Artificial Intelligence (AI)

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.

ISO 42001:2023 places a duty on organisations adopting AI systems to understand the impact on individuals, groups of individuals, and society; both positive and negative. Artificial Intelligence Impact Assessments (AIIA) are not a formality, they exist to protect not only fairness and compliance, but human capability. This responsibility matters because technological erosion rarely happens suddenly. It doesn’t announce itself, it arrives quietly through comfort and convenience. A concern I have is the lack of balance between all the financial benefits that AI has to offer and the potential long-term consequences.

An AIIA should not only explore bias, privacy, and safety. It should also ask:

• What skills weaken if humans stop performing this task?

• Does this system build capability or remove the need for it?

• What are the societal implications if cognitive engagement declines?

• How do we avoid creating a workforce of operators instead of thinkers?

I experienced something similar as a child. I received my first computer at ten, and within months my handwriting had changed to look more like computer fonts. As a teenager and as a young adult, I walked or cycled everywhere. After graduating university, I needed a car to commute 20+ miles to work, and not too long after that I began driving everywhere. Even a short walk to buy a newspaper started to feel unnecessary.

I recall watching Idiocracy, a satirical film set in a future where society slowly abandoned curiosity, learning, and thoughtful decision making, leading to a population that couldn’t solve basic problems. Joe Bauers, an average man from the present day, wakes up centuries later as the smartest person alive because he still possessed basic reasoning skills.

In no way should this article suggest that AI is bad. AI can simplify decision making, accelerate work, and make life more efficient in a way that amplifies human capability. It can also weaken both skills and independence if society over-relies on AI as a substitute for thought rather than a support for it. The danger is not intelligence disappearing overnight. It is a slow, comfortable slide into convenience where fewer people understand how things work, fewer question information, and fewer develop deep expertise because automated systems handle everything; a potential gradual drift into the world that Idiocracy mocks.

A healthier path is to use AI as a partner, not as a replacement. We can let tools handle repetition while we apply judgment, creativity, and continuous professional development. There is much discussion on AI taking over the world, and parallels to Terminator and John Connor, but we shouldn’t ignore the possibility that civilisation could sleepwalk into a future where the smartest person in the room is the one who can still think for themselves. The future belongs to those who can think with machines rather than depend on them.

In publishing, where authority and trust are paramount, ethics can be both a guiding light and a minefield. Artificial Intelligence is reshaping how content is written and published, offering unprecedented efficiency but also raising ethical questions. This article explores how AI use in publishing can either support expert work or simulate it for influence or profit, often with damaging consequences.

• Use of AI to enhance the work of expert writers and publishers – AI can support skilled professionals by streamlining research, suggesting improvements, and accelerating drafting processes. This allows authors, editors, and publishers to produce higher-quality content more efficiently while retaining creative and authoritative control.

• Use of AI to fake expertise in writing and publishing for profit or influence – AI tools can generate polished, authoritative-sounding text, enabling individuals with little subject knowledge to publish materials with no means to verify accuracy, such as sites with a primary purpose of generating advertising or referral-related revenue. Inaccurate or misleading information undermines trust, distorts public discourse, and floods the market with low-quality or deceptive content while projecting authenticity.

Ethical AI use enhances productivity while ensuring that human expertise, critical judgment, and accountability remain central to the publishing process. The threshold for ethical AI use is clear to me: AI should assist experts, not create expertise, and the authors should be the subject matter experts and able to write the article independently, even if they choose to use AI to enhance productivity, clarity, and efficiency.

Ethical AI in Support of Human Expertise

When used ethically, AI can enhance the capabilities of skilled professionals without compromising integrity or authority. These are examples of how AI can respectfully support subject matter experts in the publishing process:

• AI-assisted outlining and structuring – Subject matter experts can use AI to organise ideas, generate summaries, or improve coherence, allowing them to focus on deep analysis and insights.

• Improved productivity without sacrificing integrity – AI helps streamline tasks like grammar correction, rewording, and formatting, reducing the time spent on administrative aspects of writing.

• Experts are responsible for factual accuracy – AI may provide suggestions, but final validation, critical thinking, and real-world expertise shape the published work.

• Refinement without compromising meaning – AI tools can enhance readability, correct errors, and optimise for audience engagement while preserving the writer’s original message and intent.

Where Ethical Boundaries Are Crossed

Unfortunately, AI is often used not to enhance genuine expertise, but to simulate it. This approach introduces risk, erodes trust, and undermines professional standards. Misuse includes bypassing real subject knowledge, misleading audiences, or generating content purely for financial gain, regardless of accuracy or credibility. AI should never be used to publish work that the author couldn’t understand, write, or explain without it.

• Mass-production of AI-generated content without subject knowledge – Using AI to generate numerous articles on specialized topics without subject-matter expertise leads to shallow and misleading content.

• Plagiarism and misinformation risks – AI can fabricate facts, misinterpret sources, or produce content that closely resembles existing material, raising ethical and legal concerns.

• Deception and false authority – Presenting AI-generated work as if written by an expert misleads readers and erodes trust in professional knowledge.

• Revenue-driven content farming – Some use AI to create high volumes of low-quality content, designed solely to rank on search engines and generate advertising revenue, regardless of accuracy or reader value.

• Automated publishing without human oversight – AI lacks ethical judgment, industry experience, and the ability to apply critical judgement in context, making unchecked AI-generated content prone to serious errors and misleading claims.

Ethical AI use supports expertise, boosts efficiency, and preserves credibility. In contrast, misuse leads to misinformation, low-quality content, and eroded trust in professional knowledge. AI is a powerful assistant but human expertise remains irreplaceable. As AI continues to evolve, so too must our standards for credibility, authorship, and trust. In a world where anyone can publish, the true measure of value lies not in how content is created, but in who stands behind it.

I am pleased to report that I completed the next stage of my journey to become an AI subject matter expert. I passed the ISO 42001 Lead Auditor exam. Although I currently qualify only as a PECB Certified ISO 42001 Provisional Auditor, I can upgrade this from Provisional Auditor to Auditor later this year.

This journey has included attending seminars, reading news articles, conversations with other professionals, and generally trying to stay informed and remain current in a rapidly evolving field. This article summarises professional concerns and forms a core part of delivering governance of artificial intelligence. The extent to which these risks and concerns already exist and unfold daily is open to debate and not part of this article. I leave this with you to consider.

Misinformation and disinformation

AI can create and amplify false or misleading information at an unprecedented scale, threatening trust in media and democratic institutions.

• AI models can generate thousands of fake articles, social media posts, or reviews in seconds, tailored to spread specific narratives, making manipulating public opinion easier for bad actors.

• AI can create realistic videos or audio clips of individuals saying or doing things they never actually did for purposes such as blackmail, propaganda, or to discredit public figures.

• AI-powered automated bots can hijack social media platforms, amplifying false narratives or silencing dissenting voices.

• As AI-generated content becomes more challenging to distinguish from genuine material, people may lose trust in legitimate sources of information, leading to societal instability.

• State-sponsored actors could leverage AI to influence elections, destabilise economies, or create population discord.

Bias and discrimination

AI systems are only as unbiased as the training data. Without careful oversight, they can perpetuate or even exacerbate discrimination.

• AI learns from historical data, which often reflects societal inequalities. Recruitment algorithms, for example, trained on biased data might favour specific demographics over others.

• Without transparency in AI decision-making processes, it is challenging to identify and address discriminatory outcomes.

• AI tools and solutions developed by teams with limited diversity can lead to blind spots in understanding and addressing diverse needs.

• Companies deploying biased AI systems can face reputational damage, lawsuits, and regulatory scrutiny.

Job displacement and economic impact

AI is transforming the job market, raising concerns about unemployment and economic inequality.

• Routine manufacturing, logistics, customer service, and transportation jobs are highly susceptible to automation. Self-driving vehicles could replace millions of drivers, for example.

• Transitioning displaced workers into new roles requires significant training programs and education investment. The lag between technological advancement and workforce adaptation is an important concern.

• AI may disproportionately benefit those who own and develop the technology, widening the gap between low and high-income groups.

• While AI boosts productivity, the economic benefits may not translate into job creation, potentially leaving millions without viable employment.

Privacy

AI systems thrive on data, but this dependency raises concerns about privacy violations, unethical data usage, and mass surveillance.

• Companies and governments could collect vast amounts of personal data to train AI models without explicit consent.

• AI-powered surveillance tools like facial recognition cameras can track movements and activities, often infringing on civil liberties.

• The centralisation of data for AI training can increase the risk of breaches, exposing sensitive information to hackers.

• Using AI to analyse and link disparate data sources can make it nearly impossible for individuals to remain anonymous.

Loss of control

As AI systems grow more sophisticated, there is increasing concern about their autonomy and the potential for catastrophic misuse.

• Advanced AI systems may act in ways their creators did not anticipate, potentially causing harm in critical areas such as healthcare or transportation.

• AI-driven weapons could operate without human intervention, raising ethical and strategic dilemmas, including the potential for accidental escalation of conflicts.

• When AI surpasses human intelligence, it might prioritise itself over the well-being of humanity, leading to existential threats.

• Many AI algorithms are complex and opaque, making it challenging to understand decision-making processes. This lack of transparency can lead to dangerous or harmful outcomes.

• Governments and organisations struggle to keep up with the pace of AI development, creating a gap in oversight that could allow harmful applications to flourish.

With international cooperation, proactive regulation, ethical development, and public awareness, we can collectively address these risks and shape a safer, more trustworthy AI future.