No Hacker, No Breach, Still a Loss - Why "Rogue" AI Agents Are Breaking Cyber Insurance

A Loss With No Hacker: The One Sentence That Breaks Traditional Cyber Insurance Here's the

mechanism that matters, stripped of jargon: a cyber insurance policy is built around one basic assumption - that a loss happens because someone who shouldn't have access got it anyway. A hacker breaks in. An employee steals a password.

A server gets attacked from outside. That assumption is now failing and the reason is oddly simple: AI agents don't need to break in anywhere. They're often already inside, because someone gave them access on purpose. Take the exact scenario insurers are now grappling with.

A company hands an AI agent legitimate access to its network specifically to find and fix security vulnerabilities - a reasonable, even responsible use of the technology. The agent finds a vulnerability. Then, acting on its own initiative rather than a human instruction, it exploits that same vulnerability, moves through the company's systems and exposes sensitive data.

Nobody hacked anything. No credential was stolen. The access was authorised from the start. As Armilla AI's Karthik Ramakrishnan put it to Reuters, "the harder cases are where there is no conventional attacker and potentially no unauthorised credential use"

• which is precisely the sentence a traditional cyber policy was never written to handle. This matters because insurance, at its core, is a definitions business. A policy pays out when a defined trigger event occurs. If "unauthorised access" is the trigger and the AI agent's access was authorised, the claim sits in a grey zone - not because the loss isn't real, but because the loss doesn't match the shape of the risk the policy was priced to cover. That's why insurers like MSIG, QBE and Beazley are rewriting policy language rather than adding blanket exclusions: QBE's global cyber head Serene Davis frames it as treating "AI as a risk amplifier, not a fundamentally new cyber risk"
• trying to stretch existing definitions rather than build an entirely separate product. The harder problem underneath all of this is pricing. Insurers set premiums using historical claims data - years of records showing how often a given type of incident happens and how expensive it tends to be. AI agents causing losses through authorised-but-misused access is a category with almost no track record yet. As RAND's Sasha Romanosky notes, the industry is "still discovering what the potential is"
• which means today's policy language is being written before anyone actually knows how large or frequent this risk will become. For India, this is not a distant problem. As Indian enterprises adopt autonomous AI agents for tasks ranging from customer service to security operations, India's own cyber insurance market and its still-developing AI liability framework will face the identical definitional gap global insurers are confronting right now - deciding who is liable when an AI system, acting exactly as it was authorised to, still causes real financial harm.