dorsal/arxiv
View SchemaRiskBridge: Turning CVEs into Business-Aligned Patch Priorities
| Authors | Yelena Mujibur Sheikh, Awez Akhtar Khatik, Luoxi Tang, Yuqiao Meng, Zhaohan Xi |
|---|---|
| Categories | |
| ArXiv ID | 2601.06201vv1 |
| URL | https://arxiv.org/abs/2601.06201 |
| License | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ |
Abstract
Enterprises are confronted with an unprecedented escalation in cybersecurity vulnerabilities, with thousands of new CVEs disclosed each month. Conventional prioritization frameworks such as CVSS offer static severity metrics that fail to account for exploit probability, compliance urgency, and operational impact, resulting in inefficient and delayed remediation. This paper introduces RiskBridge, an explainable and compliance-aware vulnerability management framework that integrates multi-source intelligence from CVSS v4, EPSS, and CISA KEV to produce dynamic, business -- aligned patch priorities. RiskBridge employs a probabilistic Zero-Day Exposure Simulation (ZDES) model to forecast near-term exploit likelihood, a Policy-as-Code Engine to translate regulatory mandates (e.g., PCI DSS, NIST SP 800-53) into automated SLA logic, and an ROI-driven Optimizer to maximize cumulative risk reduction per remediation effort. Experimental evaluations using live CVE datasets demonstrate an 88% reduction in residual risk, an 18-day improvement in SLA compliance, and a 35% increase in remediation efficiency compared to state-of-the-art commercial baselines. These findings validate RiskBridge as a practical and auditable decision-intelligence system that unifies probabilistic modeling, compliance reasoning, and optimization analytics. The framework represents a step toward automated, explainable, and business-centric vulnerability management in modern enterprise environments
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"abstract": "Enterprises are confronted with an unprecedented escalation in cybersecurity vulnerabilities, with thousands of new CVEs disclosed each month. Conventional prioritization frameworks such as CVSS offer static severity metrics that fail to account for exploit probability, compliance urgency, and operational impact, resulting in inefficient and delayed remediation. This paper introduces RiskBridge, an explainable and compliance-aware vulnerability management framework that integrates multi-source intelligence from CVSS v4, EPSS, and CISA KEV to produce dynamic, business -- aligned patch priorities. RiskBridge employs a probabilistic Zero-Day Exposure Simulation (ZDES) model to forecast near-term exploit likelihood, a Policy-as-Code Engine to translate regulatory mandates (e.g., PCI DSS, NIST SP 800-53) into automated SLA logic, and an ROI-driven Optimizer to maximize cumulative risk reduction per remediation effort. Experimental evaluations using live CVE datasets demonstrate an 88% reduction in residual risk, an 18-day improvement in SLA compliance, and a 35% increase in remediation efficiency compared to state-of-the-art commercial baselines. These findings validate RiskBridge as a practical and auditable decision-intelligence system that unifies probabilistic modeling, compliance reasoning, and optimization analytics. The framework represents a step toward automated, explainable, and business-centric vulnerability management in modern enterprise environments",
"arxiv_id": "2601.06201",
"authors": [
"Yelena Mujibur Sheikh",
"Awez Akhtar Khatik",
"Luoxi Tang",
"Yuqiao Meng",
"Zhaohan Xi"
],
"categories": [
"cs.SE",
"cs.AI"
],
"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
"title": "RiskBridge: Turning CVEs into Business-Aligned Patch Priorities",
"url": "https://arxiv.org/abs/2601.06201",
"version": "v1"
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