SSRM Documentation Review
Specification
Review and validate SSRM documentation.
Threat coverage
Architectural relevance
Lifecycle
Data storage, Resource provisioning
Supply Chain
Evaluation, 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. Confirm the Cloud Service Provider (CSP) has a process to regularly review its own SSRM documentation and that of key suppliers (e.g., hardware vendors, colocation providers), ensuring shared responsibilities are clearly defined and current by updating its matrix to reflect infrastructure-layer responsibilities such as physical security, virtualization, and network segmentation. 2. Verify these reviews are conducted at least annually or when major service changes occur (e.g., new data center deployments, platform upgrades), ensuring the SSRM reflects changes in control ownership, data handling, and operational responsibilities.
Standards mappings
42001: A.2.3 Alignment with other organizational policies 42001: A.10.2 Allocating Responsibilities 27001: 9.1 Monitoring measurement analysis and evaluation 27001: 9.3 Management review 27001: A.5.20 Addressing information security within supplier agreements 27001: A.5.23 Information security for use of cloud services 27002: A.5.20 Addressing information security within supplier agreements 27002: 5.23 Information security for use of cloud services
Addendum
N/A
Article 15 Article 17 Article 28 Annex IV (2) (f) Annex VII (5.3)
Addendum
Mandate a dedicated SSRM documentation structure, a formal review or validation cycle, and specific review responsibilities across different actors (e.g., provider vs. deployer).
No Mapping
Addendum
NIST AI 600-1 is missing SSRM documentation review and validation.
C4 PC-01 C5 SSO-04 C5 OIS-03 C5 OIS-04
Addendum
N/A
AI-CAIQ questions (1)
Are the SSRM documentation reviewed and validated?