Real-time threat detection in cloud and enterprise settings remains constrained by high alert noise, data drift, and limited analyst capacity; organizations need evidence on whether deeper AI integration improves detection timeliness and operational quality. This study’s purpose is to quantify the association between AI-enhanced cybersecurity frameworks and measurable outcomes under routine operations. We adopt a quantitative, cross-sectional, case-based design spanning 18 heterogeneous deployments across cloud-first, hybrid, and on-premises environments. The sample consists of cloud and enterprise cases that meet inclusion criteria for active SOCs, centralized logging, and at least one AI-driven detection or orchestration component. Following a targeted review of 100 peer-reviewed papers to ground constructs and measures, we operationalize an AI Integration Index and model its relationship to key variables detection latency, precision, recall, F1, false-positive rate, and mean time to respond using a pre-registered analysis plan: descriptive profiling, correlation screening with multiple-comparison control, robust OLS and median quantile regression for continuous outcomes, beta or
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