The rapid evolution of enterprise information systems, coupled with increased reliance on interconnected digital infrastructures, has amplified the complexity and susceptibility of software ecosystems to dependency vulnerabilities. These vulnerabilities, if unaddressed, can propagate across systems, resulting in operational disruption, financial loss, and reputational damage. This research critically examines AI-assisted approaches to identifying, mitigating, and resolving dependency vulnerabilities in large-scale enterprise systems. Building upon the theoretical foundation of machine learning, data analytics, and autonomous system design, the study integrates multiple perspectives from supply chain risk management, cloud-native architectures, cybersecurity protocols, and healthcare informatics to construct a comprehensive model for proactive vulnerability management. The study employs qualitative synthesis of secondary research and extensive case analysis of enterprise systems, emphasizing real-time dependency monitoring, automated patch application, and predictive risk modeling. The findings indicate that AI-powered interventions significantly enhance resilience by reducing detection latency, optimizing resource allocation for remediation, and enabling predictive forecasting of potential vulnerability exploitations. Moreover, the integration of AI within enterprise dependency frameworks facilitates adaptive learning from historical incidents, improves interdepartmental coordination, and provides actionable insights to stakeholders for decision-making under uncertainty. The study highlights critical theoretical debates surrounding ethical AI deployment, explainability, and risk governance, positioning AI-assisted vulnerability resolution as a strategic imperative for sustainable enterprise operations. The research concludes by outlining future directions for hybrid human-AI governance, enhanced interpretability frameworks, and the development of standardized metrics for AI effectiveness in enterprise dependency management.

AI-Assisted Vulnerability, Enterprise Systems, Dependency Resolution, Predictive Risk Modeling

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