AICM AtlasCSA AI Controls Matrix
DSP · Data Security and Privacy Lifecycle Management
DSP-11Cloud & AI Related

Personal Data Access, Reversal, Rectification and Deletion

Specification

Define and implement, processes, procedures and technical measures to enable data subjects to request access to, modification, or deletion of their personal data, according to any applicable laws and regulations.

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, Data curation

Development

Design, Guardrails

Evaluation

Evaluation, Validation/Red Teaming

Deployment

Orchestration, AI Services supply chain

Delivery

Operations, Maintenance, Continuous monitoring

Retirement

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 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.

Orchestrated

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.

Application

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.

Implementation guidelines

[All Actors]
1. Provide technical enforcement for data-at-rest subject to data-subject requests within systems the actor controls.

2. Govern model / training-data handling for data-subject rights for any models or datasets the actor manages.

3. Orchestrate middleware / flow pipelines for rights requests for any integration layers the actor operates.

4. Deliver or expose interfaces that enable end-user access and visibility for rights management for personal data the actor controls.

5. Govern compliance and contracts for data-subject rights.

Auditing guidelines

1. Examine whether the CSP’s policy and procedures related to data privacy address the requirement that authorized users must be able to access, modify, or delete personal data, and whether it is handled according to the applicable laws and regulations.

2. Establish whether the CSP has processes to manage and respond to data access requests from data subjects and whether it has documented the roles and responsibilities for this process.

3. Select a range of data changes to confirm that only authorized users can access, modify, and delete personal data successfully. Select a sample of data access requests to establish that these were completed correctly following the CSP’s processes. Confirm that all relevant evidence was formally documented.

4. Determine if third-party providers are evaluated according to the CSP’s policy and procedures related to data privacy, and whether those providers address the requirement that authorized users can access, modify, or delete personal data.

5. Verify that data subjects are informed about their rights and the procedures to exercise them.

6. Examine documentation of infrastructure capabilities that support identifying and isolating personal data for subject access requests. 

7. Verify the implementation of secure mechanisms for selective data deletion at the infrastructure level. 

8. Review audit logging systems that track data access, modification, and deletion actions at the infrastructure layer. 

9. Assess infrastructure data mapping that documents where personal data is stored to support comprehensive request fulfillment. 

10. Verify that infrastructure-level retention controls support deletion requirements.

Standards mappings

ISO 42001No Gap
42001: A.2.3 Alignment with other organizational policies
42001: A.8.2 System documentation and information for users
27001: A.5.34 Privacy and protection of personal identifiable information (PII)
27002: 5.34 Privacy and protection of personal identifiable information (PII)
Addendum

N/A

EU AI ActNo Gap
Article 10 (2)
Article 52 (2)
Addendum

N/A

NIST AI 600-1Partial Gap
GV-6.1-004
MS-2.2-003
Addendum

NIST AI 600-1 only speaks to training data regarding the DSP-11 topic, which aims "to ensure that personal data is processed according to any applicable laws and regulations and for the purposes declared to the data subject."

BSI AIC4Partial Gap
COM-01
COM-04
IDM-01
AM-02
Addendum

For such topics, there is the GDPR in the EU. The GDPR is translated to local regulations for every country in the EU. This is a explicit target of GDPR.

AI-CAIQ questions (1)

DSP-11.1

Are processes, procedures, and technical measures defined and implemented to enable data subjects to request access to, modify, or delete their personal data according to applicable laws and regulations?