Change Agreements
Specification
Include provisions limiting changes directly impacting customer owned environments/tenants to explicitly authorized requests within service level agreements.
Threat coverage
Architectural relevance
Lifecycle
Data storage, Resource provisioning
Training
Evaluation, Validation/Red Teaming, Re-evaluation
Orchestration, AI Services supply chain, AI applications
Operations, Maintenance, Continuous monitoring, Continuous improvement
Archiving, 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
Auditing guidelines
1. Inquiring with Control Owners 1.1 Conduct interviews with personnel responsible for managing cloud infrastructure changes and customer compute environment modifications to understand authorization processes for altering AI processing resources in customer tenants. Verify their understanding of controls that prevent unauthorized changes to compute configurations, storage access, or network settings that directly impact customer AI workloads and data processing capabilities. 2. Inspecting Records and Documents 2.1 Review Cloud Infrastructure Deployment Change Policies: Evaluate policies governing updates to compute resources, storage configurations, network settings, and AI accelerator allocations that affect customer tenant environments. 2.2 Inspect Customer Cloud Service Agreements: Look for restrictions on automatic infrastructure updates, changes to compute resource allocation, storage access modifications, or alterations to network configurations and AI processing capabilities. 2.3 Assess Infrastructure Rollback or Configuration Control Mechanisms: Customers should be able to maintain specific infrastructure configurations or reject resource-impacting updates. Review infrastructure versioning, tenant isolation controls, or customer-managed resource settings. 2.4 Verify Infrastructure Change Authorization Processes: Examine documented procedures requiring explicit customer authorization before implementing changes to compute resources, storage access, or network configurations that directly impact customer AI workload performance. 2.5 Review Customer Infrastructure Change Documentation: Validate that customers receive proper notification and authorization requests before infrastructure changes that affect their AI processing capabilities or data access patterns. 2.6 Examine SLA Compliance for Infrastructure Service Modifications: Confirm that cloud infrastructure changes maintain agreed resource allocation parameters and customer-authorized performance specifications.
Standards mappings
42001: Clause 8.1 Operational planning and control 42001: A.10.2 Allocating responsibilities 42001: A.10.4 Customers
Addendum
However, ISO 42001 does not specifically mention CCC-05 topic of "explicitly authorized requests within service level agreements" of changes. No mapping to ISO 27001 for CCC-05.
No Mapping
Addendum
The EU AI Act does not cover the CCC-05 topic, "Include provisions limiting changes directly impacting customer-owned environments/tenants to explicitly authorized requests within service level agreements," for any of the AI structures defined within the EU AI Act.
GV-6.1-004
Addendum
NIST AI 600-1 is insufficient to explicitly require that SLAs include provisions restricting changes directly impacting AI Customer-owned environments or tenants to those that have been explicitly authorized.
DEV-03 DEV-09
Addendum
N/A
AI-CAIQ questions (1)
Are provisions included that limit changes directly impacting customer owned environments/tenants to explicitly authorized requests within service level agreements?