Demystifying Supply Chain Risk Assessment
Digital twins are the natural technological vehicle for implementing DPPs at scale. A productlevel digital twin can aggregate material provenance data, manufacturing process parameters, carbon accounting, and end-of-life disposition information into a single queryable record. That is precisely the structure ESPR requires. The reliability of that DPP twin, however, depends entirely on the trustworthiness of its data sources. That is where SoT’ s supply domain risk categories do their work. DPP information requirements encompass detailed material composition, manufacturing location and process information, product durability and recyclability data, carbon footprint metrics, and multi-tier supply chain traceability data. SoT’ s risk categories“ Supply Hygiene Risks”( RC-8) and“ Supply Availability Risks”( RC-675) provide question sets directly relevant to these requirements, while“ Supplier Organizational Security Risks”( RC-76) addresses the verification and documentation capabilities necessary for DPP data integrity.
CYBER-RESILIENCE IN INTEGRATED SUPPLY CHAINS
The integration of digital twins, IoT sensors, and AI decision systems across multi-tier supply chains dramatically expands the cyber attack surface. A compromise of a digital twin’ s data feeds can corrupt procurement decisions, falsify DPP records, or manipulate predictive models in ways that are difficult to detect. SoT’ s RC-286( Service Security Risks) provides systematic coverage for these threats.
RF-567 addresses malicious code injection into AI services embedded in digital twin decision loops. RF-571 covers encryption, access logging, and cross-border data transfer controls for digital twin data streams. RF-573 assesses the data integrity verification mechanisms that protect the fidelity of digital twin inputs. BoK v1.5’ s RC-696( AI Infrastructure Security Risks) extends this coverage to AI infrastructure components specifically, including guardrail integrity and MCP server security. These are attack vectors that have emerged as AI agents become embedded in supply chain operations, and they are not yet addressed by most existing assessment frameworks.
4 MAPPING SYSTEM OF TRUST TO REGULATORY, AI GOVERNANCE, AND DIGITAL TWIN REQUIREMENTS
Having described both the SoT framework and the regulatory landscape, we now demonstrate how the two align in practice. We do this through narrative examples for the two major regulatory instruments and then through a consolidated mapping table that organizations can use as a starting point for their own compliance planning.
EU AI ACT ARTICLE 25 VALUE CHAIN RESPONSIBILITIES
Article 25 requires that providers of high-risk AI systems and third parties supplying components agree in writing on necessary information sharing, capabilities, and technical access [ 9 ]. These requirements are substantively unchanged by the Digital Omnibus proposal,
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