Insurance | Runtime control

    LLM Governance for Insurance Catastrophe Claims: Runtime Controls and Evidence

    Before a surge, define which LLM-assisted claims activities need runtime controls and assign each an action: allow, redact, block, rate limit or escalate for human approval. During the surge, enforce those actions in the execution path for every interaction and tool call, with no unapproved bypass. Afterward, produce evidence showing the policy version applied and the decision taken. A catastrophe event tests whether controls built for normal volumes still hold.

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    During the surge: enforce in the execution path

    1. Evaluate every interaction and tool call against the current approved policy version
    2. Apply allow, redact, block, rate limit or escalate before the action takes effect
    3. Define and document the behavior when enforcement capacity is exceeded
    4. Route escalations to reviewers who can see the underlying data
    5. Log exceptions with approver and time

    After the surge: evidence a reviewer can test

    • The approved action-level policy and its version history for the window
    • Per interaction and tool call: the policy version applied and the decision taken (allowed, redacted, blocked, rate limited, escalated)
    • Escalation records showing who reviewed, when, and the outcome
    • Approved exceptions and policy changes, with approver and time
    • Third-party and vendor coverage for the LLMs and tools in use
    • Owners for governance, risk controls and internal audit

    Why a claims surge stresses LLM controls

    Catastrophe events push carriers and brokerages to extend LLM use into intake, triage, document summarization, adjuster support and policyholder communications, usually faster than normal change processes allow. Controls designed for steady volumes can be bypassed for speed, overloaded, or applied unevenly across teams and tools.

    The exposure falls into four areas: sensitive policyholder data reaching a model or a tool, claim-related outputs used without review, tool calls that no one authorized, and an inability to show a regulator what the AI was permitted to do and what it did during the event.

    This guide stays at the level of individual interactions and tool calls. It separates three questions: what policy was approved in advance, what was enforced in the execution path during the surge, and what evidence remains afterward. Treating them separately keeps a policy document from being mistaken for operational control.

    Before the surge: set an action for each activity

    Start with an inventory of LLM-assisted claims activities and map each to the claim administration and case management areas that the NAIC Model Bulletin says an AI Systems Program should cover. Then define an action-level policy for each, approved by named governance owners and versioned. NAIC working group materials refer to approval thresholds by action and to prompt governance and versioning, which supports this structure. The thresholds themselves are your decision.

    The table below is a starting position, not a regulatory requirement. NAIC working group commentary suggests that summarizing adjuster notes is lower risk than triage that influences which cases get review, because the adjuster can inspect the underlying entries. That is commentary, not a safe harbor.

    • Intake: Redact identifiers not needed for the task. Rate limit by channel. Block tool calls that write to the claim record without approval.
    • Triage and routing: Escalate for human approval where output affects which claims receive review or in what order.
    • Document summarization: Allow with redaction. Confirm the reviewer can open the source documents.
    • Adjuster support: Allow reads within the adjuster's entitlements. Block tool calls outside an approved list.
    • Policyholder communications: Escalate for human approval before anything that states a coverage or payment position is sent.

    Mapping to NAIC, NYDFS and Colorado

    The NAIC Model Bulletin, adopted in December 2023, expects an AI Systems Program that addresses governance, risk management controls and internal audit, and it names claim administration and payment among the covered uses. NAIC's AI Systems Evaluation Tool, still in pilot, asks for the AI governance framework, including risk mitigation, internal controls and the process for relying on third-party AI systems and data. State adoption varies, so confirm how the bulletin applies in each state where you write business.

    NYDFS Circular Letter 7 (July 2024) expects a corporate governance framework for oversight of AI systems and refers to third-party vendor contract terms drawn from the NAIC bulletin. As of October 2026, its definition of AI systems is tied to underwriting and pricing, so apply it to claims as a reference for governance structure rather than as a stated claims duty, and confirm scope with counsel.

    For Colorado SB 24-205, have counsel map any obligations that reach your claims use cases to the same evidence set. A single record of policy, enforcement decisions and escalations lets you answer each regime from one source rather than rebuilding it per request.

    Where runtime enforcement tooling fits

    Trussed enforces policy in the execution path across models, agents, tools and MCP servers, applying allow, redact, block, rate limit and escalate for human approval to individual interactions and tool calls. That enforcement produces audit-ready evidence mapped to NAIC and NYDFS, and it can be deployed self-managed in the customer cloud with configurable retention and data residency. Trussed has run continuously in production in a regulated enterprise for more than 18 months, with 99.95% uptime on live production traffic.

    The scope is deliberate. It covers enforcement and evidence at the interaction and tool-call level. Your pre-surge policy decisions, reviewer staffing and legal scoping remain yours, and the steps in this guide apply whatever tooling you use.

    Three phases of surge governance

    Before

    Scope activities, set action-level policy, test at expected peak volume.

    During

    Enforce policy on each interaction and tool call. Record any exception with approver and time.

    After

    Assemble evidence of what the LLM was permitted to do and what it did.

    Test your surge controls before the next event

    Review how policy enforcement and audit-ready evidence work for LLM interactions and tool calls in claims workflows.

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