The regulatory and risk landscape
- NAIC Model Bulletin on the Use of AI Systems by Insurers (Dec 2023): adopted by 25 states plus D.C. as of early 2026 (adoption map). It expects every insurer using AI to maintain a written AI governance program covering inventory, risk assessment, human oversight, testing, and vendor-AI diligence. Regulators can request this documentation during market conduct exams.
- NAIC AI Systems Evaluation Tool: a standardized examination framework being piloted by twelve states January–September 2026 (pilot summary). Translation: AI governance is moving from "expectation" to "exam item."
- AI does not suspend existing law. Unfair trade practices, unfair discrimination, and rate regulation apply to AI-assisted decisions exactly as to human ones. "The model did it" is not a defense.
- Colorado remains the bellwether for algorithm and data-governance requirements (the SB 21-169 regime, currently life-focused but widely expected to expand by line).
- NIST AI Risk Management Framework (plus its Generative AI Profile) is the de facto scaffolding for a defensible governance program, and the one the NAIC bulletin echoes.
- EU AI Act: relevant only with EU business or operations; high-risk obligations phase in through August 2026.
Top risks to manage, in order
- Confidentiality: employees pasting policyholder or company data into consumer AI tools. Solved with enterprise agreements plus policy; fix this first.
- Accuracy / hallucination in outputs that reach customers, regulators, or decisions.
- Unfair discrimination / bias if AI touches risk selection or pricing.
- Vendor risk: AI is arriving embedded in software insurers already buy; know where.
- Over-reliance: staff accepting AI output without review ("automation bias").
Questions leaders should be asking
Use these in management meetings; they map to what examiners will ask:
- Do we have a written AI governance policy and an inventory of where AI (including vendor-embedded AI) is used today?
- Do employees have a sanctioned, enterprise-grade AI tool, and a clear rule about consumer tools?
- For each use case: what is the human review step, and who is accountable for the output?
- How would we answer a market conduct exam question about AI in underwriting or claims, today?
- Which vendors have added AI features to products we already license, and what data do they see?
- What is our measurement plan: are we tracking time saved, error rates, and adoption, or just launching pilots?
- Who owns AI governance? (Common answer: a small cross-functional group of data science, legal/compliance, IT security, and a business sponsor.)
A pragmatic 90-day posture
- Weeks 1–4: adopt an interim acceptable-use policy (template below); procure enterprise AI access with zero-data-retention / no-training terms; brief all staff.
- Weeks 4–8: stand up the governance group; inventory current AI use including vendor tools; select 2–3 pilot use cases with named owners and success metrics.
- Weeks 8–13: run pilots with human-in-the-loop review; measure; report results and a scale/kill decision to the executive team.
The goal is governed momentum: moving fast enough to learn, with guardrails proportionate to risk. The two failure modes are symmetric: banning AI (staff will use personal accounts invisibly, so-called "shadow AI") and ungoverned enthusiasm (which regulators are now actively examining for).
For the multi-year strategic view (where the industry stands, what competitors have deployed, and a phased 36-month progression with governance gates and economics) see the companion AI integration phases.
Responsible-use policy template
Template
This is a policy TEMPLATE. Bracketed items require company-specific decisions; have Legal/Compliance review it before formal adoption. Aligned with the NAIC Model Bulletin's expectations for a written AI Systems Program and the NIST AI Risk Management Framework.
1. Scope
These guidelines apply to all employees and contractors using: general-purpose AI assistants; AI features embedded in vendor software (including underwriting, claims, and productivity platforms); and internally built AI/LLM applications. Traditional predictive models remain governed by existing model-governance policy; where an AI system feeds a regulated decision, both policies apply.
2. Sanctioned tools: the bright line
- Use only company-approved AI tools [list; e.g., enterprise instances under company agreements with no-training and retention terms].
- Never enter company, policyholder, claimant, broker, or employee information into personal or consumer AI accounts. This includes "just this once," and it includes screenshots.
- Requests for new tools or AI-enabled vendor features go to [AI governance group] before use.
3. Data rules
| Data class | Sanctioned enterprise tools | Consumer / personal AI tools |
|---|---|---|
| Public information | ✓ Allowed | ✓ Allowed |
| Internal, non-confidential | ✓ Allowed | ✗ Prohibited |
| Confidential business | ✓ Allowed with need-to-know | ✗ Prohibited |
| Policyholder / claimant PII, PHI | ⚠ Approved use cases only; minimize and de-identify where feasible | ✗ Prohibited |
| Restricted (M&A, litigation) | ✗ Requires specific approval | ✗ Prohibited |
Outputs derived from confidential inputs inherit the input's classification.
4. Human accountability
- You own what you ship. AI output that you send, file, or act on is your work product. Review it as you would a junior colleague's draft.
- Consequential decisions require human review. No AI output may, without documented human review, determine or effectively determine: risk selection or declination, pricing or rating, claim acceptance/denial or reserve values, coverage interpretations communicated externally, or personnel decisions.
- Verify facts, numbers, and citations. Any figure, quotation, legal or regulatory citation, or policy-language reference must be checked against the source before use.
- Disclosure: [company position; recommended minimum: disclose AI assistance within work products supporting actuarial opinions and regulatory filings; customer-facing disclosure per applicable state law].
5. Use-case risk tiers
| Tier | Examples | Requirements |
|---|---|---|
| Low | Drafting, summarizing internal docs, code assistance, meeting notes | Sanctioned tool + human review; no approval needed |
| Medium | Submission triage, document extraction feeding a human decision, internal RAG knowledge tools | Registered in AI inventory; defined owner; documented accuracy evaluation before and after deployment |
| High | Anything materially influencing underwriting, pricing, or claims outcomes; anything customer-facing | Full model-governance treatment: validation, bias testing, monitoring, documented human oversight, Legal/Compliance sign-off, exam-ready documentation |
| Prohibited | Fully automated adverse decisions (declination, denial, non-renewal) without human review; AI-generated legal or regulatory positions without counsel review; data use violating §3 | n/a |
6. Governance structure
- AI Governance Group: [named members; recommended: data science lead (chair), Legal/Compliance, IT Security, business-unit sponsor]. Owns this policy, the AI inventory, tool approvals, and tier classification.
- AI inventory: a living register of every AI system in use (internal, vendor-embedded, and experimental) with owner, tier, data touched, and evaluation status. This inventory is the first thing a market-conduct examiner will ask for.
- Vendor AI diligence: procurement and renewals must ask: Does this product use AI? On what data? Can it be disabled? What are the provider's training and retention terms?
- Incident handling: suspected AI-caused errors reaching customers, regulators, or financials are reported to [channel] within [24 hours]; treat like any other E&O-relevant incident.
- Records: for Medium/High-tier systems, retain prompts, configurations, model versions, evaluation results, and review decisions per [retention schedule].
7. Security notes
- AI systems that read external documents (submissions, emails, claims correspondence) are exposed to prompt injection: adversarial instructions embedded in those documents. Such systems must be designed with least-privilege tool access and no unreviewed external actions; Medium tier minimum.
- Report suspected AI-related phishing or deepfake contact (voice or video impersonation of executives, brokers, or claimants is now a standard fraud vector) to IT Security immediately.
8. Training requirement
All staff complete [AI awareness briefing] before tool access; Medium/High-tier system owners complete [role-specific training]. Re-certification [annually]. Review cycle for this document: [quarterly] by the AI Governance Group.