Demystifying Supply Chain Risk Assessment
compliance questions. Both work within the same framework, which reduces miscommunication and accelerates response to emerging risks.
PRACTICAL EXAMPLE: APPLYING SOT TO AN AI SUPPLY CHAIN SUPPLIER
To illustrate how SoT assessments translate into measurable governance outputs, consider the following illustrative hypothetical implementation scenario reflecting common industry deployment patterns.
A multinational manufacturer evaluated a third-party supplier providing an AI-enabled procurement analytics platform intended to support sourcing decisions across multiple jurisdictions. Because the platform influenced supplier selection decisions and relied on externally sourced data feeds, the organization classified the supplier as elevated risk and applied a targeted SoT assessment consisting of 82 risk measures drawn from RC-76( Supplier Organizational Security Risks), RC-286( Service Security Risks), RC-695( AI Model Integrity Risks), and RC-698( AI Governance and Compliance Risks).
The assessment was structured to produce measurable outputs suitable for procurement governance, regulatory due diligence, and audit review. Rather than generating a single composite score without context, the organization evaluated results across the relevant SoT domains to identify specific control deficiencies and evidence requirements. Table 5-1: Representative SoT Assessment Output for an AI-Enabled Supplier.
summarizes the assessment results.
Assessment Area Measures Reviewed Pass Control Gaps High-Risk Findings
Supplier Organizational Security Risks 24 18 4 2
Service Security Risks 21 15 3 3
AI Model Integrity Risks 19 11 5 3
AI Governance and Compliance Risks 18 10 5 3
Total 82 54 17 11
Table 5-1: Representative SoT Assessment Output for an AI-Enabled Supplier.
The assessment identified several material control deficiencies requiring remediation prior to unrestricted deployment approval. The most significant findings included the absence of documented provenance records for externally sourced training data, incomplete privileged access reviews for model administrators, and insufficient evidence of bias and robustness testing for AI-generated sourcing recommendations. The supplier’ s incident response procedures also lacked provisions addressing model drift and poisoned training data scenarios.
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