Next-Generation Cyber War Rooms: Autonomous Adversarial Benchmarking of Multi-Tier Defense Systems

Modern cyber threats evolve at machine speed, rendering static benchmarks obsolete, particularly for small and medium-sized enterprises (SMEs) seeking cost-effective and verifiable resilience. However, this study presents a forward-looking methodology for evaluating multi-tier open source defense systems through autonomous adversarial benchmarking in cyber war room simulations. Traditional benchmarks that focus on throughput, CPU usage, and false positive rates are insufficient against adaptive AI enabled threats. We propose a closed loop framework in which AI agents, including reinforcement learning and large language models with optional generative adversarial components, autonomously generate novel attack vectors such as evasions, polymorphic malware, and spear phishing to challenge detection, analysis, and response across six defense layers. Telemetry from all layers is consolidated in the SIEM and converted into operational metrics, including Time to Bypass, Defensive Adaptation Rate, Mean Time to Knowledge, and Resilience Score. These metrics drive a feedback loop that updates defensive content and the adversarial models and enable continuous and repeatable evaluation. The design is cost effective and reproducible for small and medium sized enterprises through virtualization, open source tooling, and strict sandboxing with network isolation, and it improves organizational readiness against evolving AI enabled attacks.