Explainable AI-Driven Cyber Risk Analytics and Model Reliability Assessment for Intelligent Governance of U.S. Critical Infrastructure: An XGBoost and SHAP-Based Intrusion Detection Framework

Explainability is critical for cybersecurity decisions: The paper uses SHAP (SHapley Additive exPlanations) alongside XGBoost to make AI intrusion detection systems interpretable, meaning operators can understand why the system flagged a threat rather than treating it as a "black box"—essential for building trust in automated security decisions affecting critical infrastructure.

Multiple AI models improve detection reliability: The study compares XGBoost, Random Forest, and Decision Tree classifiers to detect cyber threats, suggesting that no single AI approach is perfect; using multiple models together provides more robust protection against evolving attacks like DDoS, ransomware, and Advanced Persistent Threats (APTs).

AI governance requires reliability assessment frameworks: Beyond just deploying AI systems, the research emphasizes the need for formal frameworks to assess model reliability and performance, ensuring that automated decision-making systems protecting energy, healthcare, transportation, and financial infrastructure meet trustworthiness standards before deployment.

Traditional cybersecurity alone is insufficient: The paper identifies that conventional security mechanisms fall short against modern threats in dynamic network environments, positioning AI-driven analytics as a necessary complement—though this underscores the importance of carefully validating these new systems before relying on them for critical infrastructure protection.