The Techno-Neutrality Solution to Navigating Insurance Coverage for Cyber Losses
Bibliographic record
Abstract
Insurers currently constrict coverage for losses involving electronic information in traditional insurance product lines. As a result, insurance customers are driven to the brave new world of non-standardized varieties of cyber-risk insurance policies. That world abounds with coverage gaps as the market for cyber insurance sorts itself out. Until that synchronization of coverage for cyber losses occurs, litigation is bound to occur as the boundaries of coverage remain patchwork and uncertain. This article examines the degree to which cyber losses differ from other insured losses. The cyber-loss insurance coverage jurisprudence reveals a mishmash of principles and coverage terms that are largely focused on the technology of the loss and not on the nature of the loss insured. Unpredictable and unhelpful analogies have ensued, prompting a highly inefficient coverage marketplace and resulting litigation experience. This article also draws parallels with the market experience of a number of now-commonplace insurance coverage products, like commercial general liability policies, that also went through an initial period of uncertainty. Lessons from those prior insurance experiences are instructive as the wild world of cyber insurance stabilizes. This article proposes that, to reduce the prevalence of insurance coverage disputes about cyber losses, courts should jettison the "cyber" loss differentiation altogether and instead focus on the nature of the inherent risk insured against, as opposed to the risk's "cyber" quality. Taking a technologically neutral stance-applying "techno-neutrality" to insurance policy language-can act as a market stabilizer. This approach is preferable to introducing new, untested insurance products or, alternatively, risking arbitrary coverage gaps under traditional product lines. The long-term, more commercially sensible solution is for insurers to simply fold cyber-loss coverage into traditional coverage products and not differentiate losses based on particular or peculiar property characteristics.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.031 |
| Scholarly communication | 0.015 | 0.023 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".