Explainable AI Network Assesses Cybersecurity Risk with Shallow Architecture.

This paper presents a novel neural network architecture, the Neuro-Bayesian-Symbolic Residual Attention Shallow Network (NBS-RASN), specifically engineered for transparent cybersecurity risk assessment. Unlike many deep learning models that sacrifice interpretability for performance, NBS-RASN is a shallow network that incorporates domain expertise, causal logic, and expert judgment directly into its differentiable components. It features a unique "gatekeeper" layer that enforces five epistemological axioms, ensuring that all risk propagations adhere to principles like precision and causality. Despite its limited depth, the NBS-RASN leverages residual attention and feedback loops to learn complex risk patterns effectively, avoiding the "black box" problem common in deeper models. The network generates fully decomposable risk scores, which include a deterministic weighted component and an expert adjustment, with each adjustment clearly linked to specific risk amplifiers like blast radius or exploitation patterns. Validated across 20 open-source projects covering various OWASP Top 10 categories, the model achieved high confidence scores (0.79-0.97), demonstrating that explainability can be a core design principle rather than an afterthought. This work challenges the notion that deep learning necessitates deep networks, suggesting that shallow architectures with integrated deep reasoning can excel in critical domains like cybersecurity where interpretability is paramount.