Data Flow Documentation
Specification
Create data flow documentation to identify what data is processed, stored or transmitted where. Review data flow documentation at defined intervals, at least annually, and after any change.
Threat coverage
Architectural relevance
Lifecycle
Data collection, Resource provisioning
Design
Evaluation
Orchestration, AI Services supply chain
Operations, Maintenance, Continuous monitoring
Archiving, Data deletion
Ownership / SSRM
PI
Owned by the Customer (AIC)
The Customer (AIC) is 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 services or products they consume.
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 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.
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 CSP’s procedures and technical requirements for data flow documentation, and ensure that a review is carried out at least annually and after any change. Establish that this process and key controls comply with the CSP’s data privacy and security policy. Establish whether the CSP has documented the roles and responsibilities for this process. 2. Select a sample of documents to check that they have been completed to the correct specifications and reviewed. 3. Review whether data flow documentation includes an assessment of the accuracy, completeness, timeliness, and sustainability of the data (flow). 4. Identify if data flow documentation includes how data is processed, stored, and transmitted. 5. Verify that data flow documentation is reviewed at defined intervals, at least annually, and after any significant changes to the data processing environment. 6. Verify compliance with relevant data protection laws and organizational policies throughout the data flow documentation process. 7. Determine if the data flow documentation includes data processed, stored, or transmitted to or from third parties. 8. Verify that documentation identifies the data types that flow through different infrastructure components, including specific storage systems, network segments, and compute resources. 9. Review the documentation update process, confirming there are defined procedures for revising documentation when infrastructure changes occur, such as new components, architecture modifications, or decommissioning. 10. Assess evidence of regular documentation reviews, verifying that reviews occur at least annually and are comprehensive enough to validate accuracy and completeness. 11. Examine records of documentation updates following infrastructure changes, confirming that updates are timely and adequately reflect the modified environment. 12. Verify that infrastructure data flow documentation is accessible to relevant personnel and integrated with higher-level data flow mapping maintained by service consumers.
Standards mappings
42001: 7.5.2 Creating and updating documented information 42001: A.4.2 Resource Documentation 42001: A.4.3 Data Resources 42001: A.7.2 Data for development and enhancement of AI system 42001: A.7.3 Acquisition of data 42001: A.7.5 Data provenance 27001: 7.5.2 Creating and Updating
Addendum
Reviewing data flow diagrams at defined intervals at least annually and after any change should be introduced in ISO 42001.
Article 11 (1) Article 10 (2) (e)
Addendum
N/A
GV-6.1-003
Addendum
NIST AI 600-1 only speaks to the third-party aspect of the DSP-05 topic, not the organization's data.
DM-03 DM-04 PC-02 BC-05
Addendum
N/A
AI-CAIQ questions (2)
Are data flow documentation created to identify what data is processed, stored, or transmitted where?
Are data flow documentation reviewed at defined intervals, at least annually, and after any change?