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As digital infrastructures expand, the scalable and context-aware management of software vulnerabilities is crucial to organizations. This article offers an integration of international research trends since 2020 regarding how the Common Vulnerability Scoring System (CVSS v3) and Common Weakness Enumeration (CWE) can be incorporated into dynamic and scalable Vulnerability Management (VM) lifecycles. Through examination of the state-of-the-art, real-world deployments, and experimental outcomes, the paper finds strengths in machine learning integration, DevSecOps orchestration, and context-aware risk modeling. However, there are some challenges, such as the limitations of static scoring, interoperability limitations, and limited data availability. The review concludes with a layered model proposing the joining of explainable AI, asset metadata, and feedback loops to result in predictive, business-aligned vulnerability management. We provide directions for future work to address outstanding research challenges and aid the advancement of smart, resilient VM systems
Keywords:
Vulnerability Management, Common Vulnerability Scoring System, Common Weakness Enumeration, Context-Aware Risk Prioritization, Explainable Artificial Intelligence
Cite Article:
"Designing a Scalable Vulnerability Management Lifecycle Using CVSS v3 and CWE Classification", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 8, page no.a337-a345, August-2025, Available :http://www.ijrti.org/papers/IJRTI2508041.pdf
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000773
ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator