Artificial intelligence is now embedded in decisions across finance, health, retail, and public services. With the upside comes exposure: model errors, bias, data misuse, and unpredictable failures. Insurers are beginning to write AI-specific cover, but as with car or home insurance, protection won’t be automatic. To secure a policy that is both affordable and viable for insurers, organisations will need to show reasonable precautions through robust governance, a living AI policy, and evidence that requirements are followed in practice.
The idea of insuring against AI risks may sound futuristic, but real-world harms are no longer hypothetical. From credit scoring to recruitment and medical imaging, failures are already creating measurable impacts. This shift explains why insurers are now exploring AI-specific products, and why businesses must increasingly demonstrate that they are managing these risks responsibly.
Lessons from other insurance products
The insurance industry has always operated on the principle of reasonable precautions. A policyholder must take steps to protect themselves and their property, otherwise the insurer may decline a claim. These expectations are explicit in policy terms and not left to interpretation.
- Car insurance – policyholders are expected to lock the doors, remove the keys from the ignition, and avoid leaving valuables visible. If a car is stolen while the engine is left running or the keys are inside, insurers will reject claims on the grounds of negligence.
- Home insurance – insurers require homeowners to close windows and lock all external doors when leaving the property. Many policies also specify the use of approved locks. If entry was gained through an open window or an unlocked door, theft claims can be denied. Some insurers also mandate working smoke alarms as a condition for fire coverage.
- Travel insurance – travellers must take reasonable care of their belongings, such as keeping passports and valuables on their person or in a hotel safe. Losses that occur because items were left unattended, for example, on a sunbed at the beach, are commonly excluded.
- Health insurance – policyholders must accurately disclose pre-existing medical conditions when applying for cover. Failure to disclose such information may result in claims being refused or the policy being voided entirely.
These examples illustrate a clear pattern: insurance is not designed to cover reckless or preventable losses. Instead, it assumes that the insured party will act responsibly, follow mandatory controls, and reduce the likelihood of avoidable claims.
Economic viability of AI insurance
For AI insurance to succeed, the policy must balance two interests: it must be affordable and worthwhile for the business while remaining sustainable and profitable for the insurer. Without this balance, either premiums will be prohibitively high, or insurers will withdraw cover altogether.
For businesses:
- Predictable costs – Companies want insurance to protect against catastrophic or unexpected AI failures, not to replace routine risk management. If premiums are too high, businesses will either self-insure (absorbing risks internally) or avoid purchasing cover altogether.
- Fair pricing for good governance – Businesses that can demonstrate alignment with ISO/IEC 42001, robust AI policies, and effective lifecycle controls should be rewarded with lower premiums. This mirrors the way homes with burglar alarms or cars with immobilisers qualify for discounts.
- Avoidance of hidden gaps – Businesses need clarity on what is excluded. “Silent AI exclusions” in cyber, professional indemnity, or liability cover can lead to gaps that make coverage ineffective. A policy is only economically viable if it provides genuine protection against the risks the company faces.
- Support for compliance – As AI regulations tighten (e.g., under the EU AI Act), insurance aligned with those requirements helps companies offset compliance costs by reducing the financial impact of failures or incidents. This strengthens the value proposition of cover.
For insurers:
- Risk selection and underwriting discipline – Insurers need assurance that customers are acting responsibly. Without this, claims could spiral in frequency and size, making AI policies unprofitable. Structured frameworks like ISO/IEC 42001 or mappings to the NIST AI Risk Management Framework provide underwriters with measurable checkpoints.
- Loss prevention through controls – By requiring baseline precautions, such as model validation, bias testing, and incident response plans, insurers reduce the likelihood of avoidable losses. This preserves the claims ratio and ensures premiums reflect true residual risk rather than gross exposure.
- Pricing for uncertainty – AI carries novel risks such as systemic bias, cascading errors, or IP infringement at scale. Insurers must price in these uncertainties while avoiding premiums so high that they deter buyers. The only way to narrow the pricing gap is to insist on strong governance evidence from policyholders.
- Limiting systemic exposure – Some AI failures could create correlated losses across many policyholders (e.g., reliance on the same third-party model provider). To remain viable, insurers may cap aggregate exposures, share risk through reinsurance, or demand that businesses diversify suppliers.
The shared value proposition rests on balance. For businesses, AI insurance must deliver affordable premiums, meaningful protection, and the reassurance that one failure will not destabilise financial resilience. For insurers, it must reduce uncertainty, limit avoidable claims, and ensure that payouts respond to unforeseen incidents rather than preventable negligence. The bridge between these interests is demonstrable governance. An AI policy aligned with ISO/IEC 42001, not just written but embedded across the organisation, provides the evidence of responsibility that makes the economics viable. It is this visible governance that enables insurers to underwrite sustainably and allows businesses to access cover with confidence.
Artificial Intelligence Impact Assessment (AIIA)
Insurance ultimately exists to protect people, not just balance sheets. For AI, that means insurers will increasingly look beyond technical controls to the human and societal impacts of algorithmic decisions. An Artificial Intelligence Impact Assessment provides the missing bridge:
Individuals:
- How are people affected if the AI system fails?
- Are there safeguards against discrimination, wrongful denial of services, or reputational harm?
- Insurers will expect evidence of user-centric risk analysis and redress mechanisms.
Groups:
- Does the AI system disadvantage certain demographics, communities, or professions?
- Could biases compound existing inequities?
- Demonstrating proactive bias testing and mitigation will be essential for cover.
Society:
- Could widespread adoption of the system cause systemic harm (e.g., destabilising financial markets, spreading misinformation, or eroding trust)?
- Insurers may cap exposure to such risks, but businesses must still show they have mapped and mitigated them.
The business:
- Beyond compliance, how would a failure impact on brand, trust, and financial resilience?
- AIIAs document these scenarios and demonstrate to insurers that risks are both understood and actively managed.
By embedding the AIIA into governance, businesses not only strengthen their insurance case but also align with regulators, stakeholders, and the broader social licence to operate.
Clearly defined AI policy
Insurers will expect companies to have a formal, leadership-approved AI policy that sets the tone for safe and responsible use of artificial intelligence. The policy should not only exist on paper but must be embedded into daily practice, communicated across the business, and reinforced by training and accountability.
- Leadership-approved and organisation-wide.
- Sets out principles of safe, lawful, and ethical AI use.
- Communicated to staff and backed by regular training.
- Assigns clear roles and responsibilities for oversight.
Defined scope and AI inventory
An organisation cannot manage what it does not know it has. Insurers will look for a clear inventory of AI systems that captures ownership, purpose, and risk classification. This inventory should help demonstrate that the business understands which systems are critical, which are high-risk, and how external stakeholder or regulatory expectations are being addressed.
- Comprehensive list of AI systems in use.
- Ownership and accountability documented.
- Risk categories assigned (low, medium, high).
- Stakeholder and regulatory requirements considered.
Structured risk assessment
Insurance is built on understanding risk, and insurers will expect businesses to do the same for their AI systems. Companies must carry out structured assessments of legal, ethical, operational, and security risks and ensure these are revisited whenever a model is retrained, repurposed, or deployed in a new context.
- Formal AI risk assessments completed and updated.
- Legal, ethical, operational, and security risks identified.
- High-risk systems subject to enhanced safeguards.
- Regular reviews triggered by system changes or retraining.
The Artificial Intelligence Impact Assessment (AIIA) covered earlier will also include risks to individuals, groups, society, and the business.
Operational resilience
Insurers will expect to see strong operational controls that span the entire lifecycle of AI systems, from design to retirement. This means having processes for responsible development, secure data handling, rigorous validation, controlled retraining, and clear points where human judgment is required.
- Documented lifecycle processes for design, training, deployment, monitoring, and retirement.
- Strong data governance (provenance, rights, privacy, and security).
- Validation for accuracy, bias, robustness, and reliability.
- Change control processes and retraining safeguards.
- Clear rules on human oversight and intervention.
Monitoring and incident readiness
Continuous monitoring is critical for keeping AI systems safe and reliable. Insurers will expect organisations to track performance, fairness, and compliance on an ongoing basis and to have an incident response plan ready for when things go wrong. That plan must be tested, so staff know how to act in real scenarios.
- Ongoing monitoring for accuracy, bias, and compliance.
- Documented incident response plan for AI-specific failures.
- Defined escalation procedures and responsibilities.
- Evidence of rehearsals or drills of the response plan.
Evidence and documentation
In disputes or claims, evidence matters. Insurers will expect companies to maintain detailed records that prove risks were managed responsibly. This includes documentation of risk assessments, testing results, monitoring reports, corrective actions, and audit trails of significant model changes.
- Documented records of all risk and control activities.
- Audit trails for model changes and decision-making.
- Retention of evidence to support claims or investigations.
- Corrective actions recorded and tracked to closure.
Leadership and accountability
Responsible AI cannot be delegated entirely to technical teams. Insurers will expect senior leadership to be visibly involved in oversight, ensuring that governance is a cultural priority. Named individuals must be accountable for AI risks, and there should be evidence that leaders actively drive a culture of responsibility.
- Senior management visibly engaged in AI oversight.
- Named individuals accountable for risk and compliance.
- Oversight committees or equivalent governance structures.
- A culture of responsibility supported from the top down.
Regulatory alignment
Compliance is no longer optional; it is a condition for trust. Insurers will expect companies to demonstrate that their AI systems meet legal obligations, such as data protection, equality laws, and sector-specific requirements. Businesses should also show they are prepared to adapt quickly as new regulations emerge.
- Systems designed to comply with data protection and discrimination laws.
- Alignment with industry-specific regulations.
- Processes in place to track regulatory change.
- Adaptation mechanisms to meet new legal requirements.
Competence and training
Even the best policies fail if staff lack the knowledge to follow them. Insurers will expect evidence that employees at all levels are trained to use AI responsibly. This includes broad AI literacy for general staff and deeper, role-specific training for developers, auditors, and risk managers.
- AI literacy training for all employees.
- Specialist training for developers, auditors, and oversight roles.
- Mandatory training tied to AI policy requirements.
- Records of training completion and effectiveness.
Ongoing review and improvement
AI governance must evolve with the technology itself. Insurers will expect companies to review their systems regularly, track and close corrective actions, and commit to continual improvement. This shows that businesses are not just meeting today’s standards but preparing for tomorrow’s risks.
- Regular reviews of AI risks, incidents, and performance.
- Documented management reviews with action tracking.
- Corrective actions logged and closed.
- Commitment to continual improvement and adaptation.
From risk transfer to building trust
Insurers will expect companies to show that they are treating AI with the same care as any other business-critical system. Strong policies, structured risk management, lifecycle controls, and a culture of responsibility will be seen as the “locked doors and smoke alarms” of AI, essential for affordable and dependable coverage.
AI insurance is not just about transferring risk, it is about building trust in a technology that is both powerful and unpredictable. Just as locked doors and smoke alarms became shorthand for responsible home ownership, AI policies, impact assessments, and lifecycle controls will become the baseline for responsible AI adoption. Insurers, businesses, regulators, and society all stand to benefit if these foundations are laid early.
Information security, risk management, internal audit, and governance professional with over 25 years of post-graduate experience gained across a diverse range of private and public sector projects in banking, insurance, telecommunications, health services, charities and more, both in the UK and internationally – MORE
