Artificial intelligence is transforming nearly every corner of the business world, and the insurance industry is no exception. For business owners, AI presents a dual-edged reality: it introduces powerful new tools for growth and efficiency, but it also creates new categories of risk that existing insurance products were never designed to address. Nowhere is this tension more visible than in cyber insurance.
This guide is written for business owners who use AI tools — whether that means a customer service chatbot, a machine learning model for fraud detection, a generative AI platform for content creation, or simply a software vendor that has embedded AI into its products. You do not need a background in technology or law to follow along. The goal is to give you a practical understanding of how AI is reshaping cyber risk and cyber insurance, and what you should be doing right now to protect your business.
How AI Is Changing the Threat Landscape
For most of the history of cyber insurance, the dominant threats were relatively well understood: hackers breaking into systems to steal data, ransomware encrypting files and demanding payment, employees clicking on phishing emails, and businesses failing to patch known vulnerabilities. These remain serious threats. But artificial intelligence has fundamentally changed both the sophistication and the scale at which attackers operate.
AI-powered phishing is one of the most significant changes in the threat landscape. Traditional phishing emails were often easy to spot — awkward grammar, generic salutations, suspicious links. Today, attackers use large language models to craft highly personalized, grammatically perfect emails that mimic the writing style of real individuals within your organization. These emails can reference genuine business relationships, recent transactions, and specific colleagues by name. The barrier to entry for a convincing social engineering attack has dropped dramatically, and the volume of attacks has increased accordingly.
Deepfakes represent another frontier in AI-enabled fraud. A deepfake is a synthetically generated audio or video that realistically mimics a real person. There have been documented cases of criminals using AI-generated voice clones to impersonate executives on phone calls, convincing finance employees to wire millions of dollars to fraudulent accounts. Video deepfakes are increasingly used in business email compromise schemes and fraudulent identity verification. For businesses that rely on voice or video authorization for financial transactions or access controls, deepfake technology poses a direct and immediate threat.
Automated attacks powered by AI are also allowing threat actors to operate at a scale and speed that was previously impossible. AI can be used to scan thousands of systems for vulnerabilities simultaneously, to automate the process of exploiting those vulnerabilities, and to adapt attack strategies in real time based on the defensive responses it encounters. This means that the time between the discovery of a vulnerability and its widespread exploitation has shortened considerably, leaving businesses with less time to respond before they are affected.
How AI Is Changing the Defense and Claims Side of Cyber Insurance
The same technology that empowers attackers is also being deployed on the defense side, including by cyber insurers and the security vendors they partner with. Many insurers now use AI-powered tools to assess a prospective insured’s security posture before issuing a policy. These tools can scan publicly visible aspects of your network — open ports, software versions, misconfigured systems — and generate a risk score without you having to answer a single question. This means insurers may already know more about your security environment than you realize before you ever submit an application.
On the claims side, AI is being used to analyze the cause and scope of incidents more quickly, to detect patterns indicative of fraud, and to evaluate whether a claimed loss is consistent with the reported facts. Some insurers use AI to review forensic data after an incident to determine whether the policyholder’s security controls were actually in place as represented. If the evidence suggests that security measures were misrepresented on the application, the insurer may seek to deny coverage on grounds of material misrepresentation — a serious legal risk for businesses that check boxes on insurance applications without verifying that the described controls are actually implemented.
AI is also changing how insurers price risk over time. Rather than relying solely on historical loss data, insurers can now use real-time threat intelligence, AI-driven vulnerability scanning, and behavioral analytics to adjust pricing and coverage terms dynamically. This is leading to a more individualized and data-driven underwriting process, which has significant implications for how businesses should manage their security programs in order to maintain favorable insurance terms.
New Exposures That AI Creates for Businesses
Businesses that deploy AI tools — not just those that are attacked through AI — face a new category of risk that cyber insurance was not originally designed to cover. When an AI system makes a decision that causes harm, the legal and insurance analysis becomes considerably more complex.
Consider a business that uses an AI model to make hiring decisions. If the model produces discriminatory outcomes, the business faces potential liability under employment discrimination law. That claim might look like an employment practices claim, a technology error claim, or even a civil rights claim depending on how it is framed. None of these fit cleanly into a standard cyber policy, which is typically focused on data breaches, network intrusions, and the costs of responding to them.
Generative AI creates its own set of exposures. If your business uses a large language model to generate marketing content, legal documents, or customer communications, and that content turns out to be plagiarized, defamatory, or factually wrong, you face potential intellectual property liability, defamation claims, or professional liability exposure depending on the context. Cyber policies do not routinely cover these types of claims. Media liability or technology errors and omissions policies may be more relevant, but many businesses using AI do not carry those coverages.
AI systems can also fail in ways that cause direct operational harm. An AI-driven process control system in a manufacturing environment might make incorrect decisions that result in equipment damage or product defects. An AI customer service platform might provide incorrect information that leads a customer to take a harmful action. These operational failures may generate liability that falls between cyber coverage, professional liability coverage, and product liability coverage — leaving gaps that no single policy addresses.
There is also the risk of what is sometimes called model poisoning or adversarial attacks on AI systems. An attacker who understands how your AI model works can craft inputs specifically designed to cause the model to make incorrect decisions. This type of attack might not involve any traditional intrusion into your systems, yet it could cause significant harm. Whether a cyber policy would cover a loss caused by a manipulated AI model is an unsettled question that is only beginning to be addressed in policy language.
How Current Cyber Policies Do and Don’t Address AI-Specific Incidents
Most cyber insurance policies currently in the market were designed before AI became a widespread commercial tool. As a result, they do not specifically address AI-related risks in their coverage grants or exclusions. Whether an AI-related incident is covered depends on how the incident is characterized and whether it falls within the existing coverage language.
Standard cyber policies typically cover losses arising from unauthorized access to or use of computer systems, data breaches involving protected personal or confidential information, ransomware and extortion events, business interruption caused by a network security failure, and certain errors and omissions by the insured in providing technology services. An AI-related incident that fits within one of these categories is likely covered. An AI-related incident that does not fit neatly into any of these categories creates a coverage question that may need to be resolved through negotiation or litigation.
For example, if an attacker uses AI to craft a phishing email that tricks an employee into providing login credentials, leading to a data breach, that incident would very likely be covered under a standard cyber policy — the mechanism of the attack does not change the fundamental nature of the covered event. On the other hand, if your company’s AI model makes a series of incorrect predictions that result in a financial loss, but there was no intrusion and no data breach, a standard cyber policy may offer no coverage at all.
Some insurers are beginning to add AI-specific language to their policies, either as endorsements that extend coverage or as exclusions that limit it. Exclusions are more common at present. You may find exclusions for losses caused by AI systems making autonomous decisions, for intellectual property claims arising from AI-generated content, or for regulatory fines related to AI misuse. These exclusions can significantly narrow coverage for AI-forward businesses and should be reviewed carefully before a policy is purchased or renewed.
The Coverage Gap Question: Cyber Event or Product Liability?
One of the most difficult questions in AI and insurance is how to classify a harm caused by an AI system’s decision. Is it a cyber event covered by a cyber policy? Is it a product defect covered by a product liability policy? Is it a professional error covered by an errors and omissions policy? Or is it a combination of all three, with coverage potentially spread across multiple policies — or falling through the gaps between them?
This question does not have a universal answer, and it is one of the areas where insurance law is still developing. The characterization of the harm depends on facts like whether the AI system was sold as a product or delivered as a service, whether the harm resulted from a design defect in the model or from an error in how the model was deployed, and whether the injured party was the business’s customer, a third party, or the business itself.
A company that develops and sells AI software faces product liability exposure that a company merely using a third-party AI tool does not. A company that uses AI in delivering professional services — such as a law firm using AI for legal research or a medical practice using AI for diagnostic support — faces professional liability exposure that a retailer deploying AI for inventory management does not. Understanding where your business falls in this spectrum is essential to identifying which insurance coverages are relevant and whether you have gaps.
The practical advice here is to work with an insurance broker who understands both technology risks and AI specifically, and to have your legal counsel review your AI-related contracts and use cases to identify the categories of liability you face. Once you understand your exposure, you can map it against your current insurance portfolio and identify gaps that need to be addressed through additional coverage or policy endorsements.
Regulatory Exposure Related to AI Misuse
Regulation of artificial intelligence is developing rapidly in the United States and internationally. Federal agencies including the Federal Trade Commission, the Equal Employment Opportunity Commission, and the Consumer Financial Protection Bureau have all issued guidance applying existing law to AI-based decision-making. Several states have enacted or are considering specific AI laws, with requirements ranging from algorithmic impact assessments to prohibitions on certain uses of AI in employment and lending decisions. The European Union’s AI Act, which took effect in 2024 and is being phased in through 2026, has extraterritorial reach that may affect US businesses operating in or serving European markets.
If your business is investigated by a regulatory agency for alleged AI misuse — say, the FTC investigating whether your AI-based marketing practices were deceptive, or a state attorney general investigating whether your AI hiring tool produced discriminatory outcomes — you will face defense costs and potentially substantial civil penalties. Whether your cyber policy covers those costs depends on how the policy defines regulatory coverage and what exclusions apply.
Most cyber policies include some form of regulatory defense coverage, but it is typically limited to investigations arising from a data breach or privacy violation. An investigation arising from AI misuse that does not involve a data breach may not fall within this coverage. Even where coverage exists, policies commonly exclude coverage for fines and penalties resulting from intentional violations of law, which can complicate coverage for AI-related enforcement actions if the regulator alleges that the business knowingly used a discriminatory or deceptive AI system.
For businesses with significant AI regulatory exposure, it is worth exploring whether a standalone AI liability policy or a technology errors and omissions policy with AI-specific coverage is available and appropriate. The most important step, however, is building a genuine AI governance program — documented policies for how AI is selected, tested, monitored, and governed — because robust governance reduces both the likelihood of a regulatory problem and the severity of the consequences if one occurs.
How Insurers Are Asking About AI in Underwriting Questionnaires
The cyber insurance underwriting process is changing in response to the rise of AI. Many insurers have begun adding questions about AI use to their underwriting questionnaires, and this trend is accelerating. You can expect to be asked whether your business uses AI or machine learning tools, what functions those tools perform, whether you have conducted any risk assessments of your AI systems, what data those systems are trained on or have access to, and whether you have policies governing the use of AI within your organization.
These questions matter for two reasons. First, your answers affect the coverage you are offered and the premium you pay. Insurers are trying to assess the additional risk that AI use creates, and businesses that demonstrate mature AI governance practices are likely to receive more favorable terms than those that cannot describe their AI programs at all. Second, your answers create representations that the insurer can rely on. If you represent that you have an AI governance policy in place and you do not, or if you describe your AI use inaccurately, you risk a claim being denied on the basis of material misrepresentation.
Businesses should review their cyber insurance applications carefully with these issues in mind. Before you submit an application, make sure you have a clear understanding of what AI tools your business uses, what data they access, and what controls you have in place. If you use third-party AI tools, understand whether the vendor’s use of your data for model training or other purposes creates additional exposure that you need to disclose. Accurate and thorough disclosure is both a legal obligation and a practical necessity for maintaining coverage.
Practical Steps for Businesses Using AI Tools
There are several concrete steps that businesses using AI tools should take to protect their insurance position and reduce their overall AI-related risk exposure.
The first step is to conduct an inventory of AI tools and systems used by your business. This includes not just systems you have built or purchased specifically as AI tools, but also AI features embedded in software you already use — customer relationship management platforms, accounting software, marketing tools, and productivity applications frequently include AI-powered features that may not be obvious. Understanding what AI tools you use, what data they process, and what decisions they influence is the foundation of any AI risk management program.
The second step is to review the contracts with your AI vendors. Key questions include who owns the data that the AI system processes, whether the vendor uses your data to train its models, what security and privacy obligations the vendor has undertaken, and what liability the vendor accepts if its AI system causes harm. Many AI vendor contracts are drafted heavily in the vendor’s favor, with limited warranties and broad liability limitations. Negotiating better terms — or at least understanding what risks you are accepting by contract — is essential before you deploy an AI system in a sensitive business context.
The third step is to review your insurance portfolio with AI risks explicitly in mind. Work with your broker to identify how your current policies apply to AI-related incidents, where the gaps are, and what options exist to fill them. Pay particular attention to the intersection of your cyber policy, your professional liability or errors and omissions policy, your product liability policy if applicable, and your directors and officers policy, which may be implicated if AI-related failures lead to shareholder claims or regulatory investigations targeting the company’s leadership.
The fourth step is to develop an AI governance framework. This does not need to be elaborate, but it should document your approach to selecting and vetting AI tools, how you test AI systems before deployment, how you monitor AI systems in production, and what your policies are for human oversight of AI decisions. A documented governance framework demonstrates to insurers, regulators, and courts that your business takes AI risk seriously, and it provides a defense against claims that AI-related harm resulted from reckless disregard for known risks.
The Future of AI and Cyber Insurance
The insurance market is still in the early stages of adapting to AI risk. Over the next several years, we can expect significant changes in how cyber insurance products are structured and what they cover in relation to AI.
Standalone AI liability policies are likely to become more widely available and more sophisticated. These policies will be designed to cover the specific risks that AI systems create — algorithmic errors, biased outputs, AI-generated content claims, and regulatory enforcement related to AI misuse — rather than relying on cyber policies drafted before AI became a significant business tool.
Cyber policies themselves will increasingly distinguish between AI-related and non-AI-related risks, with specific coverage provisions and exclusions for each. Businesses that understand this evolution and stay ahead of it by building strong AI governance programs and maintaining open communication with their insurers will be better positioned to obtain comprehensive coverage at reasonable cost.
Regulatory developments will also shape the insurance landscape. As AI-specific regulations are enacted at the state and federal level, insurers will develop products designed to address the specific liabilities those regulations create. The EU AI Act’s risk classification framework — which categorizes AI uses as unacceptable, high-risk, limited-risk, or minimal-risk — may influence how US insurers think about pricing AI risk even before similar US legislation is enacted.
The businesses that navigate this landscape most successfully will be those that treat AI governance not as a compliance burden but as a risk management strategy — recognizing that the same practices that reduce the likelihood of AI-related harm also make the business more insurable and more defensible when things go wrong. Working closely with legal counsel who understands both AI governance and insurance law is an increasingly important element of that strategy.