This paper presents the Cybersecurity Psychology Framework (CPF), a novel taxonomy designed to categorize and quantify human-centric vulnerabilities in security operations. The core contribution of this work is two-fold: first, we operationalize the CPF by mapping its subcategories to specific, measurable indicators derived from standard SOC tooling (e.g., Splunk, Elasticsearch, Qualys) and communication platforms (e.g., Slack, Teams), formalizing these measures through algorithmic definitions. Second, we propose and detail a lightweight, efficient architecture for a Large Language Model (LLM) that leverages Retrieval-Augmented Generation (RAG) and targeted fine-tuning on a compact, domain specific corpus. This architecture is designed to analyze the structured and unstructureddata defined by the CPF algorithms to identify latent psychological risks. We argue that this approach makes sophisticated behavioral analysis computationally feasible and accessible, moving beyond theoretical taxonomy to provide a practical tool for proactive risk mitigation. The paper concludes with a methodology for validating the framework and its LLM component in a real-world environment.