DSP · Data Security and Privacy Lifecycle Management

DSP-19Cloud & AI Related

Data Location

Specification

Define and implement, processes, procedures and technical measures to specify and document the physical locations of data, including any locations in which data is processed or backed up.

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

Data collection, Data storage, Resource provisioning

Development

Design, Guardrails

Evaluation

Evaluation, Validation/Red Teaming

Deployment

Orchestration, AI Services supply chain

Delivery

Operations, Maintenance, Continuous monitoring

Retirement

Data deletion, Archiving

Ownership / SSRM

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)

Orchestrated

Owned by the Customer (AIC)

Application

Owned by the Customer (AIC)

Implementation guidelines

[Applicable to all providers]
1. Maintain list of all data assets, their owners, sensitivity, origin, and processing methods.

2. Create data flow diagrams (DFDs) to identify data sources, destinations, and storage points, and document the flow to trace data from origin to final destination.

3. Identify physical and logical data repositories,  AI models,  APIs, and data backups in DFDs and data maps, and use these maps and flow diagrams to identify potential security risks and exposure areas.

4. Determine where data will be stored (on-premises, cloud regions, or countries), who is processing it and ensure it is confined to specific geographic areas as required. Establish measures to limit data storage and processing to specific geographic areas.

5. Implement access controls to restrict data access based on user roles, permissions, and security policies.

6. Secure data at rest, in transit, and in use with encryption techniques.

7. Implement measures to mask and anonymize data to prevent data loss.

8. Establish backup and recovery processes are implemented to restore data in case of loss or system failures. 

9. Define data retention policies for how long data should be stored and when to securely dispose of it.

10. Establish a process for customers to specify where their data will be stored and notify them if data is replicated outside that area.

Auditing guidelines

1. Verify that infrastructure policies and documentation cover the physical storage locations of AI workloads and associated data, and enforce ethical use standards for AI data processing and storage.

2. Verify documented roles and responsibilities related to managing AI system infrastructure, including physical storage governance.

3. Verify that policies cover jurisdictional restrictions and guidelines for data storage and processing on the infrastructure layer.

4. Verify that the organization maintains source(s) of record for all physical storage locations supporting AI workloads, with clear data lineage.

5. Verify accuracy and completeness of physical storage records as maintained and reported by infrastructure systems.

6. Verify that obligations of both the infrastructure provider and its suppliers regarding AI system storage and processing are documented.

7. Verify that AI infrastructure components used in data storage and processing meet organizational policy and ethical standards.

8. Verify procedures for continuous monitoring and auditing of AI storage systems to ensure compliance with ethical standards and regulations.

9. Verify that infrastructure risk management strategies include measures to mitigate bias and ensure transparency in AI system storage and processing.

10. Verify documented incident handling procedures for AI infrastructure-related data storage events, including reporting and remediation.

Standards mappings

ISO 42001No Gap

42001: A.4.2 Resource Documentation
42001: A.4.5 System and Computing Resources
42001: A.7.5 Data provenance
42001: A.2.3 Alignment with other organizational policies
27001: A.5.9 - Inventory of information and other associated assets
27001: A.8.12 - Data leakage prevention
27001: A.8.13 - Information backup
27002: 5.9 - Inventory of information and other associated assets
27002: 8.12 - Data leakage prevention
27002: 8.13 - Information backup

Addendum

N/A

EU AI ActPartial Gap

Article 11 (1)
Article 10 (2)

Addendum

Data processing documentation is covered but physical location specifics and backup location documentation is not detailed.

NIST AI 600-1Full Gap

No Mapping

Addendum

NIST AI 600-1 does not cover the DSP-19 topics of documenting the "physical locations of data," which include locations where data is backed up.

BSI AIC4No Gap

PSS-12

Addendum

N/A

AI-CAIQ questions (1)

DSP-19.1

Are processes, procedures, and technical measures defined and implemented to specify and document the physical locations of data, including any locations where data is processed or backed up?