AI Supply Chain Audit: Third-Party Component Assessment

Regulatory Framework

The EU AI Act establishes specific requirements for vendor verification that apply to high-risk AI systems. These requirements draw on established EU product safety and conformity assessment practices but adapt them for AI's unique characteristics — continuous learning, data dependency, probabilistic outputs, and emergent behaviour.

This guide covers supplier assessment criteria, contractual compliance verification, and ongoing monitoring in practical terms. The focus is on actionable implementation rather than regulatory interpretation, providing templates, checklists, and workflows that compliance teams can deploy immediately.

Methodology and Process

The assessment methodology should follow a structured cycle: scope definition, criteria mapping, evidence collection, gap analysis, findings classification, remediation planning, and verification. Each phase builds on the previous one, creating a traceable chain from regulatory requirement to compliance evidence.

Evidence collection spans three domains: documentary evidence (policies, procedures, specifications, test reports), operational evidence (system logs, monitoring outputs, incident records), and testimonial evidence (interviews with developers, operators, and governance personnel). All three domains are necessary for a complete compliance picture.

Practical Implementation

For vendor verification, begin with scoping: identify which AI systems require assessment, determine the applicable assessment pathway, and establish timelines aligned with the August 2026 deadline. Create a detailed work plan with assigned responsibilities and milestone dates.

Develop assessment criteria mapped directly to the AI Act's articles and, where available, harmonised standards or common specifications. Each criterion should specify: the requirement source, the evidence needed to demonstrate compliance, the assessment method, and clear pass/fail indicators. This mapping ensures comprehensive coverage and consistent assessment across different AI systems.

Documentation and Continuous Improvement

Assessment documentation must be maintained for the AI system's lifetime plus 10 years (Article 18). Design documentation practices for sustainability: use standardised templates, automate evidence collection where possible, and maintain version control to track compliance evolution over time.

Build feedback loops from assessment findings into AI development and governance processes. Each assessment cycle should not only verify compliance but identify opportunities to strengthen controls, improve documentation quality, and enhance organisational AI governance maturity.

This article is for informational purposes only and does not constitute legal advice. Regulatory requirements change frequently — verify current rules with official sources. Built by Sawai Gyoseishoshi Office, Hiroshima, Japan.