Runtime Threat Detection in Multi-Cloud Workloads: From Drift Detection to Real-Time Blocking
Year: 2025
Type: article
Abstract: Traditional perimeter defenses prove insufficient against sophisticated threats targeting ephemeral workloads and dynamic cloud environments. Runtime security emerges as a critical layer of protection by continuously monitoring workload behavior and identifying anomalies as they occur. This article presents a comprehensive framework for implementing runtime threat detection across containers, serverless functions, and API workloads in multi-cloud deployments using agentless Cloud-Native Application Protection Platform (CNAPP) solutions. Core detection mechanisms, including configuration drift monitoring, privilege escalation prevention, and memory-based attack identification, are examined alongside behavioral baseline establishment techniques. The framework demonstrates how runtime sensors reduce false positives through contextual risk assessment while seamlessly integrating with Security Information and Event Management (SIEM) and Security Orchestration, Automation and Response (SOAR) platforms. Implementation results indicate significant improvements in Mean Time to Detect (MTTD) and Mean Time to Respond (MTTR), validating the effectiveness of runtime security in enhancing overall cloud security posture. The proposed framework provides organizations with actionable strategies for deploying real-time blocking mechanisms and automated response workflows that adapt to the dynamic nature of modern cloud infrastructures.
Author: Naresh Kiran Kumar Reddy Yelkoti
Institution: Wilmington University
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4411352105