Model Documentation
Specification
Define, implement, enforce, approve, document, communicate, maintain and evaluate processes and procedures for model documentation. Regularly review and update the model documentation.
Threat coverage
Architectural relevance
Lifecycle
Team and expertise
Design, Supply Chain
Evaluation, Validation/Red Teaming, Re-evaluation
AI Services supply chain, AI applications
Operations, Continuous improvement
Model disposal
Ownership / SSRM
PI
Shared Cloud Service Provider-Model Provider (Shared CSP-MP)
The CSP and MP 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.
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. Assess the CSP's controls for storing and managing model documentation provided by customers or third parties. 2. Verify that documentation is accessible only to authorized personnel. 3. Review procedures for maintaining the integrity and confidentiality of model documentation. 4. Evaluate data retention policies related to model documentation. 5. Confirm that documentation is properly backed up and protected from loss or damage.
Standards mappings
42001 A.6.2.7 - AI system technical documentation 42001 B.6.2.7 - AI system technical documentation
Addendum
N/A
Article 11 (1) Article 11 (2) Article 13
Addendum
N/A
GV-1.2-001 MG-2.2-002 MP-1.1-002 MP-2.2-001 MS-2.9-002 MG-3.2-003
Addendum
NIST AI 600-1 speaks to documenting specific elements that would be part of a model card but does not address the formal aspects of a model card, nor its regular review, based on MDS-03 topics.
C4 PC-02 C5 SP-01 C5 SP-02
Addendum
N/A
AI-CAIQ questions (2)
Are processes and procedures defined, implemented, enforced, and evaluated for documenting, approving, communicating, evaluating, and maintaining model documentation?
Is the model documentation regularly reviewed and updated?