AI hallucinations are moving out of the technical handbook and into the boardroom, the courtroom, and the insurance industry. As enterprises deploy generative AI across critical operations, an uncomfortable question is beginning to define the next chapter of the AI era: when the machine gets it dangerously wrong, who carries the cost?

by Kasun Illankoon, Editor in Chief at Tech Revolt

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There is a scene that has played out, with slight variations, in courtrooms and compliance offices around the world over the past two years. A professional, a lawyer, a financial adviser, a medical information service, relies on a generative AI tool to produce output that goes directly to a client, a regulator, or a court. The output is confident. It is also wrong. And by the time anyone notices, the damage is already in motion.

The most cited early example is Mata v. Avianca, the 2023 US federal case in which a lawyer submitted legal briefs containing entirely fabricated case citations generated by ChatGPT. The lawyer was sanctioned. The AI tool was not. That distinction, seemingly obvious on the surface, turns out to be one of the most consequential fault lines in the emerging legal debate around artificial intelligence: when an AI system produces harmful, misleading, or commercially damaging output, where exactly does the liability land?

For a growing cohort of insurers, legal experts, risk consultants, and enterprise leaders, this question is no longer theoretical. It is becoming, with considerable urgency, a matter of policy design, coverage architecture, and corporate governance.

The Insurance Market Is Already Moving

The idea of insuring against AI hallucinations might sound like the domain of future-facing risk panels and speculative white papers. The reality is that the market is already moving, quietly and with increasing speed, toward products that address exactly this category of exposure.

Magdalena Konig, General Counsel at Sirius International Holding and a specialist in advanced technology law with a qualification in AI Ethics, is direct about where things stand.

"They already are," she says, when asked whether AI hallucinations could become a recognised insurable business risk on par with cyberattacks or data breaches. "Whilst a standalone comprehensive cover is difficult to craft, the insurance market is moving quickly to keep pace with new technology risk, like AI hallucinations and model drift."

Konig points to policies already being written by insurers including Libertate Insurance LLC and Chubb, which offer coverage spanning regulatory fines through to third-party lawsuits. More immediately available, she notes, are products covering specific AI-related loss scenarios rather than hallucination as a categorical coverage trigger. The examples she gives are telling: policies covering chatbots swearing at customers, or the now well-publicised case of an AI virtual assistant for an airline giving passengers incorrect information about bereavement fares.

The challenge, Konig explains, is structural. "A hallucination is hard to define as a trigger event, the legal framework for liability is still being built and actuarial data on hallucination frequency, severity, and loss patterns essentially doesn't exist in any standardised form yet."

What is emerging instead is a patchwork: professional indemnity extensions covering AI-assisted advice that produces wrong outputs, media liability policies covering AI-generated defamatory content, representation and warranty insurance for AI training data intellectual property issues in mergers and acquisitions, and errors and omissions extensions for AI-enabled professional services.

Significantly, Konig also flags the other direction of travel. "AI exclusions or AI sub-limits are being written too, and at a faster rate," she cautions, "so how much comfort is actually available is yet to be tested." The market is expanding coverage and contracting it simultaneously, which makes navigating AI risk insurance a materially more complex task than it might first appear.

The Liability Question That Most Enterprises Are Getting Wrong

Ask most enterprise technology leaders where liability sits when a generative AI system causes harm and they will, in most cases, point upstream. Toward the model developer. Toward the company whose name appears in the API documentation. This assumption, Konig argues, is largely incorrect, and the gap between what enterprises believe and how the law actually operates is itself a significant source of unmanaged exposure.

"The widespread assumption that liability flows upstream to developers is largely wrong," she says. The logic she applies is intuitive once laid out. "If a company sends a defamatory email, the company is liable, not Microsoft for providing Outlook."

In the Mata v. Avianca case, the lawyer was sanctioned, not the AI tool that generated the fabricated citations. The deployer, the entity that chose to use the tool, evaluated its fitness for purpose, supervised its output, and decided what to act on, is in the position of primary accountability.

Regulatory frameworks are reinforcing this architecture. The EU AI Act allocates most obligations to deployers of high-risk AI systems. The Colorado AI Act explicitly creates deployer obligations. The proposed EU AI Liability Directive, despite significant scaling back, does not fundamentally displace this structure either.

"Deployers should assume they bear the liability and Providers should assume they'll be named as co-defendants in significant cases regardless of contractual protections. Both should expect product liability to become increasingly important as a separate channel of exposure."

This reframing has practical consequences for how enterprises should be structuring their AI governance. It means that buying a model from a major provider and deploying it across customer-facing operations does not transfer risk upward. It concentrates it squarely in the hands of the organisation doing the deploying.

The Shadow AI Problem Nobody Is Talking About

If the liability question is being misread, the governance question is being underestimated in a different and arguably more dangerous way. Most enterprises know, at some level, that AI tools occasionally produce wrong outputs. The common response has been to add a caveat to acceptable use policies: do not rely on AI uncritically. For Konig, this kind of response fundamentally misreads the problem.

"We haven't built operational infrastructure that catches the cases where someone did rely on AI uncritically before the error reaches a customer, a regulator, a counterparty, or a court," she says.

The real threat, in her framing, is not the occasional hallucination in isolation. It is the organisational consequence pattern: the accumulated exposure created when AI-assisted decisions flow through operations without adequate review checkpoints.

There is a second, connected problem that she describes with notable precision. Employees are using AI tools that have not been sanctioned by their organisations, often with company data, in ways that create data leakage exposure and error propagation outside any governance perimeter.

"We only find out about it when something goes wrong," Konig observes. This shadow AI deployment problem is not a technology failure. It is an organisational design failure, and it is one that a clause in an acceptable use policy will not fix.

"Most enterprises are still treating AI as a productivity tool to layer on top of existing operations. By the time they recognise it as an operational substrate that requires redesigned governance, several quarters of accumulated exposure will already be in place."

The solutions she describes are not technical. They are structural: rebuilding review processes, decision documentation, knowledge management, and oversight workflows for an environment where AI is embedded throughout the organisation's operations, not sitting alongside them.

When Regulators and Insurers Start Requiring Proof

The next phase of this story is one where the question shifts from whether organisations should have AI governance frameworks to whether they can prove they do. Konig believes this transition is closer than most enterprise leaders currently expect.

On the regulatory side, the architecture is already taking shape. The EU AI Act and the Colorado AI Act both include requirements for documented governance frameworks, accountable senior management, model inventories, risk assessments, ongoing monitoring, and incident response procedures. ISO 42001 and the NIST AI Risk Management Framework are, in practice, becoming the reference standards for what a documented governance framework looks like. Certification, she suggests, is the likely next phase.

On the insurance side, the same dynamic is playing out through underwriting. Insurers writing AI-related coverage are beginning to require governance attestations as a precondition for coverage. The two pressures, regulatory and commercial, are reinforcing each other in a feedback loop. Regulator expectations create a baseline that insurers can underwrite to. Insurer requirements create market pressure for compliance with regulatory expectations even where regulators have not yet enforced.

"The companies that will struggle are those treating AI governance as a project to complete rather than a capability to build. The frameworks themselves are not the hard part; the hard part is making them operational, keeping them current as AI deployment expands, and producing evidence that they're actually functioning rather than just documented."

Konig's timeline for these developments is specific. Within the next 18 months, she expects that most regulated industries, spanning financial services, healthcare, insurance, telecommunications, and utilities, will require formal AI governance documentation. Cyber insurance and professional indemnity insurance will commonly include AI governance questions in underwriting. Supplier onboarding processes will include AI governance attestation requirements. ISO 42001 certification will become a commercial expectation rather than a differentiator.

The Predictable Pattern

There is a pattern that Konig describes with the clarity of someone who has watched it play out across multiple technology cycles.

"Losses, then the regulatory response, then the insurance market, then the governance frameworks, then the certifications, then the boardroom integration."

It is the sequence through which every significant new category of technology risk has moved, from cybersecurity to data privacy. AI hallucination liability is now in the early stages of that same arc.

What makes the current moment distinctive is the speed at which the commercial and regulatory infrastructure is forming. The AI liability insurance market is projected to grow at 35 percent annually and reach $5 billion by 2030. Regulatory frameworks that were still in draft form two years ago are now in force or approaching enforcement. The window in which enterprises can treat AI governance as a background concern, something to address eventually, is closing faster than most risk registers currently reflect.

The liability question, when an AI system produces harmful output, is one that courts are only beginning to work through. The insurance question, what coverage is available and what it actually triggers, is one that the market is working through in real time, often faster than the legal frameworks can keep up. And the governance question, whether enterprises have built the internal infrastructure to manage AI-generated risk at the operational level, is the one that most organisations have not yet asked themselves with adequate seriousness.

The machine, as it turns out, does not carry the liability when it gets something wrong. The organisation that chose to deploy it does. And in the months ahead, the systems for enforcing that accountability, regulatory, legal, and commercial, are going to become considerably more robust.

The smart organisations, as Konig puts it, are already on the last phase of that cycle. The rest are building up exposure they have not yet quantified, in operations they have not yet audited, from tools they may not yet even know their employees are using.

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