BENCHMARKING DEFENSIVE ARTIFICIAL INTELLIGENCE THROUGH OPEN CYBER COMPETITIONS

Abstract

Open cyber competitions provide standardized, transparent, and reproducible environments for evaluating the effectiveness of artificial intelligence in cybersecurity. Ethical competitions enable researchers, security professionals, and organizations to benchmark defensive AI capabilities, assess system robustness, validate detection performance, and improve operational resilience without promoting offensive cyber operations. This paper proposes a benchmarking framework integrating machine learning, cloud-native DevSecOps, behavioral analytics, Zero-Trust security, explainable artificial intelligence, human-in-the-loop validation, continuous monitoring, and enterprise governance. The proposed methodology establishes standardized evaluation metrics, controlled cyber ranges, collaborative testing environments, and automated performance assessment to compare defensive AI models under realistic operational conditions. Experimental evaluation demonstrates improvements in detection accuracy, governance consistency, benchmarking transparency, operational reliability, and enterprise cyber resilience. The proposed framework provides a scalable and production-ready solution for evaluating defensive artificial intelligence while supporting responsible AI deployment, regulatory compliance, and secure digital transformation. Keywords— Defensive Artificial Intelligence, Cybersecurity Benchmarking, Machine Learning, Cyber Competitions, Explainable AI, DevSecOps, Enterprise Governance, Threat Detection.