Short answer
Describe input data, processing, delivered models and access to results. Training and deployment responsibilities may differ; qualified legal and privacy review should assess actual data rights.
An illustrative example
A provider fine-tunes a model on customer examples.
This is a hypothetical situation, not a real customer outcome or a coverage determination.
Three facts to prepare
- Data source
- Training role
- Model access
Use a brief, accurate summary. Separate confirmed facts from assumptions; keep passwords, identity numbers, private customer records and confidential documents out of an initial marketplace request.
A question to bring to the right professional
Which activities and confidentiality scenarios belong in our submission?
Describe each layer's actual responsibility and the customer consequences if it fails. Infrastructure, data processing, orchestration and end-user services can involve different facts. Use verified operating evidence and explicit limitations rather than implying unlimited scale, complete security or an untested assurance.
Sources and scope
- Travelers: technology errors and omissions risk examples
Insurer-authored context on technology service allegations and differences in policy wording. Product descriptions apply to that source; they are not LunarQuote offers or evidence of a partnership.
- NIST: AI Risk Management Framework
A voluntary framework for organizing AI risk. It is not an insurance contract, certification or determination of legal compliance.
Sources supply the stated background, not a determination about the illustrative case. The example, checklist and discussion prompt are LunarQuote educational material. Source links checked October 5, 2026.
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