STA · Supply Chain Management, Transparency, and Accountability

STA-14Cloud & AI Related

Supply Chain Governance Review

Specification

Periodically review the organization's supply chain partners' IT governance policies and procedures.

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 curation, Data storage, Resource provisioning

Development

Design, Training, Guardrails

Evaluation

Evaluation, Validation/Red Teaming, Re-evaluation

Deployment

Orchestration, AI Services supply chain, AI applications

Delivery

Operations, Maintenance, Continuous monitoring, Continuous improvement

Retirement

Archiving, Data deletion, Model disposal

Ownership / SSRM

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

Shared Application Provider-AI Customer (Shared AP-AIC)

The AP and AIC both share responsibility and accountability 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 offer and consume.

Implementation guidelines

[All Actors]
1. Establish periodic review schedules (annually or based on risk assessment) to evaluate supply chain partners' IT governance policies, procedures, and compliance frameworks.

2. Assess partner governance maturity including their change management processes, incident response procedures, data governance controls, and security oversight mechanisms.

3. Require standardized evidence handovers from supply chain partners that document their governance practices, including:
 - Current governance policy documentation
 - Compliance attestations and audit reports
 - Risk management and incident response procedures
 - Change control and approval workflows.

4. Verify alignment between partner IT governance practices and your organization's security requirements, regulatory obligations, and risk tolerance levels.

5. Document governance review outcomes including identified gaps, partner improvement commitments, and any residual risks requiring ongoing monitoring or mitigation.

6. Maintain a governance assurance chain by:
 - Providing downstream partners with summaries of your governance reviews
 - Creating transparency into upstream partner governance status
 - Enabling end-to-end governance visibility for final customers (AICs).

7. Integrate governance review findings into vendor risk assessments, contract negotiations, and ongoing supplier relationship management processes.

Auditing guidelines

1. Examine whether the cloud service provider has defined and implement a process for reviewing the governance practices of its supply chain partners, including third-party infrastructure providers, software vendors, and managed service providers.

2. Determine whether contractual agreements with supply chain partners include provisions granting the CSP the right to audit or review their governance and security controls, particularly where these partners impact data security, service availability, or regulatory compliance.

3. Evaluate whether the CSP actively conducts these reviews on a defined schedule, and maintains documented evidence that the review process is being followed in accordance with the established policy.

Standards mappings

ISO 42001No Gap

42001: A.2.3 Alignment with other organizational policies
42001: A.10.3 Suppliers
27001: A.5.22 Monitoring
review and change management of supplier services
27002: 5.22 Monitoring
review and change management of supplier services

Addendum

N/A

EU AI ActPartial Gap

Article 17
Article 25 (1)
Article 28 (1)
Annex VII 5.3

Addendum

Vendor risk assessments and audits, contractual clauses requiring governance transparency, third-party security attestation reviews (e.g., ISO 27001, SOC 2), and a schedule for periodic supply chain risk reviews.

NIST AI 600-1Full Gap

No Mapping

Addendum

No NIST AI 600-1 control focuses on periodic review of organizational supply chain partners' IT policies and procedures.

BSI AIC4No Gap

C4 PC-01
C5 SSO-04

Addendum

N/A

AI-CAIQ questions (1)

STA-14.1

Are the IT governance policies and procedures for organization's supply chain partners periodically reviewed?