The first liabilityclass where thesystem decides
- Risk class
- AI Risk
- Focus
- Models · Agents · Autonomy
- Related products
- NOVA
- Operating model
- Verify → Monitor → Prevent → Recover → Insure
Why the traditional market struggles
Technology E&O assumes a defect that can be traced, reproduced and patched. Model behaviour is emergent, distributionally sensitive and often non-reproducible. Causation is contested, the chain of responsibility spans model developer, deployer and integrator, and the loss can be reputational, regulatory and financial at once. Underwriting it requires evaluation evidence, not assurances.
What we structurearound.
Erroneous output and decision harm
Financial or physical loss caused by model output relied upon in a live process.
Autonomous agent action
Exposure created when a system holds tool access, spend authority or the ability to transact.
Model integrity
Data poisoning, prompt injection, model extraction and adversarial manipulation.
Intellectual property and content
Third-party claims arising from training data, generated output and downstream distribution.
Regulatory and compliance
Exposure under emerging AI regimes, including obligations attaching to high-risk deployments.
Performance and availability
Degradation, drift and dependency failure in production systems on which clients rely.
The loop, appliedto this class.
- Verify
Review of evaluation methodology, guardrails, human-in-the-loop design and deployment boundaries.
- Monitor
Continuous signal on drift, incident rate, autonomy scope and change in model or provider.
- Prevent
Agreed containment: capability limits, escalation triggers and rollback conditions.
- Recover
Incident response covering technical containment, disclosure and third-party exposure.
- Insure
Risk transfer calibrated to the autonomy level actually operating in production.
What stays under observation once the risk is bound.
- Autonomy level in production
- Evaluation and red-team cadence
- Human oversight coverage
- Tool and spend authority scope
- Model and provider change rate
- Incident and near-miss telemetry
- Data provenance posture
- AI model developers
- Enterprises deploying agentic systems
- Healthcare, legal and financial AI vendors
- Platform and infrastructure providers
- Investors underwriting AI portfolios