Supply Chain Risk Management
Specification
Periodically review risk factors associated with supply chain relationships.
Threat coverage
Architectural relevance
Lifecycle
Data collection, Data curation, Data storage, Resource provisioning, Team and expertise
Supply Chain
Re-evaluation, Validation/Red Teaming, Evaluation
Orchestration, AI Services supply chain, AI applications
Operations, Maintenance, Continuous monitoring, Continuous improvement
Data deletion
Ownership / SSRM
PI
Owned by the Cloud Service Provider (CSP)
The Cloud Service Provider (CSP) is responsible for the design, development, implementation, and enforcement of the control to mitigate security, privacy, or compliance risks associated with cloud computing (processing, storage, and networking) technologies in the context of the services or products they develop and offer. The CSP is responsible and accountable for implementing the control within its own infrastructure/environment. The CSP is responsible for enabling the customer and/or upstream partner to implement/configure the control within their risk management approach. The CSP is accountable for ensuring that its providers upstream implement the control related to the service/product developed and offered by the CSP.
Model
Owned by the Model Provider (MP)
The model provider (MP) designs, develops, and implements the control as part of their services or products to mitigate security, privacy, or compliance risks associated with the Large Language Model (LLM). Model Providers are entities that develop, train, and distribute foundational and fine-tuned AI models for various applications. They create the underlying AI capabilities that other actors build upon. Model Providers are responsible for model architecture, training methodologies, performance characteristics, and documentation of capabilities and limitations. They operate at the foundation layer of the AI stack and may provide direct API access to their models. Examples: OpenAI (GPT, DALL-E, Whisper), Anthropic(Claude), Google(Gemini), Meta(Llama), as well as any customized model.
Orchestrated
Shared Model Provider-Orchestrated Service Provider (Shared MP-OSP)
The MP and OSP are jointly responsible and accountable for the design, development, implementation, and enforcement of the control to mitigate security, privacy, or compliance risks associated with Large Language Model (LLM)/GenAI technologies in the context of the services or products they develop and offer.
Application
Owned by the Application Provider (AP)
The Application Provider (AP) is responsible for the design, development, implementation, and enforcement of the control to mitigate security, privacy, or compliance risks associated with Large Language Model (LLM)/GenAI technologies in the context of the services or products they develop and offer. The AP is responsible and accountable for the implementation of the control within its own infrastructure/environment. If the control has downstream implications on the users/customers, the AP is responsible for enabling the customer and/or upstream partner in the implementation/configuration of the control within their risk management approach. The AP is accountable for carrying out the due diligence on its upstream providers (e.g MPs, Orchestrated Services) to verify that they implement the control as it relates to the service/product develop and offered by the AP. These providers build and offer end-user applications that leverage generative AI models for specific tasks such as content creation, chatbots, code generation, and enterprise automation. These applications are often delivered as software-as-a-service (SaaS) solutions. These providers focus on user interfaces, application logic, domain-specific functionality, and overall user experience rather than underlying model development. Example: OpenAI (GPTs,Assistants), Zapier, CustomGPT, Microsoft Copilot (integrated into Office products), Jasper (AI-driven content generation), Notion AI (AI-enhanced productivity tools), Adobe Firefly (AI-generated media), and AI-powered customer service solutions like Amazon Rufus, as well as any organization that develops its AI-based application internally.
Implementation guidelines
Auditing guidelines
1. Verify the CSP conducts regular reviews of supply chain risks at least annually or after significant changes addressing operational, security, compliance, and reputational risks, such as onboarding a new infrastructure vendor or changes in data residency laws. 2. Confirm risk assessments are documented, kept up to date, and informed by indicators like SLA violations, security incidents, or audit findings (e.g., repeated downtime from a storage provider or failure to meet encryption standards while involving relevant internal teams). 3. Ensure that identified risks lead to mitigation actions such as revising third-party agreements, enhancing monitoring controls, or replacing non-compliant vendors and that these actions are tracked and supported by audit-ready documentation, especially when risks impact service availability or regulatory compliance.
Standards mappings
42001: A.2.3 Alignment with other organizational policies 42001: A.10.3 Suppliers 27001: A.5.19 Information security in supplier relationships 27001: A.5.21 Managing information security in the information and communication technology (ICT) supply chain 27001: A.5.23 Information security for use of cloud services 27002: 5.19 Information security in supplier relationships 27002: 5.21 Managing information security in the information and communication technology (ICT) supply chain 27002: 5.23 Information security for use of cloud services
Addendum
N/A
Article 9
Addendum
The EU AI Act does not explicitly state the requirement to "periodically review risk factors associated with the supply chain."
GV-6.1-005 RM-1.2-002
Addendum
N/A
C4 SR-03 C4 SR-04 C5 SSO-02 C5 SSO-04
Addendum
N/A
AI-CAIQ questions (1)
Are risk factors associated with the supply chain relationships periodically reviewed?