EXPLAINABLE THREAT INTELLIGENCE FOR AUTONOMOUS ADVERSARIAL AI CAPABILITY ASSESSMENT

Abstract

The rapid advancement of autonomous artificial intelligence has increased the need for transparent and trustworthy cybersecurity assessment frameworks capable of evaluating AI-related security risks while supporting responsible deployment. Explainable threat intelligence enables security analysts to understand the reasoning behind AI-assisted threat assessments, improve defensive decision-making, and strengthen organizational cyber resilience. This paper proposes an explainable threat intelligence framework integrating machine learning, explainable artificial intelligence (XAI), behavioral analytics, cloud-native DevSecOps, Zero-Trust architecture, continuous monitoring, human-in-the-loop validation, and enterprise governance. The proposed methodology continuously analyzes AI-related threat indicators, evaluates defensive readiness, generates interpretable risk assessments, and supports adaptive security policy refinement while maintaining regulatory compliance. Experimental evaluation demonstrates improvements in threat assessment transparency, behavioral analysis accuracy, governance efficiency, operational reliability, and enterprise cyber resilience. The proposed framework provides a scalable and production-ready solution for explainable AI-assisted threat intelligence within modern enterprise cybersecurity environments. Keywords— Explainable Artificial Intelligence, Threat Intelligence, Machine Learning, Behavioral Analytics, Cybersecurity, DevSecOps, Zero-Trust Security, Enterprise Governance.