Recent industry reporting indicates that meantime to exploit has become negative in several observed datasets, implying that exploitation may occur before patch availability for some classes of vulnerabilities. Adversarial use of artificial intelligence (AI) is a documented accelerant of this trend. This paper addresses the operational problem that follows in healthcare cybersecurity: the volume and velocity of vulnerability disclosure exceed human analytic capacity, which leads practitioners to under-prioritize, or defer entirely, individual Common Vulnerabilities and Exposures (CVEs) at precisely the moment their risk is rising. Adopting the design science research paradigm of Hevner et al. [8], the study develops and evaluates a purposeful information technology artifact intended to resolve this problem within a mid-sized United States healthcare system. The artifact is a three-application automated CVE intelligence, prioritization, and remediation-tracking pipeline implemented in Microsoft Azure Logic Apps, integrating the National Vulnerability Database (NVD), the CISA Known Exploited Vulnerabilities (KEV) catalog, the Microsoft Security Response Center (MSRC) CVRF API, Microsoft Defender, Claroty xDome, Microsoft Security Copilot, and ServiceNow, and operationalizing the four risk factors codified in CISA Binding Operational Directive (BOD) 26-04. In naturalistic operation across three CISA Weekly Vulnerability Summary bulletins, the artifact processed 5,216 unique CVE references and reduced them to 534 environment-relevant findings, an 89.8 percent exposure-first reduction, before expensive per-CVE enrichment and ticketing. Findings indicate that governed automation demonstrably increases CVE coverage, reduces low-value enrichment volume, and creates an auditable prioritization record. Because no controlled before-and-after time-and-motion study was conducted and no independent ground-truth severity labels were collected, remediation-speed and analyst-productivity outcomes remain future validation targets rather than demonstrated results.
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