Context-Aware Security Policies for Data-Driven Applications in Cloud-Native Architectures

Providers of cloud-native applications require security frameworks which respond to the contextually flowing, evolving, and multifaceted structure of applications and their data streams. Existing enforcement policies overlook-contextual workload behavior, user roles, and environmental metadata, resulting in gaps in policy enforcement as well as high false-positive rates. This research proposes a context-aware policy enforcement framework applied into admission controllers and service mesh layers that are Kubernetes-native. To determine accuracy, latency, and overhead, a hybrid dataset composed of synthetic microservices and policy violation logs was analyzed. Experimental results demonstrate inclusion of contextual signals improves detection accuracy by 41% while maintaining policy decision latency under 8ms in 95% of test cases. Predictive benchmarks based on prior performance demonstrated a further 36% reduction in false positives when compared to threshold-based approaches. With the ability to enforce consistently across tenants and workloads, the proposed framework provides high precision, low latency policy enforcement while enabling proactive mitigation of context drift. These results enable the implementation of secure, scalable DevSecOps workflows in cloud-native environments.