Managing Nth-Party Risk in AI Supply Chains: A Framework for Assessing Vendor, Model, and Dependency Risks in Multi-Layered AI Ecosystems

Abstract

AI systems are increasingly dependent on multi-layered supply chains, including foundation models, APIs, datasets, and tooling, creating complex Nth-party risk exposure. Traditional third-party risk management frameworks are inadequate for addressing dynamic dependencies, cascading vulnerabilities, and continuous model updates. This paper proposes a structured framework for assessing and governing Nth-party AI risks, combining supply chain mapping, dependency classification, risk propagation modeling, and multi-dimensional assessment metrics. Continuous monitoring and adaptive risk scoring provide real-time visibility into evolving vulnerabilities, while integration with enterprise risk management and regulatory standards ensures accountability and compliance. Operational recommendations emphasize vendor transparency, cross-functional governance, continuous auditing, and risk-based procurement strategies. By embedding these practices into AI lifecycles, organizations can proactively mitigate inherited risks, reduce systemic exposure, and maintain regulatory compliance. The framework provides a comprehensive approach to Nth-party AI risk, supporting resilient, secure, and auditable AI ecosystems capable of withstanding emerging threats and operational challenges.