Role of artificial intelligence in modern cybersecurity vulnerability management practices

Akinyemi, Adeyemi Mobolaji and Sims, Sherry (2025) Role of artificial intelligence in modern cybersecurity vulnerability management practices. World Journal of Advanced Research and Reviews, 26 (1). pp. 555-584. ISSN 2581-9615

Introduction: Modern cybersecurity landscapes have never been challenged so much in a highly dynamic digital transformation, and most of the organizations are not able to manage vulnerabilities across complex infrastructure environments efficiently. It analyzes the practice of vulnerability management (VM) in several sectors and finds large discrepancies between the technological capabilities and the expertise implementation. Therefore, organizations running in cloud and on- premises environments face challenges related to the creation of robust security postures when dealing with resource constraints. Methods: Research approach involved systematic reviewing of vulnerability management practices in various organizations mainly amongst enterprises that operate in the United States markets. Data collection incorporated quantitative measurements of VM metrics, AI implementation success rate, and analysis of conventional approach and approach that adopted AI in the security process. The primary sources of the research were structured questionnaires, performance measures’ analysis, and assessment of AI-based vulnerability solutions. Results: Findings showed that organizations that have deployed AI to enhance their vulnerability management processes gained 76% increased rate in threat identification as opposed to conventional techniques. It is proved that machine learning algorithms reach the 89% accuracy in the prioritization of the critical vulnerabilities, and ASs help to decrease the mean time to remediate the comparable value by 65%. The integration of deep capability in this case reduced the false positives by 82%, this is beneficial since it fully optimizes the use of resources. Discussion: There is a lot of evidence that shows that the integration of AI into vulnerability management work flow is hugely beneficial especially in terms of both time and accuracy in terms of threat detection. Artificial intelligence proves to be quite effective in contextualizing threats in certain environments within an organization; it does not belong to the shortcomings of CVSS-like scoring systems in this context. It is, therefore, clear that machine learning will not replace human intelligence in strategic thinking and discerning intricate vulnerability issues and therefore, the idea of hybrid human- Artificial Intelligence. Conclusion: Researched proofs provide substantial evidence for change that artificial Intelligence has brought in the current approach towards vulnerability management. The use of AI technologies is evidence based in the way that it strengthens the organization on capacity to detect, evaluate and mitigate risks within the context of security as well as increase resource efficiency. Considering that, future security frameworks should rely on a correct interaction between human-based skills and artificial intelligence-based options, with no elimination of one by another.