Understanding AI Insurance and the role it plays in AI Assurance

Understanding AI Insurance and the role it plays in AI Assurance

As AI becomes more prevalent, organisations face two interconnected challenges: ensuring their AI systems operate safely and reliably (AI assurance) and managing the financial risks when they don't (AI insurance).

AI Assurance: Bringing Responsible and Secure AI to Practice

AI assurance encompasses the practices, processes, and frameworks designed to ensure AI systems behave as intended, operate safely, and align with organisational values, ethical principles and regulatory requirements. This includes technical measures like bias testing, performance monitoring, and robustness validation, as well as governance practices like ethical review processes, audit trails, and accountability structures.

AI assurance aims to answer fundamental questions: Is this AI system making fair decisions? Can we explain how it reached its conclusions? Are there adequate safeguards against misuse or failure? Does it comply with relevant regulations and ethical standards?

In September 2025, the UK Government released the Trusted third-party AI Assurance roadmap which sets out four steps they will take to spur the growth and improve the quality of the UK's AI assurance ecosystem, as committed to in recommendation 29 of the AI Opportunities Action Plan. This follows from their Introduction to AI Assurance from February 2024 and Assuring a Responsible Future for AI, released in November 2024 which assessed the state of the UK AI assurance market and identified opportunities for future growth.

The Connection Between AI Assurance and AI Insurance

AI insurance and AI assurance are intrinsically connected through the challenge of risk allocation in our automated world. While AI assurance focuses on preventing problems before they occur, AI insurance provides financial protection when prevention efforts fall short.

This relationship creates a feedback loop: effective AI assurance practices reduce insurance premiums by demonstrating lower risk profiles, while insurance requirements drive organisations to implement stronger AI assurance measures. Insurance companies evaluate an organisation's AI assurance practices when determining coverage and pricing, making assurance not just a technical necessity or compliance box ticking exercise but an economic advantage by linking the safety and security of AI systems with the insurance premium.

Understanding this connection helps explain why AI insurance is emerging as a specialised form of coverage designed to address the unique risks that artificial intelligence creates in our increasingly automated world.

Understanding AI Insurance

AI insurance represents a new category of coverage that protects organisations against liabilities arising from their use of artificial intelligence systems. Unlike traditional business insurance that covers familiar risks like property damage or general liability, AI insurance addresses the novel challenges created when AI when used as intended produces harmful outcome. The coverage typically includes protection against discrimination claims from biased algorithms, errors in AI-driven decision-making, privacy violations from AI data processing, and business interruption when AI systems fail. Essentially, it covers scenarios where artificial intelligence systems cause financial, physical, or reputational harm to third parties.

The Liability Challenge in Automated Systems

One of the most complex aspects of AI-related incidents is determining responsibility. Traditional liability frameworks assume clear lines of accountability, when a human makes a decision that causes harm, we know who to hold responsible. AI systems complicate this picture significantly.

Consider an AI hiring algorithm that discriminates against certain candidates. Who bears responsibility? The company that deployed the system? The software vendor that developed it? The data scientists who trained the model? The organisations that provided the training data? This creates what experts call the "multiplayer accountability problem", responsibility becomes distributed across multiple parties in ways that traditional legal frameworks struggle to address.

To support this challenge, AI models could come with documentation of assurance techniques including data cards that detail dataset origins and biases, system cards that outline model architecture and limitations, and audit cards that quantify regulatory risks for independent verification. This structured information enables insurers and other stakeholders to properly assess and underwrite AI-related risks, creating greater transparency and accountability in AI deployment.

This challenge mirrors similar issues we see with AI-generated content and intellectual property rights, where determining authorship and ownership becomes complex when multiple parties contribute to an AI system's output.

Insurance as a Risk Allocation Mechanism

AI insurance functions as more than just financial protection, it serves as a practical mechanism for allocating risk in our automated world. When AI systems cause harm, insurance provides immediate compensation to victims while legal systems work to establish longer-term frameworks for liability.

The insurance industry approaches this challenge through sophisticated risk assessment methodologies. Insurers analyse factors such as the type of AI system being used, the decisions it makes, the potential for harm, and the safeguards in place. This analysis results in premium pricing that reflects the actual risk profile of different AI applications.

Market-Driven Safety Standards

An important development in AI insurance is how it creates market incentives for safer and more secure AI development and deployment. Insurance premiums effectively price different levels of AI risk, organisations with robust AI governance, testing protocols, and monitoring systems pay lower premiums than those with weaker safeguards.

This pricing mechanism creates a natural economic incentive for organisations to invest in AI safety measures. Companies that implement comprehensive model risk management of AI models akin to SR-11-7 / SS1/23, maintain detailed audit trails, and establish clear governance protocols find themselves rewarded with lower insurance costs.

Insurance companies also require specific safety demonstrations before providing coverage. Policyholders must show evidence of proper AI system validation, ongoing monitoring procedures, and incident response capabilities. These requirements effectively create industry standards for AI assurance that operate through market mechanisms rather than regulatory mandates.

The Role of Actuarial Science in AI Risk

The insurance industry's expertise in actuarial risk assessment proves particularly valuable in the AI context. Insurers have centuries of experience in quantifying and pricing various types of risk, and they're now applying this expertise to artificial intelligence.

Actuarial analysis of AI risks involves examining historical data on AI incidents, analysing the probability of different types of failures, and assessing the potential severity of various AI-related harms. However, this approach faces significant challenges due to the lack of historical data and rapid pace of innovation in AI systems.

In the absence of claims history, underwriters cannot rely on empirical loss distributions and must instead resort to theoretical modelling or simulation-based methods to estimate risk from first principles. For severity assessments, insurers may sometimes use historical claims involving human decision-makers or legacy technologies as proxies, for example, evaluating the financial consequences of a misdiagnosis or underwriting error, to estimate the potential cost of a similar mistake made by an AI model.

Despite these methodological adaptations, this analytical approach helps establish more accurate pricing for AI insurance while also identifying the most significant risk factors that organisations should address.

Current State and Future Evolution

AI insurance is still an emerging field, with policies and coverage options evolving rapidly as both insurers and organisations gain more experience with AI-related risks. Early policies tend to focus on the most obvious risks, discrimination in hiring algorithms, errors in financial decision-making systems, and privacy violations in data processing.

As the field matures, we can expect to see more sophisticated coverage options that address emerging AI applications like autonomous systems, advanced medical diagnostics, and complex automated trading systems. The insurance industry is also developing better methods for assessing AI risks and pricing coverage appropriately.

With academic articles titled 'Insuring AI: Incentivising Safe and Secure Deployment' forthcoming and a new £2m academic-industry partnership between Axa and University of Edinburgh set to develop novel methods to understand, measure, and ultimately insure against risk associated with the commercial application of artificial intelligence this space is set to grow.

Practical Implications for Organisations

For organisations using AI systems, understanding AI insurance involves recognising both the direct benefits of financial protection and the indirect benefits of improved risk management practices. The process of obtaining AI insurance typically requires organisations to conduct thorough assessments of their AI systems, document their safety procedures, and implement ongoing monitoring protocols.

This process often reveals gaps in AI governance that organisations might not have otherwise identified. Many companies discover that obtaining AI insurance helps them develop more comprehensive approaches to AI risk management, even beyond what the insurance requires.

The Intersection of Insurance and Regulation

AI insurance operates in a space where regulatory frameworks are still developing. While governments work to establish comprehensive AI regulations, insurance provides a parallel mechanism for managing AI risks through market-based approaches.

This creates an interesting dynamic where insurance requirements may actually precede regulatory mandates, effectively setting industry standards before formal regulations are established. Organisations may find themselves adopting AI safety practices to meet insurance requirements that later become regulatory requirements.

Looking Forward

AI insurance represents more than just a new product category, it reflects a broader shift in how society manages the risks associated with artificial intelligence. As AI systems become more capable and more prevalent, the need for sophisticated risk management approaches becomes more critical.

The evolution of AI insurance will likely mirror the evolution of AI technology itself, with coverage options becoming more sophisticated as new applications emerge and as we develop better understanding of AI-related risks. Organisations that engage with AI insurance early will not only protect themselves from potential liabilities but also position themselves to benefit from the risk management insights that the insurance process provides.

Understanding AI insurance means recognising it as both a protective mechanism and a tool for improving AI governance practices, a dual role that may prove essential as artificial intelligence continues to transform business and society.