Quantum Risk Assessment Model
Before choosing algorithms or planning a migration, an organization should quantify how exposed each business system is to the quantum threat. A structured quantum risk assessment (QRA) turns a vague worry into a ranked, defensible set of priorities — so scarce migration effort lands on the systems that matter most first.
A five-step model
- Asset identification & classification. Inventory sensitive data, core systems and key business processes, and classify them by value and sensitivity.
- Threat modeling. Map the relevant threats — data leakage, authentication bypass, system tampering — and weigh them against the likely quantum timeline and the gap between current key sizes and projected quantum capability.
- Vulnerability assessment. Examine reliance on quantum-weak cryptography, key lengths in use, and the security of the surrounding protocols.
- Risk quantification & ranking. Combine qualitative and quantitative scoring to rank systems and surface the high-risk domains.
- Risk-response strategy. Select responses — PQC migration, hybrid deployment, hardening — under the classic accept / transfer / mitigate / avoid framing.
Key factors that drive the score
- Data lifetime. Long-lived sensitive data raises risk the most, because it can be captured today and decrypted later — see harvest-now, decrypt-later and Mosca's inequality (
X + Y > Z). - Cryptographic dependency. Heavy reliance on RSA/ECC means a larger attack surface once a quantum computer arrives.
- System update cycle. Systems that are slow or hard to change need an earlier start — the migration runway is longer.
- Existing protection maturity. Strong current controls reduce, but do not eliminate, quantum exposure.
- Compliance pressure. Finance, government and healthcare face the most urgent regulatory and contractual drivers.
Tools and methods
- CISA/NSA/NIST — "Quantum-Readiness: Migration to PQC" (Aug 2023): inventory-first methodology and prioritization guidance.
- ETSI GR QSC 004 (Quantum-Safe Security / Quantum Risk Assessment): a structured risk-assessment framework.
- Commercial QRA tooling for automated discovery, scoring and reporting.
Worked example: an e-commerce order system
Applying the model to a typical online order platform produces the following profile:
| Factor | Observation | Rating |
|---|---|---|
| Sensitive data type | PII, transaction records, payment data | High |
| Data lifetime | Long-term retention required by law | High |
| Crypto dependency | Heavy use of RSA/ECC for TLS and tokens | High |
| Update cycle | Two major releases per year — long lead time | Medium |
| Protection maturity | Good controls, but quantum posture unknown | Medium |
| Compliance | PCI DSS and GDPR obligations | High |
Overall rating: HIGH. The combination of long-lived, regulated payment data and heavy RSA/ECC dependence means this system should be prioritized for migration.
From assessment to action
A completed QRA feeds directly into the rest of the program. Use it to seed your cryptographic discovery, confirm harvest-now exposure, shape the construction blueprint, and sequence the phased migration.
Standards & references
- 安全牛《后量子密码安全能力构建技术指南（2025版）》— "QKD做后盾 PQC加速跑" (industry report, synthesized; not quoted verbatim).
- CISA/NSA/NIST — Quantum-Readiness: Migration to PQC (Aug 2023)
- ETSI — Quantum-Safe Cryptography (incl. GR QSC 004)
- NIST — Post-Quantum Cryptography project
- Resources — full standards register
量子风险评估模型
在选择算法或规划迁移之前，机构应当先量化每个业务系统对量子威胁的暴露程度。结构化的量子风险评估（QRA）能把模糊的担忧转化为一份可排序、可论证的优先级清单——让有限的迁移投入优先落在最关键的系统上。
五步模型
- 资产识别与分级。盘点敏感数据、核心系统与关键业务流程，并按价值与敏感度分级。
- 威胁建模。梳理相关威胁——数据泄露、认证绕过、系统篡改——并结合量子时间线，以及当前密钥长度与预期量子能力之间的差距加以权衡。
- 脆弱性评估。考察对量子脆弱密码的依赖程度、在用密钥长度，以及周边协议的安全性。
- 风险量化与排序。结合定性与定量评分，对系统排序并识别高风险领域。
- 风险应对策略。在经典的接受 / 转移 / 缓解 / 规避框架下，选择应对手段——PQC 迁移、混合部署、安全加固。
驱动评分的关键因素
- 数据生命周期。长期留存的敏感数据使风险最为突出，因为它今天即可被截获、日后再解密——参见先收集后解密与 Mosca 不等式（
X + Y > Z）。 - 密码依赖度。对 RSA/ECC 的重度依赖意味着量子机器到来时攻击面更大。
- 系统更新周期。变更缓慢或困难的系统需更早动手——所需迁移窗口更长。
- 现有防护成熟度。当前控制越强，量子暴露越低，但无法彻底消除。
- 合规压力。金融、政府与医疗面临最迫切的监管与合同驱动。
工具与方法
- CISA/NSA/NIST《Quantum-Readiness: Migration to PQC》（2023年8月）：资产盘点优先的方法论与优先级指引。
- ETSI GR QSC 004（Quantum-Safe Security / 量子风险评估）：结构化的风险评估框架。
- 商用 QRA 工具：自动化发现、评分与报告。
实例演练 电商订单系统
将该模型套用于典型的在线订单平台，得到如下画像：
| 因素 | 观察 | 评级 |
|---|---|---|
| 敏感数据类型 | 个人信息、交易记录、支付数据 | 高 |
| 数据生命周期 | 法律要求长期留存 | 高 |
| 密码依赖度 | TLS 与令牌重度使用 RSA/ECC | 高 |
| 更新周期 | 每年两次大版本——交付周期长 | 中 |
| 防护成熟度 | 控制良好 但量子态势未知 | 中 |
| 合规 | PCI DSS 与 GDPR 义务 | 高 |
总体评级 高。长期留存且受监管的支付数据，叠加对 RSA/ECC 的重度依赖，意味着该系统应当优先迁移。
从评估到行动
一份完成的 QRA 可直接驱动后续工作：用它启动密码资产发现，确认先收集暴露面，塑造建设蓝图，并为分阶段迁移排序。
标准与参考
- 安全牛《后量子密码安全能力构建技术指南（2025版）》——"QKD做后盾 PQC加速跑"（行业报告，综合改写，非逐字引用）。
- CISA/NSA/NIST — Quantum-Readiness: Migration to PQC（2023年8月）
- ETSI — Quantum-Safe Cryptography（含 GR QSC 004）
- NIST — Post-Quantum Cryptography 项目
- 资源链接 — 完整标准登记册