Third-Party AI Vendor Risk: Developing Assessment Frameworks for Machine Learning Service Providers

The rapid integration of artificial intelligence (AI) and machine learning (ML) technologies into enterprise operations has fundamentally altered the vendor risk landscape. Organizations increasingly rely on third-party AI vendors for mission-critical processes such as fraud analytics, credit decisioning, underwriting, customer segmentation, and transportation route optimization. Traditional vendor risk methodologies, designed around static information systems, fail to account for model-dependent variability, training data sensitivity, algorithmic opacity, and the dynamic behaviors inherent to continuously learning systems. This research develops a comprehensive assessment framework explicitly tailored to the characteristics of AI vendors. Drawing from more than two hundred vendor assessments performed across the banking, insurance, and transportation sectors, the study identifies four emergent risk domains uniquely associated with AI service providers: model transparency risks, data lineage and provenance uncertainties, algorithmic bias vulnerabilities, and drift susceptibility associated with real-time or periodic retraining cycles. The proposed hybrid conceptual-quantitative framework integrates governance evaluation, model assurance, fairness testing, explainability analysis, and continuous monitoring. The methodology introduces novel mechanisms for assessing proprietary models without requiring model access and presents a composite risk scoring model for quantifying AI-specific vendor exposures. Empirical validation at a Fortune 500 financial services organization demonstrates that the framework enables materially improved risk visibility, more defensible regulatory compliance, and more accurate long-term vendor management strategies. This work provides the first end-to-end, AI-focused vendor risk assessment methodology capable of operating under real-world constraints of opacity and limited disclosure, addressing a significant and urgent gap in third-party risk management practices.