Assets Classification
Specification
Classify and document the physical, and logical assets (e.g., applications) based on the organizational business risk. Review and update the assets’ classification at least annually or upon significant changes.
Threat coverage
Architectural relevance
Lifecycle
Data curation, Resource provisioning
Not applicable
Not applicable
Not applicable
Operations
Not applicable
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. Examine the policy relating to defining the organization's business risk. 2. Confirm that the physical and logical assets are being classified in accordance with defined policy and procedures. 3. Review the asset Inventory to determine if assets are cataloged and tagged according to the organization's business risk classification criteria.
Standards mappings
42001: 6.1.2 AI Risk Assessment 42001: A.4.2 Resource documentation 42001: A.4.3 Data resources 42001: A.4.4 Tooling resources 42001: A.4.5 System and computing resources 42001: A.2.3 Alignment with other organizational policies 27001: 6.1.2 Information security risk assessment 27001: A.5.9 Inventory of information and other associated assets 27001: A.5.12 - Classification of information 27001: A.5.37 - Documented operating procedures 27001: A.5.29 - Information security during disruption 27002: 5.9 Inventory of information and other associated assets 27002: 5.12 - Classification of information 27002: 5.37 - Documented operating procedures 27002: 5.29 - Information Security during disruption
Addendum
N/A
No Mapping
Addendum
Classify and document all relevant physical and logical assets supporting such systems, based on their associated business risk. These classifications should be reviewed and updated at least annually, or whenever significant changes occur.
GV-1.6-001 GV-1.6-002 GV-1.6-003
Addendum
Requiring the asset inventory to be categorized based on business risk requires more interpretation than what is provided in the AICM control specification, such as: defining the classification criteria, assessing the business risk to each asset, assigning the classifications, and ensuring consistency, etc.
AM-02
Addendum
N/A
AI-CAIQ questions (2)
Are the physical and logical assets (e.g. applications) classified and documented based on the organizational business risk?
Is the assets' classification reviewed and updated at least annually or upon significant changes?