Who it's for · Insurers & underwriting
You can't price what nobody has measured.
AI exposure is walking into underwriting with no loss history and no standard assessment behind it. AetherLab measures how an AI system fails and what those failures cost, so coverage decisions rest on evidence instead of an application form.
New exposure, no instrument.
Every mature line of coverage rests on an assessment standard: inspections, actuarial tables, loss runs. AI risk has none of that yet: behavior varies by prompt, changes with every model update, and the application form can't see any of it.
No loss history
The systems are too new and too fast-moving for experience data alone to carry the pricing decision.
Shifting behavior
A model update can change an insured system's risk profile overnight, after the policy is bound.
Unpriceable narratives
"We take safety seriously" is not an input to a rate. Severity-scored findings mapped to financial impact are.
Failure modes, priced in business terms.
AdversarialScan establishes how the system breaks under adversarial pressure. The Evidence Pack ties each finding to its financial consequence. The idea is plain: connect risk insight to economic impact, then to the controls that contain it, then to measured improvement.
Inputs to appetite
Before binding
A pre-bind assessment establishes the system's baseline: what breaks, how easily, and what the exposure looks like in dollar-denominated terms your committee can weigh.
Inputs to terms
Over the term
Re-assessment at renewal or material change, plus production guardrails between, give the policy a monitored risk posture instead of a point-in-time snapshot.
Where AetherLab sits in your process.
The same assessment infrastructure already runs at production scale, screening 150,000+ AI checks per day across text and image for institutions with high-consequence content risk, including in the payments ecosystem.
Common questions from underwriting teams.
- What does an AetherLab assessment give an underwriter that an application form does not?
- Measured behavior. AdversarialScan attacks the insured system the way a motivated adversary would and returns severity-scored findings tied to business and financial impact. That converts "the applicant says they have controls" into observed evidence of how the system fails and how badly.
- Can assessments be repeated over a policy term?
- Yes. The same break-goals can be re-run at renewal or on material change, producing comparable results over time. Production guardrails can also run continuously between assessments, so posture is monitored rather than sampled once at binding.
- Does AetherLab price or underwrite the risk itself?
- No. AetherLab produces the risk evidence: severity-scored findings mapped to financial impact. Pricing, appetite, and coverage decisions remain with your underwriting function; the Evidence Pack is built to slot into that process, not replace it.
- What kinds of AI systems can be assessed?
- Text-based systems (chat, agents, copilots), image-generation and image-understanding systems, and multimodal products. Break-goals are defined per engagement, so the assessment reflects the exposures that matter to the line of coverage.
Bring an instrument to AI underwriting.
You might be building an AI line, adding AI questions to an existing one, or evaluating one specific insured. Either way, we'll show you what the assessment measures and how the evidence reads.
Ask about the Evidence Pack
Leave your email and we'll walk you through what an Evidence Pack contains for your use case: severity-scored findings, business-impact mapping, and the approval record.