XAI-Based Dynamic Cybersecurity Risk Management and Its Conceptual Architecture

Abstract

Managing cybersecurity risk is a basic requirement for preserving systemic resilience in an increasingly unstable digital world, regardless of an organization's size or operational type. There is a noticeable knowledge asymmetry in the corporate cyber insurance underwriting market due to the inability of traditional qualitative or static risk assessment techniques to identify evolving threat vectors. To provide real-time, dynamic cybersecurity threat quantification, this survey offers a thorough analysis of cutting-edge architectures that combine Explainable Artificial Intelligence (XAI) with bimodal Large Language Models (LLMs). This study develops an objective operational framework for risk transparency. It carefully evaluates multi-col-linear feature correlation measures, interpretive mathematical frameworks like SHAP (Shapley Additive explanations), and natural language and source-code tokenization engines like CodeBERT. Additionally, we investigate how these automated approaches directly link control verifications to the National Institute of Standards and Technology (NIST) Special Publication 800-53 protocol while balancing intricate technological vulnerability telemetries with conventional regulatory frameworks. In the end, this survey creates a structured taxonomy of current literature, describes structural mapping procedures, pinpoints systemic research gaps concerning the discoverability of open-source assets, and delineates crucial future paths for constructing transparent, auditable, and insurable corporate technical infrastructures.