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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2026
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| Accès en ligne: | https://arxiv.org/abs/2601.03504 |
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| _version_ | 1866915712647299072 |
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| author | Erlemann, Rasmus Morris, Charles Colyer Sathe, Sanjyot |
| author_facet | Erlemann, Rasmus Morris, Charles Colyer Sathe, Sanjyot |
| contents | The emergence of large-scale quantum computing threatens widely deployed public-key cryptographic systems, creating an urgent need for enterprise-level methods to assess post-quantum (PQ) readiness. While PQ standards are under development, organizations lack scalable and quantitative frameworks for measuring cryptographic exposure and prioritizing migration across complex infrastructures. This paper presents a knowledge graph based framework that models enterprise cryptographic assets, dependencies, and vulnerabilities to compute a unified PQ readiness score. Infrastructure components, cryptographic primitives, certificates, and services are represented as a heterogeneous graph, enabling explicit modeling of dependency-driven risk propagation. PQ exposure is quantified using graph-theoretic risk functionals and attributed across cryptographic domains via Shapley value decomposition. To support scalability and data quality, the framework integrates large language models with human-in-the-loop validation for asset classification and risk attribution. The resulting approach produces explainable, normalized readiness metrics that support continuous monitoring, comparative analysis, and remediation prioritization. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_03504 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Full-Stack Knowledge Graph and LLM Framework for Post-Quantum Cyber Readiness Erlemann, Rasmus Morris, Charles Colyer Sathe, Sanjyot Cryptography and Security The emergence of large-scale quantum computing threatens widely deployed public-key cryptographic systems, creating an urgent need for enterprise-level methods to assess post-quantum (PQ) readiness. While PQ standards are under development, organizations lack scalable and quantitative frameworks for measuring cryptographic exposure and prioritizing migration across complex infrastructures. This paper presents a knowledge graph based framework that models enterprise cryptographic assets, dependencies, and vulnerabilities to compute a unified PQ readiness score. Infrastructure components, cryptographic primitives, certificates, and services are represented as a heterogeneous graph, enabling explicit modeling of dependency-driven risk propagation. PQ exposure is quantified using graph-theoretic risk functionals and attributed across cryptographic domains via Shapley value decomposition. To support scalability and data quality, the framework integrates large language models with human-in-the-loop validation for asset classification and risk attribution. The resulting approach produces explainable, normalized readiness metrics that support continuous monitoring, comparative analysis, and remediation prioritization. |
| title | Full-Stack Knowledge Graph and LLM Framework for Post-Quantum Cyber Readiness |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2601.03504 |