Controlling Generative AI Spend in Insurance Claims by Enforcing Usage Policy in the Execution Path

Where spend-driving usage originates in claims

Generative AI consumption in claims is a product of workflow design more than of any single model choice. NAIC materials list claims uses such as accident image analysis, settlement value estimation, fraud detection, triage and fast-tracking, reserving, salvage and subrogation, and report ordering. Add intake, document summarization, coverage analysis and adjuster assistance, and a single claim can touch several models and tools.

Volume can be large. Written testimony describes one claims TPA whose generative AI tool produced more than 50,000 medical summaries in three months, each reviewed by examiners. That is one example, not a benchmark, but it shows how a routine task multiplies.

Four patterns commonly drive unplanned consumption, and each can be bounded at a specific point. Repeated calls over the same document can be limited by a per-workflow rate limit at the request. Agent retries and tool loops can be limited by a cap on tool invocations per task. Routing across multiple models can be limited by a model allowlist per workflow, applied where the route is chosen. Oversized inputs can be limited by input size rules before the request reaches the model. Start by inventorying which claims workflows generate calls, who owns them and which models and tools each one reaches.

Why enforcement placement matters more than budgeting

A monthly budget review reports consumption after it has happened. A manual approval queue acts before consumption but adds a wait to every request it covers. In claims, where intake and adjuster assistance are time-sensitive, the first control arrives too late and the second slows the work. Teams that meet the second with friction often route around it, which creates unmonitored usage.

NAIC Big Data and AI Working Group material says earlier controls still matter but are not enough on their own. It names generative AI input and output controls, data-boundary controls, approval thresholds by action, and documented responses to threshold breaches. It also flags control lag and controls that sit outside the day-to-day workflow as concerns.

Enforcing policy in the execution path addresses both problems. The decision is made on the request itself, before it reaches the model or tool, using attributes such as caller identity, workflow, target model, target tool, data classification and current usage against a limit. Evaluating rules against those attributes is a small step compared with a model call. No published figure is assumed here, so measure the added latency in your own environment against your throughput targets before rollout. Keep release-time thresholds, such as go/no-go decisions for deploying a use case, separate from the runtime thresholds that bound each call.

Define policy outcomes at the interaction and tool-call level

Each outcome answers a different question about a call. The mapping below is a drafting choice for claims workflows. Adjust it to your own risk assessment and approval thresholds by action type.

• Allow: Routine, in-policy calls such as summarizing a claim file within the caller's workflow and limits. Enforced inline with no human step.
• Redact: Calls that carry claimant data not needed by the target model. Fields are removed or masked before the request leaves the data boundary.
• Block: Calls to a model or tool not approved for that workflow, or inputs that breach a defined rule. Enforced inline with a recorded reason.
• Rate limit: High-volume or repetitive traffic, such as repeated summarization of the same file or runaway agent retries. Excess calls are slowed or refused, and the rest continue.
• Escalate for human approval: Exceptional actions defined by threshold, such as a tool call that changes a claim record or a call above a set usage level. Only that action waits for an approver.

Preserve claims turnaround and produce audit-ready evidence

Inline enforcement and escalation should not compete. Decide which scenarios need which. High-volume, repeatable interactions with predictable risk belong in inline decisions, because a person cannot review them at the speed of the workflow. Rare, consequential or out-of-pattern actions belong in escalation. Design escalation with defined triggers, a named approver group and a recorded outcome, rather than an ad hoc queue. Hold the single action that triggered the threshold, not the claim or the adjuster's other work. Where claims context calls for human review, as with examiner review of generated summaries, keep it in the design.

Platform teams should also keep the fast path the easy path. If one policy covers every model, tool and team, there is less reason to adopt an unmonitored alternative.

The same decisions are your evidence. The NAIC bulletin states that insurers remain responsible for compliance when they use AI, and that regulators may require explanations of AI use in claims. It also tells insurers what information a Department may request in an examination. NYDFS Circular Letter 7 (2024) expects documented processes for identifying operational, financial and compliance risks and the internal controls that mitigate them, proportionate to materiality. Its title scopes it to underwriting, though the pattern of documented risk processes and controls carries over to claims. As of October 2026, the Colorado SB 24-205 summary describes disclosure duties to the attorney general within 90 days after discovery of algorithmic discrimination, and a compliance pathway for insurers tied to commissioner rules.

None of these prescribes a log format. A practical record per interaction and tool call includes the caller, workflow, model or tool, policy version, outcome, reason and, for escalations, the approver and result. Add the documented response to any threshold breach. That shows what the AI was permitted to do and what it did.

How Trussed fits

Trussed is control, provenance and evidence infrastructure for consequential AI, built for regulated enterprises that must show what their AI was allowed to do and what it did. It enforces policy in the execution path across models, agents, tools and MCP servers, with allow, redact, block, rate limit and escalate for human approval applied to individual interactions and tool calls. Policy is enforced at runtime, not reviewed after the fact.

That enforcement generates continuous audit-ready evidence, mapped to NAIC, NYDFS, HIPAA, the EU AI Act and NIST. Trussed deploys self-managed in the customer's cloud, with configurable retention and data residency.

Insurance is a proven regulated vertical for Trussed. It runs 100+ models under one policy plane and has delivered 99.95% uptime on live production traffic. Trussed is a runtime control layer for regulated AI use, not a budgeting or reporting tool.