Abstract

The increasing integration of Artificial Intelligence (AI) into national cyber defense systems has fundamentally transformed threat detection, predictive analytics, and automated incident response. While these AI-driven capabilities enhance operational efficiency and resilience, they also introduce novel vulnerabilities, including susceptibility to adversarial attacks, model drift, cascading failures, and automation bias. This paper examines the pivotal role of AI safety evaluations in mitigating such risks, ensuring the reliability, robustness, and trustworthiness of AI-enabled defense mechanisms. It explores contemporary evaluation frameworks, performance and safety metrics, and methodological approaches to testing AI systems under simulated and real-world cyber threats. Through case studies of AI-powered intrusion detection systems, autonomous Security Operation Centers (SOC), and hybrid human-AI architectures, the study demonstrates measurable improvements in threat response accuracy, operational safety, and analyst decision-making. Furthermore, it outlines risk mitigation strategies, including continuous model monitoring, scenario-based testing, and policy integration, emphasizing the importance of human-in-the-loop (HITL) interventions. The findings highlight that systematic AI safety evaluations are essential not only for technical reliability but also for strategic national security, providing a framework for policymakers, cybersecurity professionals, and AI developers to enhance cyber resilience against evolving threats.

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