Artificial Intelligence-Enabled Vulnerability Management in Complex Enterprise Ecosystems: A Multi-Domain Analytical Study

Abstract

The rapid expansion of enterprise information systems has intensified the complexity of dependency networks and exposed organizations to unprecedented security vulnerabilities. Modern enterprises, characterized by distributed architectures, heterogeneous platforms, and dynamic workloads, face the persistent challenge of efficiently identifying, prioritizing, and mitigating system vulnerabilities while minimizing operational disruption. Artificial intelligence (AI), particularly through machine learning and predictive analytics, offers transformative potential in automating vulnerability detection, resolution, and risk prioritization. This research investigates the role of AI-assisted approaches in dependency vulnerability management within large-scale enterprise ecosystems. By synthesizing theoretical perspectives from cybersecurity, software engineering, and system management, this study examines AI methodologies for detecting latent vulnerabilities, evaluating risk exposure, and optimizing remediation strategies. Using a mixed-methods approach informed by historical case studies, computational simulations, and industry-driven analyses, the research emphasizes not only algorithmic effectiveness but also organizational integration, stakeholder trust, and systemic resilience. Critical discussion encompasses the evaluation of supervised, unsupervised, and reinforcement learning frameworks in predicting dependency risks, as well as the challenges posed by data sparsity, model interpretability, and adaptive adversarial tactics. Comparative insights with conventional vulnerability management approaches are provided, highlighting efficiency gains, predictive accuracy, and cost-benefit implications. Ethical considerations, such as transparency, bias mitigation, and regulatory compliance, are also addressed. This study contributes to the interdisciplinary understanding of AI-enabled enterprise security, offering practical frameworks for deployment, strategic recommendations for scalability, and directions for future empirical research.