A Method for Quantifying Human Risk and a Blueprint for LLM Integration
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866909836521766912 |
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| author | Canale, Giuseppe |
| author_facet | Canale, Giuseppe |
| contents | This paper presents the Cybersecurity Psychology Framework (CPF), a novel methodology for quantifying human-centric vulnerabilities in security operations through systematic integration of established psychological constructs with operational security telemetry. While individual human factors-alert fatigue, compliance fatigue, cognitive overload, and risk perception biases-have been extensively studied in isolation, no framework provides end-to-end operationalization across the full spectrum of psychological vulnerabilities. We address this gap by: (1) defining specific, measurable algorithms that quantify key psychological states using standard SOC tooling (SIEM, ticketing systems, communication platforms); (2) proposing a lightweight, privacy-preserving LLM architecture based on Retrieval-Augmented Generation (RAG) and domain-specific fine-tuning to analyze structured and unstructured data for latent psychological risks; (3) detailing a rigorous mixed-methods validation strategy acknowledging the inherent difficulty of obtaining sensitive cybersecurity data. Our implementation of CPF indicators has been demonstrated in a proof-of-concept deployment using small language models achieving 0.92 F1-score on synthetic data. This work provides the theoretical and methodological foundation necessary for industry partnerships to conduct empirical validation with real operational data. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2510_09635 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Method for Quantifying Human Risk and a Blueprint for LLM Integration Canale, Giuseppe Cryptography and Security 68M25, 91E30, 68T50 K.6.5; H.1.2; I.2.7 This paper presents the Cybersecurity Psychology Framework (CPF), a novel methodology for quantifying human-centric vulnerabilities in security operations through systematic integration of established psychological constructs with operational security telemetry. While individual human factors-alert fatigue, compliance fatigue, cognitive overload, and risk perception biases-have been extensively studied in isolation, no framework provides end-to-end operationalization across the full spectrum of psychological vulnerabilities. We address this gap by: (1) defining specific, measurable algorithms that quantify key psychological states using standard SOC tooling (SIEM, ticketing systems, communication platforms); (2) proposing a lightweight, privacy-preserving LLM architecture based on Retrieval-Augmented Generation (RAG) and domain-specific fine-tuning to analyze structured and unstructured data for latent psychological risks; (3) detailing a rigorous mixed-methods validation strategy acknowledging the inherent difficulty of obtaining sensitive cybersecurity data. Our implementation of CPF indicators has been demonstrated in a proof-of-concept deployment using small language models achieving 0.92 F1-score on synthetic data. This work provides the theoretical and methodological foundation necessary for industry partnerships to conduct empirical validation with real operational data. |
| title | A Method for Quantifying Human Risk and a Blueprint for LLM Integration |
| topic | Cryptography and Security 68M25, 91E30, 68T50 K.6.5; H.1.2; I.2.7 |
| url | https://arxiv.org/abs/2510.09635 |