AI Governance for Third-Party Models: The Compliance Blind Spot in Vendor AI

Abstract

As organizations rapidly deploy AI to govern many core business processes, third-party and vendor AI models are increasingly used in healthcare, financial services, manufacturing, and public sectors. While organizations invest large budgets in governing their internally developed AI, vendor AI often lacks any governance, creating a compliance blind spot which can result in many risks and violations. This governance gap is particularly critical from a cybersecurity perspective, as vendor AI systems introduce unique attack surfaces including adversarial manipulation vulnerabilities, data exfiltration risks through model APIs, and supply chain security weaknesses that traditional security controls cannot adequately address. Regulatory regimes fully assign liability to organizations deploying AI, regardless of whether the organization developed the AI in-house, or purchased it. Because organizations lack technical access to the underlying components and external behavior of vendors' AI, they cannot scrutinize vendor AI for security vulnerabilities, model poisoning attempts, or backdoor exploits. This creates an intrinsic paradox: Organizations fully own responsibility for systems that they cannot fully control, audit for security flaws, nor understand. We propose a Third-Party AI Governance Framework based on systematic vendor AI classification based on decision criticality and regulatory ramifications, shared accountability architectures based on vendor-client responsibility, audit-ready transparency standards that require full documentation of all model attributes including security testing results, and continual oversight mechanisms beyond procurement-time evaluation. Organizations that cannot establish sufficient control over vendor AI security posture are likely to experience regulatory enforcement actions, security breaches, lawsuits, and loss of stakeholder trust. By contrast, showing and proving control of vendor AI through third-party AI governance may allow an organization to survive and even give it a competitive edge.