Quantum-Aware Risk Modeling Preparing Cyber Insurance for the Post-Quantum Era
Bibliographic record
Abstract
Quantum computing poses significant risks to cybersecurity by threatening the integrity of widely used cryptographic systems like RSA, ECC, and DH. This chapter examines the urgency of rethinking traditional cyber insurance risk models in response to these quantum-enabled threats. It introduces a quantum-aware risk modeling approach that incorporates factors such as PQC migration readiness and dynamic premium adjustments. The chapter also explores how insurers can embed quantum resilience into policies through cryptographic agility requirements and third-party risk assessments. Regulatory initiatives, particularly NIST's PQC standardization, are discussed for their role in guiding industry preparedness. Simulated breach scenarios highlight potential challenges in underwriting and claim resolution in a post-quantum era. This chapter offers a forward-looking roadmap for building a resilient, adaptive cyber insurance ecosystem prepared for the quantum future.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".