Charting a Path Through the Quantum Threat

Author: Denis Avetisyan

A new framework uses knowledge graphs and artificial intelligence to assess and strengthen enterprise cybersecurity in the face of looming quantum computing advancements.

A system integrates diverse data sources-assets, certificates, vulnerabilities, and network services-into a knowledge graph, then subjects the resulting relationships to validation by a large language model, flagging discrepancies between rule-based reasoning and AI assessment for human review, and prioritizing edges based on probability thresholds and specialized prompts tailored to ten relationship semantics-including authentication, connectivity, vulnerability, and cloud dependencies-all while maintaining post-quantum cryptographic awareness and asynchronous processing for continuous refinement.

This review details a full-stack knowledge graph and large language model framework for quantifying post-quantum cryptographic readiness and enabling risk-based migration strategies.

The looming threat of quantum computing necessitates a proactive shift in cryptographic infrastructure, yet organizations currently lack scalable methods to assess their post-quantum (PQ) readiness. This paper introduces a novel framework, ‘Full-Stack Knowledge Graph and LLM Framework for Post-Quantum Cyber Readiness’, that models enterprise cryptographic assets as a knowledge graph, quantifying PQ exposure through graph-based risk analysis and leveraging large language models for data validation. The resulting approach delivers explainable, normalized readiness scores, enabling prioritized migration strategies and continuous monitoring of evolving vulnerabilities. Will this systemic understanding of cryptographic risk prove crucial in navigating the transition to a post-quantum world?

Unveiling the Quantum Threat: A System Under Scrutiny

The bedrock of modern digital security – public-key cryptography systems like RSA, Elliptic Curve Cryptography (ECC), and Diffie-Hellman – faces a critical vulnerability with the advent of quantum computing. These systems rely on the mathematical difficulty of certain problems, such as factoring large numbers or solving the discrete logarithm problem, to protect data. However, $Shor's algorithm$ , a quantum algorithm, can efficiently solve these problems, effectively breaking the encryption that secures sensitive information. This isn’t a theoretical threat; the increasing power of quantum computers suggests a real possibility of decryption of currently encrypted data, even data archived for the long term. Consequently, the confidentiality and integrity of vast amounts of data – from financial transactions and government secrets to personal communications – are at risk, presenting an existential challenge to cybersecurity as it currently exists.

The rapid advancement in quantum computing presents a clear and present danger to modern cryptographic systems, demanding a shift towards post-quantum cryptography (PQC) to preserve data security. Current encryption standards, relied upon for secure communication and data storage, are vulnerable to attacks from sufficiently powerful quantum computers utilizing algorithms like Shor’s algorithm. PQC focuses on developing cryptographic algorithms that are resistant to both classical and quantum attacks, ensuring continued confidentiality and integrity even in a post-quantum world. This transition is not merely a technological upgrade; it requires a comprehensive evaluation of existing systems, the implementation of new algorithms, and a long-term strategy for maintaining security as quantum computing capabilities evolve. Failing to proactively adopt PQC leaves sensitive data exposed to potential decryption and compromise, with far-reaching implications for governments, businesses, and individuals alike.

Organizations currently navigate a complex landscape in assessing their vulnerability to quantum-based cyberattacks. The challenge extends beyond simply understanding the theoretical threat; it requires a comprehensive inventory of all cryptographic assets, a determination of their lifespan relative to potential quantum decryption capabilities, and an evaluation of the cost and disruption associated with migrating to quantum-resistant algorithms. Many systems utilize cryptography inherited from third-party vendors or embedded within legacy software, creating a ‘shadow cryptography’ problem where exposures remain hidden. Furthermore, the long-lived nature of data – particularly archived information – means that even communications considered secure today could be decrypted years in the future, necessitating a proactive and layered approach to risk mitigation that accounts for both current and long-term data protection needs.

Mapping the Attack Surface: A Systemic Reconnaissance

Comprehensive asset discovery is the initial phase in preparing for Post-Quantum Cryptography (PQC) implementation. This process involves a complete inventory of all digital assets within an organization’s infrastructure. Specifically, this includes identifying and cataloging all servers, cryptographic keys – both symmetric and asymmetric – and digital certificates currently in use. Accurate asset identification is critical because these elements represent potential points of vulnerability when quantum computing capabilities advance. Without a complete understanding of existing assets, organizations cannot effectively prioritize migration to PQC-compliant algorithms or assess the scope of required updates and replacements. The resulting inventory forms the basis for subsequent risk assessment and remediation planning.

Mapping cryptographic dependencies involves identifying how the security of one digital asset is contingent upon the security of others within a system. This extends beyond simply listing assets to detailing the specific cryptographic relationships between them; for example, a service’s encryption may rely on a specific certificate chain, or a server’s authentication may depend on the integrity of a root key. Failure to account for these dependencies can create hidden vulnerabilities, as a compromise in a seemingly unrelated asset could cascade and impact the security of critical systems. A thorough dependency map details these connections, allowing organizations to prioritize remediation efforts based on the potential impact of a compromise and to accurately assess the scope of required post-quantum cryptographic (PQC) upgrades.

A Knowledge Graph facilitates quantum risk assessment by providing a structured visualization of digital assets and their cryptographic interdependencies. This system employs 1818 specialized scanner modules designed to identify and categorize diverse risk factors and asset types, enabling detailed analysis of potential vulnerabilities. These modules perform automated discovery and mapping, creating a comprehensive inventory of cryptographic implementations across an organization’s infrastructure. The resulting graph allows security teams to model attack paths, prioritize remediation efforts, and understand the cascading impact of compromised cryptographic algorithms in a post-quantum environment.

The External Asset Discovery System leverages a Python-based scanner framework and <span class="katex-eq" data-katex-display="false">ThreadPoolExecutor</span> to concurrently analyze assets across six risk categories, integrating real-time threat intelligence from sources like NIST NVD and CISA KEV, and ultimately constructing a knowledge graph from scan results via an LLM-assisted ETL pipeline and GraphBuilderService. — The External Asset Discovery System leverages a Python-based scanner framework and $ThreadPoolExecutor$ to concurrently analyze assets across six risk categories, integrating real-time threat intelligence from sources like NIST NVD and CISA KEV, and ultimately constructing a knowledge graph from scan results via an LLM-assisted ETL pipeline and GraphBuilderService.

Quantifying the Inevitable: A Systemic Risk Assessment

Cyber-risk quantification for quantum threats involves translating the potential impact of successful attacks into monetary values. This process requires identifying assets vulnerable to quantum computing-enabled decryption, estimating the cost of data breaches – including notification, remediation, and legal fees – and projecting potential revenue loss or reputational damage. By assigning numerical values to these impacts, organizations can calculate Annualized Loss Expectancy (ALE) or similar metrics to understand their financial exposure. This allows for a cost-benefit analysis of implementing quantum-resistant cryptography and other mitigation strategies, providing a basis for prioritizing investments in quantum cybersecurity. The quantification should account for the evolving landscape of quantum computing capabilities and the potential for cryptanalytic breakthroughs, adjusting risk assessments as new information becomes available.

Accurate assessment of vulnerability severity requires leveraging established standards like the Common Vulnerability Scoring System (CVSS) and utilizing data from reputable sources. The National Vulnerability Database (NVD), maintained by NIST, provides comprehensive vulnerability information, while the CISA Known Exploited Vulnerabilities (KEV) catalog highlights actively exploited vulnerabilities. Within a quantum cybersecurity risk framework, these sources are not equally weighted; KEV is assigned a weighting of 1.0, reflecting the immediacy of the threat, while NIST NVD receives a weighting of 0.9. This tiered approach allows for prioritization of vulnerabilities based on real-world exploitation status, ensuring that mitigation efforts are focused on the most critical risks.

Graph-based risk analytics, when applied to a comprehensive Knowledge Graph representing an organization’s assets, vulnerabilities, and threat landscape, facilitates the identification of previously unknown dependencies between systems. This allows for a more accurate assessment of potential attack paths and the cascading impact of successful exploits. The analytical framework achieves a 99.6% validity rate when confirming relationships within the Knowledge Graph, indicating a high degree of confidence in the identified dependencies. This capability enables organizations to prioritize mitigation efforts based on the criticality of interconnected assets and efficiently allocate resources to address the most significant risks, rather than focusing solely on isolated vulnerabilities.

Validating Resilience: A Systemic Preparedness Check

Organizations face an escalating threat from quantum computing, necessitating a clear understanding of their preparedness for Post-Quantum Cryptography (PQC). To address this, a Post-Quantum Readiness Scoring system has been developed, offering a quantifiable metric to evaluate an organization’s current standing. This scoring isn’t simply a pass/fail assessment; it provides a nuanced evaluation across critical areas, including cryptographic agility, key management practices, and data lifecycle policies. The resulting score allows organizations to pinpoint vulnerabilities, prioritize mitigation efforts, and track progress towards a quantum-resistant future. By translating complex security postures into a single, actionable number, the scoring system empowers leadership to make informed decisions and allocate resources effectively, ultimately reducing the risk associated with the looming quantum threat and ensuring long-term data security.

The integration of Large Language Models (LLMs) represents a significant advancement in bolstering the accuracy and speed of data analysis within complex Knowledge Graphs, particularly as organizations prepare for post-quantum cryptography. LLM-Assisted Validation and full LLM Validation processes automate the verification of relationships and data points, substantially reducing the need for manual review. This approach has demonstrated remarkable efficiency, achieving a remarkably low 0.4% disagreement rate – meaning only four out of every one thousand validations require human oversight. By intelligently cross-referencing information and identifying potential inconsistencies, these models not only accelerate the validation process but also elevate the overall reliability of the Knowledge Graph, providing a more trustworthy foundation for quantum risk preparedness strategies.

The advent of accessible Large Language Models (LLMs) such as Ollama and Google Gemma is fundamentally shifting the landscape of quantum risk validation. These tools allow organizations to deploy and operate LLMs locally, bypassing the need to transmit sensitive data to external servers and significantly bolstering security and privacy during the validation process. This localized approach facilitates the rigorous analysis of knowledge graphs used to assess post-quantum cryptographic (PQC) readiness, resulting in a high degree of confidence in the validated relationships – averaging 94.6% – and minimizing the need for costly and time-consuming manual review. By enabling secure, in-house validation, these LLMs are not merely enhancing efficiency, but also empowering organizations to proactively safeguard their critical data in the face of evolving quantum threats.

The Path Forward: Building a Quantum-Resilient Future

The looming threat of quantum computers necessitates a fundamental shift in cryptographic practices, and a proactive embrace of Post-Quantum Cryptography (PQC) standards is now paramount for sustained digital security. Organizations like the National Institute of Standards and Technology (NIST) and the European Telecommunications Standards Institute (ETSI) are actively developing and vetting these new algorithms, designed to resist attacks from both classical and quantum computing. Early adoption isn’t simply about future-proofing; it’s about mitigating the risk of “harvest now, decrypt later” attacks, where malicious actors are already intercepting encrypted data with the intention of decrypting it once quantum computers become powerful enough. A deliberate transition, guided by these established standards, allows organizations to systematically update their systems, ensuring compatibility and interoperability while minimizing disruption – a critical step in preserving the confidentiality, integrity, and authenticity of digital information for decades to come.

An organization’s vulnerability to future quantum computing attacks isn’t solely determined by the strength of its cryptography, but crucially by its network exposure – the extent to which its communications and data are accessible to potential adversaries. High network exposure significantly amplifies the risk posed by compromised cryptographic keys, as a larger attack surface increases the probability of successful interception and decryption. Therefore, prioritizing protection efforts requires a detailed assessment of an organization’s network topology, identifying critical data flows, and understanding which systems are most actively communicating externally. Focusing resources on securing those systems with post-quantum cryptography, rather than a blanket implementation across all assets, provides a more efficient and effective pathway to building a quantum-resilient infrastructure. This targeted approach acknowledges that the impact of a cryptographic break is directly proportional to the volume and sensitivity of data traversing exposed networks.

The Post-Quantum Readiness Index offers a crucial, quantifiable method for organizations to assess and track their preparation for the advent of quantum computing threats. This index doesn’t simply offer a static snapshot; it functions as a dynamic benchmark, allowing continuous monitoring of progress across key areas like cryptographic agility, key management practices, and algorithm migration strategies. By regularly evaluating performance against established metrics, organizations can pinpoint vulnerabilities, prioritize remediation efforts, and demonstrably improve their resilience. The index facilitates a proactive approach, moving beyond theoretical risk assessment to concrete, measurable improvements in post-quantum security posture, ultimately ensuring long-term data protection in a rapidly evolving technological landscape.

The framework detailed within prioritizes systemic understanding, mirroring a core tenet of robust security design. As John McCarthy observed, “It is better to deal with reality than with abstract concepts.” This approach directly applies to the presented work; the knowledge graph isn’t merely a representation of post-quantum cryptographic risks, but a dynamic model of an enterprise’s actual PQ readiness. By mapping dependencies and vulnerabilities, the system moves beyond theoretical assessments, offering a quantifiable PQ Readiness Scoring based on concrete data. This commitment to observable reality, rather than abstract idealizations, provides a foundation for effective risk mitigation and informed migration strategies.

What’s Next?

This work treats the enterprise as a system, and cybersecurity as an emergent property – a decent start, but only a start. The framework presented here isn’t a solution; it’s a particularly detailed map of the problem. Reality, after all, is open source – the code is there, but it’s the sheer complexity of the system that obscures its logic. The current iteration highlights vulnerabilities in the transition to post-quantum cryptography, but future work must address the far more subtle problem of systemic risk – the unknown unknowns that reside in the interactions between systems, not within them.

The reliance on current LLMs is, of course, a limitation. These models are pattern-matchers, exceptionally good at seeming to understand, but ultimately lacking genuine comprehension. A truly robust framework demands models capable of causal reasoning, not just correlation. Beyond that, the knowledge graph itself is incomplete – a reflection of the constantly evolving threat landscape and the inherent opacity of complex organizations. The challenge isn’t simply populating the graph with more data, but developing algorithms that can dynamically infer relationships and anticipate future vulnerabilities.

Ultimately, the goal isn’t to build a perfect security system – that’s a category error. It’s to build a system that is provably incomplete, that acknowledges its own limitations, and that can adapt and evolve as the code of reality is progressively reverse-engineered. The true measure of success won’t be the absence of breaches, but the speed and effectiveness of response when – not if – those breaches inevitably occur.

Original article: https://arxiv.org/pdf/2601.03504.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/