CyTReX: Explainable AI-Based Cybersecurity Threat Reasoning Framework for DER Networks

AI explanations are essential for security decisions: The framework uses SHAP (Shapley Additive Explanations) and decision rules to show why the AI flagged a threat, not just what it detected. This transparency helps security analysts understand and verify the system's reasoning rather than blindly trusting a single label.

Multiple threat interpretations matter more than single predictions: Rather than telling analysts "this is an attack," CyTReX generates ranked alternative threat hypotheses for the same alert. This acknowledges that the same suspicious network behavior could indicate different types of threats, requiring human judgment to determine the correct interpretation.

AI systems need structured constraints to be trustworthy: The framework constrains the AI's reasoning through an "evidence packet" combining detection outputs, threat intelligence, and model explanations. This structured approach prevents the AI from making unsupported claims and ensures every conclusion is traceable back to concrete evidence.

Context matters for critical infrastructure security: By integrating cyber threat intelligence and edge-layer detection evidence, the system provides security teams with relevant context about DER (Distributed Energy Resource) networks specifically. Generic AI reasoning without domain-specific information would be insufficient for protecting power systems.

Human-AI collaboration requires clear accountability: The framework is designed for Security Operations Center (SOC) triage, meaning humans remain in control of final decisions. By making AI reasoning transparent and traceable, the system supports human oversight rather than replacing human judgment in critical security decisions.