Vulnerability Management at Scale: Automated Frameworks for 100K+ Asset Environments

Abstract

Enterprises operating at hyperscale spanning over 100,000 endpoints, servers, and cloud assets face unique challenges in managing vulnerabilities effectively. Traditional vulnerability management (VM) tools and workflows struggle to cope with the volume, velocity, and contextual complexity of findings in such environments. Manual triage, CVSS-only scoring, and siloed remediation processes result in delayed response times, audit failures, and heightened risk exposure. As the attack surface grows dynamically, organizations require scalable, automated solutions that provide continuous visibility, contextual risk prioritization, and orchestrated remediation.
This paper proposes a modular, automation-driven framework for vulnerability management at scale. The architecture integrates continuous asset discovery, threat enrichment, risk-based prioritization, and response automation using tools such as Tenable, ServiceNow, and Cortex XSOAR. It shifts prioritization from static scoring models to contextual models incorporating exploitability, asset criticality, and threat intelligence sources like EPSS and CISA KEV. Evaluations conducted across large enterprises demonstrate a 55% reduction in mean time to remediation (MTTR), a 2.3x improvement in SLA adherence, and a 75% reduction in manual remediation effort. Case studies validate the framework's effectiveness in complex, compliance-driven industries such as healthcare and financial services. The paper concludes with strategic recommendations and future directions involving AI-based risk modeling, SBOM integration, and Zero Trust enforcement. This research offers a repeatable blueprint for security leaders seeking to operationalize vulnerability management in high-scale environments through automation, intelligence, identity and access governance and cross-platform integration.