Transatlantic Quantum Security: Bridging U.S. (NIST/FISMA) and EU (GDPR/NIS2) Cloud Cryptography Frameworks
Bibliographic record
Abstract
Abstract: This review paper discusses the regulatory risk that quantum computing presents to cloud security by using the United States (U.S.) and the European Union (EU) stances in the approach towards post-quantum cryptography (PQC). The industry infrastructures, such as healthcare, energy, and defence industries, are susceptible to quantum algorithms, as they pose a threat to the existing encryption practices. The U.S. and EU have built up different regulatory sets of rules, such as the National Institute of Standards and Technology (NIST) PQC standards and the General Data Protection Regulation (GDPR) and Network and Information Systems Directive 2 (NIS2) regulatory frameworks in the EU, although there is still a lack of synchronization between the two. A comparative legal study of current U.S. and EU practices is used to inform this review with the primary legal sources (agreements, treaties, etc.), policy documents and industry case studies considering the regulatory gaps and overlaps in the PQC regulations. The paper will introduce a novel insight into the regulatory model of cloud migration in the new post-quantum world, push to adopt a cross-compliance program that would complement the gaps in laws and promote international collaboration to oppose quantum decryption. The review combines regulatory tools, sector-based case studies and Schrems II implications, providing actual life analyses in analyzing quantum threats and safeguarding critical infrastructures by policymakers.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".