show |
|---|---|
| Accountability | Named board or senior owners; who answers for each control area |
| Pre-adoption risk | Assessment method before model or vendor AI adoption |
| Production risk | How risk posture is updated after go-live |
| Access management | Permissions for systems and people who interact with models |
| Monitoring and audit | Logs, reports, and trails that prove ongoing oversight |
Common readiness gaps
The most consistent weakness in AI governance interview readiness is a gap between documented policy and operational evidence. An organization may have a well-written governance policy that references risk management and internal controls, but cannot produce access logs, monitoring reports, or testing records that show the policy functioning day to day.
A second common gap is accountability that is described generically at the senior management level without a named individual or team responsible for a specific control area, which makes direct examiner questions difficult to answer. Because verified, current data on examiner-identified deficiency patterns was not available for this guide, compliance teams should treat these as general risk areas to test internally, and should validate any specific claims about examiner focus areas against current guidance from their state insurance department before relying on them in interview preparation.
Where runtime governance fits
Some of the technical evidence examiners look for, particularly access management, tool-call permissions, and audit logging for AI systems, is generated more reliably when governance is enforced at runtime rather than reconstructed after the fact from static documentation. Runtime policy enforcement, agent identity and permission controls, and continuous audit logging produce the kind of operational record that supports a governance narrative during an interview. This is not a substitute for the written program the Model Bulletin expects, but it can make the difference between describing a control and showing it in operation.
Frequently asked questions
Is there a single official NAIC AI Systems Evaluation Tool document?
The NAIC Model Bulletin establishes governance, risk management, and internal control expectations, but the exact structure of any state-level evaluation tool or checklist should be confirmed directly with your state insurance department rather than assumed from general descriptions.
What is the difference between the Model Bulletin and an exam interview checklist?
The Model Bulletin sets the general expectation for a written AI governance program. An exam interview, and any associated evaluation tool a state uses, tests whether that program is actually operating, using questions and document requests specific to that examination.
Who should be prepared to answer questions in the interview?
Since the Model Bulletin ties accountability to senior management and the board, at least one representative able to speak to governance ownership should be present, alongside technical staff who can explain access, monitoring, and audit controls in operational detail.
Build interview-ready AI governance evidence
Runtime governance, access controls, and audit logging can help turn a written AI governance policy into demonstrable operational evidence for exam interviews.
Explore Runtime Governance