AI Supply Chain Security addresses the risks that arise from the complex web of dependencies in modern AI systems. Just as software supply chain security protects against compromised libraries and packages, AI supply chain security protects against vulnerabilities in AI models, training data, APIs, and service providers.
The AI supply chain includes: foundation model providers (OpenAI, Anthropic, Google, Meta), fine-tuning and hosting platforms, training and evaluation datasets, AI application vendors (SaaS tools built on AI), open-source models and libraries, hardware providers (GPU infrastructure), and data annotation and labeling services.
Key risks in the AI supply chain include: model poisoning (compromised training data leading to manipulated outputs), supply chain attacks on open-source AI frameworks, vendor lock-in and single points of failure, data sovereignty issues when AI processing crosses jurisdictions, model theft or unauthorized redistribution, API security vulnerabilities, and cascading failures when upstream providers experience outages.
Organizations should implement AI supply chain security through vendor risk assessments, bill of materials for AI systems (AI BOM), continuous monitoring of third-party AI services, fallback strategies for critical AI dependencies, and contractual protections in vendor agreements.
