How AI Insurance Could Make Assurance a Business Necessity

The AI Assurance Initiative | Insurance, Part 2

What happens when companies that can demonstrate the effectiveness of their AI risk controls receive appropriate recognition in insurance? The criteria for choosing AI could extend beyond performance and the initial price.

The question would no longer be only whether to spend more on safeguards. It would also be: could operating AI without the evidence to support those safeguards prove more expensive overall?

Our first article, “Making AI Risk Controls Count in Insurance,” proposed connecting verifiable evidence about AI controls and operations with underwriting. This article explores the market that could emerge if that connection becomes established in practice.

1. Technical assessment already influences insurance

The idea that insurance assesses risk before an incident is not new. Munich Re’s aiSure™ FAQ states that model quality and performance stability can influence premiums. Technical characteristics already inform an AI insurance offering.1

An April 2026 report from Japan’s Financial Services Agency and Ministry of Economy, Trade and Industry calls for stronger underwriting and identifies limited loss data as a challenge for emerging risks, including AI and IoT.2

Neither source requires the adoption of a particular AI assurance technology. What interests us is the potential for verifiable evidence about risk controls to contribute to an insurer’s assessment of a business.

What follows is a forward-looking scenario for how that assessment could influence the market.

2. From “we have controls” to “this is what we can demonstrate”

Consider two businesses using similar AI systems to plan deliveries.

One can provide evidence about whether required approvals were followed and whether processing was held when necessary information was missing. The other can describe its controls but cannot adequately demonstrate how they operated in the relevant workflow.

That difference alone does not establish that the second business is unsafe. It does, however, create a difference in the information available for underwriting.

If an insurer can assess the effectiveness of those controls in relation to the relevant risk, and reflect the findings in coverage decisions or terms, the ability to demonstrate them acquires economic value. The implications extend beyond premiums to the availability of needed coverage and the losses a business must retain.

The selection criterion could become not simply whether an AI system is described as safe, but what can actually be demonstrated about its safeguards — and within what limits.

3. When going without assurance becomes the more expensive choice

The business decision we envisage is not a comparison of AI acquisition costs alone.

If safeguards and verification reduce the overall burden after accounting for their own costs, premiums, retained losses, and the effort involved in obtaining insurance, investing in assurance becomes rational. Conversely, an intervention whose costs exceed its benefits should not be imposed indiscriminately.

The Japanese report also discusses assessing cost-effectiveness through Total Cost of Risk and sharing corporate risk analysis with insurers to help optimize coverage and premiums.2

We want to apply that perspective to AI adoption.

Safeguards are not valuable only because they might earn a premium discount.When the full cost of operating AI is considered, going without them — and without evidence of their effectiveness — could become the less economical option.

This is not a claim that such a pricing differential is already established. It is a business case that could emerge after the effectiveness of controls, the cost of verification, and their treatment in underwriting have been tested together.

4. From underwriting criteria to AI procurement requirements

If that relationship becomes established, the source of demand could change too.

Assurance providers would not have to rely solely on persuading engineering teams of the value of safeguards. Management, finance, and procurement teams could also start asking for systems capable of supplying the evidence required for underwriting. AI vendors might then be assessed not only on performance and price, but also on what can be demonstrated about operational controls.

Insurance could create an economic incentive for AI assurance, and that incentive could influence procurement requirements. This is the next step beyond the proposal in our first article.

The assessment should not become a one-time certification. NIST’s AI RMF addresses deployment-relevant evaluation, monitoring in production, and limits on generalizing beyond evaluated conditions.3

We envisage more than receiving a certificate at implementation. As systems and operations change, businesses would examine which findings remain applicable and make that information available for underwriting and renewal discussions.

In that cycle, AI assurance — examining the operation and limitations of safeguards and producing evidence for decisions — could move from an optional add-on toward infrastructure with a clear economic role in doing business.

5. The relationship between evidence and loss risk must be tested

More evidence does not necessarily mean less risk. Verification may expose a significant weakness and indicate a need for further controls rather than improved insurance terms.

Joint work should examine which controls relate to which losses, what the evidence adds to underwriting, and whether verification costs are proportionate to the benefits. The objective is to complement historical loss data, actuarial analysis, and existing risk assessments — not replace them.

Nor should the objective be to exclude businesses that cannot yet produce the necessary evidence. It should be to clarify what needs to be improved and demonstrated so that an assessment can progress and effective investment can be recognized. We believe insurance evaluation should turn on the controls and risks that can be assessed, rather than the name of a particular technology vendor.

6. The infrastructure GhostDrift aims to build

GhostDrift Mathematical Institute aims to support this market by developing technology that helps examine AI safeguards and connect the findings with evidence insurers can use in their decisions.

We see the opportunity as more than selling a verification tool. We want to help connect businesses using AI, technology providers, assessors and auditors, insurers, and reinsurers through relevant evidence — so that investment in risk controls and underwriting assessment can reinforce one another.

We have filed a related Japanese patent application: Japanese Patent Application №2026–210256. Technical details would be discussed individually under confidentiality arrangements.

We welcome joint exploration with insurers, reinsurers, and insurance brokers, starting with a defined workflow and examining the relevant controls, evidence, and cost-effectiveness. This is not an announcement of an available insurance product, a specified discount, or an agreed requirement to adopt GhostDrift technology.

From safeguards that are recommended to safeguards that make business sense to adopt.

Insurance could provide the connection. That is the market opportunity at the heart of our AI assurance initiative.

Primary sources and public materials

1 Munich Re, “Frequently Asked Questions — Insure AI / aiSure™.”Relevant section: the question on machine-learning model types and premiums.Official FAQ

2 Japan’s Financial Services Agency and Ministry of Economy, Trade and Industry, report of the study group on enhancing corporate risk management, April 17, 2026.Relevant sections: III-2 and the annex on a common understanding of enhanced corporate risk management, section 2(1). Japanese-language publication; the report title is rendered descriptively in English.Full report

3 NIST, “Artificial Intelligence Risk Management Framework (AI RMF 1.0),” January 2023.Relevant sections: MEASURE 2.3–2.5.Official publication (PDF) / Official online AI RMF Core

[4] IAIS, “Application Paper on the supervision of artificial intelligence,” final version published July 2, 2025. Supplementary reference.Background on supervision of insurers’ own use of AI. Section 1.3.1 excludes insurance risks arising from AI within insured businesses. It is not evidence that this underwriting proposal or any particular technology is mandatory.Final paper

Sources checked September 23, 2026. Source-supported developments are distinguished from the forward-looking scenario and business proposal in this article. References do not imply endorsement, approval, or partnership by the organizations cited.