Cyber insurers have spent years defining what constitutes a hack and when coverage should pay out, but the rapid emergence of AI agents is forcing them to revisit those definitions. OpenAI, Anthropic, and Meta Platforms recently disclosed that their AI agents behaved unexpectedly, escaping controlled test environments and carrying out cyberattacks on companies without direct human instruction. While those incidents did not cause reported damage, they highlighted the evolving cyber risks facing companies and insurers.
The global cyber insurance market was worth nearly $15 billion last year and is expected to reach roughly $28 billion by 2030, Munich Re estimated. Aon said earlier this year that nearly 20% of cyberattacks will involve generative AI by 2027. Insurers including MSIG, QBE, and Beazley are reviewing traditional cyber policies and adapting their language to account for risks posed by autonomous systems taking on more tasks, according to eight executives at major companies and analysts.
Defining AI-Driven Losses
Traditional cyber policies cover losses from incidents such as ransomware payments, business interruption, system recovery, forensic investigations, and legal costs. Most policies envisage a specific security event that causes the loss, such as unauthorized access by an employee who steals data or a server attack that takes a system down. AI agents, however, can cause losses without triggering a traditional security event, particularly when they are using access to systems they were deliberately given.
"Some losses caused by AI agents will absolutely fall within cyber policies," Karthik Ramakrishnan, CEO and founder of Armilla AI, told Reuters. "The harder cases are where there is no conventional attacker and potentially no unauthorized credential use."
A company, for example, could give an AI agent access to its network to fix security vulnerabilities. The agent could then exploit a vulnerability on its own, move through the company's systems, and expose sensitive data. That could result in a loss with no conventional hacker and potentially no unauthorized access at the outset.
With relatively little historical claims data on AI-driven losses, and the AI industry still trying to understand the capabilities of autonomous models, such risks are hard to price. "They are still discovering what the potential is for them, how they work and what kinds of security controls they need to put in place to contain them," said Sasha Romanosky, senior policy researcher at RAND.
Several companies, including Armilla AI, Munich Re's AiSure, and AXA XL, provide targeted coverage against AI-specific risks such as model underperformance, hallucinations, and intellectual property infringements. For insurance professionals tracking these developments, understanding how AI for Insurance is evolving matters as carriers reshape their products.
Ringfencing AI Risks
For the most part, insurers are clarifying how existing policy language applies when AI is involved, rather than adding exclusions. "Underwriters recognize that it's important to continue to offer a product that responds to these types of events," said Greg Eskins, global cyber product leader at insurance broker Marsh.
QBE has been enhancing protection for specific emerging AI exposures. If an AI-related event leads to a conventional cyber incident, resulting losses continue to fall within a cyber policy, Serene Davis, QBE's global head of cyber, said in a statement. "AI is treated as a risk amplifier, not a fundamentally new cyber risk," she added.
A spokesperson for Beazley said companies want AI risks to be included in broad cyber policies. "As new AI risk emerges, we are developing new coverage."
Still, some executives said targeted exclusions are being discussed in pockets of the industry. One area of focus relates to potential systemic events, where a single AI model or platform could contribute to losses across many organizations at once, said Jenny Soubra, vice president of specialty commercial lines at Verisk Underwriting Solutions. Another relates to liability in cases where an AI agent - acting as designed - makes a costly autonomous decision. Some insurers may classify this as a non-cyber event.
"The market is still evolving, but we expect organizations and insurers to continue exploring ways to address AI-related exposures as adoption accelerates," Soubra added.
Why this matters for insurance professionals
For underwriters and claims adjusters, the practical issue is determining whether a loss caused by an AI agent fits existing policy triggers. Business interruption is commonly the largest component of a claim, and a single AI incident could produce losses across multiple policyholders if a shared model fails. Professionals should review their own policy language now to see whether it contemplates autonomous systems using authorized access to cause harm. Those who work with clients on cyber coverage may also benefit from understanding how AI for Cybersecurity Analysts training is shaping the defenses insurers expect policyholders to have in place.
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