Intrusion Detection and Prevention Systems (IDS/IPS) are a critical part of modern network security architectures, yet the operational behavior of AI-driven solutions before deployment remains poorly understood. While machine learning and deep learning approaches show strong detection capabilities in controlled environments, benchmark results often overlook real-world constraints such as traffic variability, processing pipelines, queueing delays, and resource contention, all of which impact effectiveness. This study provides a predictive, analytical framework for AI-enhanced IDS/IPS systems, focusing on realistic operational performance rather than empirical testing. Using deep learning detectors as baselines, the framework models detection latency, throughput limits, scalability, and performance degradation under load, incorporating pipeline-aware latency and queueing effects to avoid overly optimistic assumptions. Under clearly defined conditions, the analysis forecasts detection accuracy between 95–98%, false positive rates of 0.5–2%, and end-to-end latency of 150–400 ms depending on utilization. These estimates serve as conservative performance bounds, offering transparent, rigorous insights for deployment planning and laying the groundwork for future empirical validation.