I attended a cyber security insurance conference last week, and naturally, AI was on everyone’s mind. The room was buzzing with discussions focused on worst case scenarios. People were wrestling with foundational questions: What if an automated system goes wrong? Which policy form actually covers it? Is it already wrapped into standard coverage, or do we urgently need affirmative coverage in place?
As I sat there listening, a quiet realization set in. A lot of the audience seemed to operate under the assumption that Tech Errors & Omissions (Tech E&O) or standard Cyber policies would simply step in to handle whatever went wrong. I do not mean that critically; everyone was there to learn, and navigating insurance definitions in an AI world is uncharted territory for almost all of us. But I found myself going back and forth on a specific, complex risk that I felt was getting glossed over: model distillation abuse and model poisoning.
I want to be clear; I am a huge proponent of AI. I use these tools, I build around them, and I genuinely believe they represent a massive leap forward for productivity, research, and enterprise capabilities. I am not here to sound an alarm or suggest we pull back on adopting AI models. However, even as someone who works closely with this technology and spends a lot of time analyzing risk, I have to admit that I clearly do not have all the answers for how our industry should proceed forward when it comes to model distillation abuse and its long term financial coverage.
When people talk about AI risk, they often fall back on simple software metaphors. If a model breaks, you just roll it back to a previous clean snapshot or checkpoint, right? In theory, yes. But in practice, the true business income costs and operational friction associated with recovery are far more complex than what many people take for granted.
Take model distillation abuse, for example. If an adversary systematically queries your API to extract the proprietary logic, weights, or reasoning of your custom model, where does that claim land? Tech E&O typically covers third party liability when a service failure hurts a customer; it does not cover the internal balance sheet devaluation of your primary asset. Meanwhile, standard first party cyber policies routinely exclude trade secret losses or competitive advantage erosion. If the extraction happened through open API endpoints, an insurer might argue no traditional “unauthorized network breach” ever took place.
Model poisoning presents an even stickier business interruption problem. If malicious data corrupts your pipeline, rolling back your model is only step one. The real expense lies in what comes next: retraining the weights, re-curating datasets, running red-teaming evaluations, and performing rigorous human-in-the-loop validation to ensure the system is safe to re-deploy into production.
Will a traditional loss adjuster treat three weeks of post-rollback safety testing as covered “data restoration”? Or will they view safety alignment and re-validation as a non-reimbursable business upgrade? If you have to temporarily route your traffic to expensive third party fallback APIs while your internal engineering team fixes the pipeline, does your business interruption policy absorb that massive cost delta?
These are open questions, and they highlight where our current risk models fall short. We cannot treat probabilistic, self-learning AI models like deterministic software programs. Restoring an SQL database from a backup tape is relatively straightforward; rebuilding trust, alignment, and behavioral integrity in an LLM is iterative, expensive, and time consuming.
We are spending a lot of energy debating whether AI is covered under broad legacy definitions, but we should not lose focus on the operational reality of how these models actually fail. We need to concentrate our efforts on creating clear, affirmative language around model retraining costs, safety re-validation, and business interruption triggers.
I do not claim to have the exact policy framework mapped out yet, and I think anyone who says they have all the answers right now is probably oversimplifying the problem. But if we want to build a resilient ecosystem where enterprise AI can thrive safely, we need to start asking deeper questions about what recovery actually costs when a model goes off the rails.
