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Traditional penetration testing and vulnerability assessment techniques similarly rely on static, predefined evaluation models that fail to capture adaptive adversarial behavior. This paper presents RED (Resilient Environment for Defense testing), an AI-driven adaptive red team evaluation framework designed to quantitatively assess cyber defense resilience in controlled environments. RED models the attacker as a reinforcement learning agent that dynamically selects reconnaissance and probing actions based on observed system behavior, execution efficiency, and intrusion detection system (IDS) feedback. Unlike conventional tools, RED synthesizes vulnerabilities from exposure signals and defensive inconsistencies, enabling meaningful security evaluation even when exploitation is unsuccessful or intentionally constrained. The framework incorporates run-isolated execution, exposure-aware vulnerability synthesis, and a defense-oriented resilience scoring model that captures detection effectiveness, false negatives, and response latency. Experimental evaluation conducted in a virtualized cyber range using Kali Linux against multiple vulnerable testbeds demonstrates that RED produces richer attack surface characterization and significantly improved visibility into defensive behavior compared to static vulnerability scanners and automated exploitation frameworks. The results validate RED as a practical, safe, and reproducible methodology for evaluating cyber defense robustness under adaptive attack coditions.