AI systems are not built; they are assembled. Every model, dataset, and line of code depends on an intricate supply chain of vendors, cloud providers, open-source libraries, and pre-trained components. As regulation tightens, this chain has become a new frontier of risk. The integrity of an organization’s AI depends not only on its own practices but on every upstream contributor whose work feeds into its systems.

Recent investigations by ENISA and CISA (2025) describe this as the “AI dependency cascade.” A vulnerability or bias embedded in one widely used model can propagate across entire industries. The challenge is compounded by opacity: most organizations cannot trace the provenance of their AI components beyond a few known vendors. ISO/IEC 42001 and the EU AI Act’s Annex IV both now call for documentation of model lineage, training data, and supplier assurance. Yet few enterprises have systems capable of producing it.

The parallels with traditional supply-chain security are clear. In manufacturing, provenance systems ensure that raw materials meet quality and ethical standards. In AI, provenance means mapping and validating every element that shapes a model’s behavior. This includes data sources, labeling processes, pre-trained weights, fine-tuning parameters, and even the frameworks used to deploy the model. Without that visibility, organizations face systemic exposure: an upstream compromise, poisoned dataset, or unvetted library could undermine compliance and trust at scale.

The OECD (2023) warns that as AI markets consolidate, the risk of single points of failure will grow. A small number of foundation-model providers already underpin most commercial AI applications. Dependence on these entities creates both economic and regulatory fragility. If one provider fails to meet future audit requirements or experiences a security breach, the ripple effects could be global. For regulators, this concentration amplifies the need for cross-border cooperation on AI assurance. For enterprises, it underscores the importance of independent validation and documented vendor governance.

Operationalizing AI supply-chain integrity requires three layers of action. First, organizations must create model registries that document every external component and its provenance. Second, they should integrate third-party risk assessments specific to AI—covering data origin, model validation, and license obligations—into existing supplier management frameworks. Third, these registries and assessments must be connected to evidence libraries and audit dashboards so that provenance is verifiable, not claimed.

The consequences of neglecting supply-chain integrity are no longer theoretical. In 2024, multiple organizations reported performance degradation after upstream models changed without notice. Others discovered that their vendors’ datasets included copyrighted or sensitive material, creating new compliance liabilities. Each incident reinforces that trust in AI depends on knowing not just what a system does but what it is made of.

Governments are beginning to respond. ENISA’s 2025 Threat Landscape Report calls for a global “AI Software Bill of Materials” (AI-SBOM), mirroring cybersecurity practice. The concept is straightforward: transparency about every component in a system. Implementing it across AI would enable traceability, foster accountability, and reduce systemic risk. The organizations that lead in adopting AI-SBOM practices will define the next standard for governance readiness.

References

CISA. (2025). Advisory on AI supply-chain risks for critical infrastructure. Cybersecurity and Infrastructure Security Agency. https://www.cisa.gov
ENISA. (2025). Threat landscape report: AI in cyber-offense and defense. European Union Agency for Cybersecurity. https://www.enisa.europa.eu/publications
Organisation for Economic Co-operation and Development. (2023). Framework for the classification of AI systems. OECD Publishing. https://oecd.ai
ISO/IEC. (2023). ISO/IEC 42001:2023 Artificial intelligence management systems. International Organization for Standardization. https://www.iso.org