AICM AtlasCSA AI Controls Matrix
DSP · Data Security and Privacy Lifecycle Management
DSP-06Cloud & AI Related

Data Ownership and Stewardship

Specification

Document ownership and stewardship of all relevant documented personal and sensitive data. Perform review at least annually.

Threat coverage

Model manipulation
Data poisoning
Sensitive data disclosure
Model theft
Model/Service Failure
Insecure supply chain
Insecure apps/plugins
Denial of Service
Loss of governance

Architectural relevance

Physical infrastructure
Network
Compute
Storage
Application
Data

Lifecycle

Preparation

Team and expertise

Development

Guardrails

Evaluation

Evaluation

Deployment

Orchestration, AI Services supply chain

Delivery

Operations, Maintenance, Continuous monitoring

Retirement

Data deletion, 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

[All Actors]
1. Define and document data-ownership and stewardship policies for every dataset the actor stores or processes.

2. Maintain an up-to-date inventory (or lineage repository) of personal and sensitive data that records origin, transformations, current location, and designated owner / steward.

3. Implement traceability mechanisms—logs, metadata tagging, or equivalent—to follow data from ingestion to disposal within systems the actor controls.

4. Review and update ownership / stewardship documentation at least annually, or after significant changes, and assess compliance with those policies.

[Shared among: MP, OSP, CSP]
1. Maintain records of training and inference datasets containing personal or sensitive data, including associated ownership and usage-rights metadata.

Auditing guidelines

1. Examine the CSP’s personal and sensitive data ownership and stewardship process. Determine if the documentation defines roles and responsibilities. Establish that this process and key controls comply with the organization’s data privacy and security policy. Establish whether the CSP has documented the roles and responsibilities for this process.

2. Establish that the CSP maintains a source(s) of record of data owners and stewards and the records for which they are responsible. This must include personal and sensitive data.

3. In the absence of a documented procedure, interview the control owner(s) responsible for key staff involved in the process and/or other relevant stakeholders impacted by the process/control requirement(s) and determine if the requirement(s) is/are understood. Evidence may be provided by observing individuals, systems, and/or processes associated with data management to determine if the process requirements are generally understood and implemented consistently.

4. Examine if the documentation is reviewed on an annual basis.

5. Verify that a data responsibility matrix detailing data types, associated obligations, and responsible persons or roles has been created.

6. Verify that the CSP maintains a source of record for data owners and the records for which they are responsible.

7. Determine whether third-party data ownership and stewardship are considered in the organization's process.

8. Examine documentation distinguishing between data custody (CSP responsibility) and data ownership (customer responsibility) for storing personal and sensitive data infrastructure.

9. Verify that the CSP has defined and documented the roles and responsibilities of personnel with administrative access to infrastructure components hosting customer data.

10. Review documentation provided to customers regarding infrastructure management practices, confirming it articulates the boundaries of CSP responsibility versus customer responsibility.

11. Assess whether the CSP maintains appropriate attestations or certifications regarding infrastructure controls that support customer data stewardship obligations.

12. Verify that documentation related to infrastructure data custody is reviewed at least annually for accuracy and completeness.

Standards mappings

ISO 42001Partial Gap
42001: 7.5.2 Creating and updating documented information
42001: A.4.3 Data resources
42001: A.2.3 Alignment with other organizational policies
27001 A.5.1
27001: A.5.9 - Inventory of information and other associated assets
27002: 5.9 - Inventory of information and other associated assets
Addendum

ISO 42001 should require documenting and updating ownership and stewardship of all documented personal and sensitive data at defined intervals.

EU AI ActPartial Gap
Article 10 (2)
Article 11
Addendum

While ownership documentation is covered, periodic review cycles aren't specified.

NIST AI 600-1No Gap
GV-1.6-003
Addendum

N/A

BSI AIC4No Gap
DM-03
DM-04
PC-02
BC-05
Addendum

N/A

AI-CAIQ questions (2)

DSP-06.1

Are ownership and stewardship of all relevant personal and sensitive data documented?

DSP-06.2

Are reviews performed at least annually for the documented ownership and stewardship of all relevant personal and sensitive data?