Full-Stack Knowledge Graph and LLM Framework for Post-Quantum Cyber Readiness

• URL: http://arxiv.org/abs/2601.03504v1
• Date: Wed, 07 Jan 2026 01:31:15 GMT
• Title: Full-Stack Knowledge Graph and LLM Framework for Post-Quantum Cyber Readiness
• Authors: Rasmus Erlemann, Charles Colyer Morris, Sanjyot Sathe,
• Abstract summary: The emergence of large-scale quantum computing threatens widely deployed public-key cryptographic systems.<n>This paper presents a knowledge graph based framework that models enterprise cryptographic assets, dependencies, and vulnerabilities to compute a unified post-quantum (PQ) readiness score.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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