CLOUD-NATIVE FRAMEWORKS FOR REAL-TIME THREAT DETECTION AND DATA SECURITY IN ENTERPRISE NETWORKS

Abstract

This study investigates how cloud-native frameworks relate to real-time threat detection and enterprise data security in production-proximate organizations. Foundational studies on anomaly detection and intrusion detection systems (IDS) establish that “real time” in network defense is not merely a latency target but a precondition for identifying malicious deviations from normal behavior at the speed of modern infrastructure. The problem addressed is that elastic, microservice-based estates generate high-velocity telemetry while dissolving traditional perimeters, making timely detection and consistent data protection difficult. The purpose is to quantify the effects of cloud-native adoption on detection performance and data-security posture, and to test whether observability and zero-trust practices condition those effects. Design: quantitative, cross-sectional, case based. Sample: 185 mid- to large-scale cloud and hybrid enterprise cases with role-qualified practitioners. Key variables: cloud-native adoption, observability maturity, zero-trust practices, data-security posture, and detection performance. Objective indicators collected for the most recent quarter include mean time to detect, mean time to respond, true-positive rate, and false-positive rate. Analysis plan: reliability and CFA for construct validity; multiple regression with controls; moderation via interaction terms; mediation via bootstrap indirect effects; robustness with HC3 errors and clustered sensitivity checks. Headline findings: scales were reliable and valid (α and CR ≥ .80; CFA fit CFI and TLI > .92). Cloud-native adoption predicted better detection performance (β = .31, p < .001) and stronger data-security posture (β = .28, p < .001). Observability strengthened the adoption to detection link (interaction β = .14, p = .004) and zero-trust strengthened the adoption to posture link (interaction β = .12, p = .012). Posture partially mediated adoption’s effect on detection (indirect = .09, 95 percent CI [.05, .15]). Operationally, top-quartile adopters achieved median MTTD 18 vs 42 minutes, MTTR 55 vs 110 minutes, TPR .87 vs .72, FPR .06 vs .11, and an estimated F1 of 0.87 vs 0.77. Implications: prioritize observability hygiene, identity-centric zero trust, and policy-as-code to convert architecture into measurable security outcomes.