Supply Chain Data Security Assessment
Specification
Define and implement a process for conducting security assessments periodically for all organizations within the supply chain.
Threat coverage
Architectural relevance
Lifecycle
Data collection, Data curation, Data storage, Resource provisioning
Guardrails
Validation/Red Teaming, Re-evaluation
Orchestration, AI Services supply chain, AI applications
Operations, Maintenance, Continuous monitoring, Continuous improvement
Archiving, Data deletion, Model disposal
Ownership / SSRM
PI
Shared across the supply chain
Shared control ownership refers to responsibilities and activities related to LLM security that are distributed across multiple stakeholders within the AI supply chain, including the Cloud Service Provider (CSP), Model Provider (MP), Orchestrated Service Provider (OSP), Application Provider (AP), and Customer (AIC). These controls require coordinated actions, communication, and governance across all involved parties to ensure their effectiveness.
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 has a formal process to assess supply chain data security, covering cloud infrastructure (e.g., IAM, virtualization) and AI components (e.g., GPU clusters, object storage, orchestration tools). 2. Investigate how the CSP addresses risks from third parties like hardware vendors (e.g., chipsets), software suppliers (e.g., container runtimes), and platforms supporting AI workloads (e.g., MLaaS). 3. Review procedures for managing risks from entities handling customer data (e.g., backup services) or supporting infrastructure (e.g., CDN, DNS). 4. Confirm regular security assessments are performed per policy, covering data confidentiality (e.g., encryption), infrastructure integrity (e.g., patching), and compliance (e.g., ISO 27001).
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 15 Article 17 Annex VII 5.3
Addendum
Require providers of AI systems (not necessarily only high-risk organizations) to define and implement a process for conducting periodic, risk-based security assessments of all third-party organizations in their supply chain. This process should be integrated into the quality and risk management systems, documented for oversight, and include clear scope and frequency guidelines.
GV-6.1-005 GV-6.1-006
Addendum
N/A
C4 SR-03 C4 SR-04 C5 SSO-04
Addendum
N/A
AI-CAIQ questions (1)
Is a process for conducting periodic security assessments for all organizations within the supply chain defined and implemented?