HUMAN–AI COLLABORATIVE FRAMEWORK FOR EVALUATING EMERGING AUTONOMOUS CYBER RISKS

Abstract

The growing adoption of autonomous artificial intelligence across enterprise environments has introduced complex cybersecurity risks that require transparent, collaborative, and trustworthy evaluation mechanisms. Human expertise remains essential for validating AIgenerated assessments, interpreting complex security events, and ensuring ethical governance throughout the cybersecurity lifecycle. This paper proposes a Human–AI collaborative framework integrating machine learning, explainable artificial intelligence, behavioral analytics, cloudnative DevSecOps, Zero-Trust architecture, threat intelligence, continuous monitoring, and enterprise governance. The proposed methodology combines automated AI-driven cybersecurity analytics with human expert validation to improve threat assessment accuracy, governance consistency, operational reliability, and regulatory compliance. Experimental evaluation demonstrates improvements in defensive decision quality, threat detection accuracy, explainability, organizational resilience, and enterprise cybersecurity performance. The proposed framework provides a scalable and production-ready solution for evaluating emerging autonomous cyber risks while supporting responsible AI deployment, ethical decision-making, and secure enterprise digital transformation. Keywords— Human–AI Collaboration, Explainable Artificial Intelligence, Cyber Risk Assessment, Behavioral Analytics, DevSecOps, Zero-Trust Security, Enterprise Governance, Threat Intelligence.