Change Management Policy and Procedures
Specification
Establish, document, approve, communicate, apply, evaluate and maintain policies and procedures for managing the risks associated with applying changes to assets owned, controlled or used by the organization. Review and update the policies and procedures at least annually, or upon significant changes.
Threat coverage
Architectural relevance
Lifecycle
Data storage, Resource provisioning
Design, Training, Guardrails
Evaluation, Validation/Red Teaming, Re-evaluation
Orchestration, AI Services supply chain, AI applications
Operations, Maintenance, Continuous monitoring, Continuous improvement
Data deletion, Model disposal
Ownership / SSRM
PI
Shared across the supply chain
Shared control ownership refers to responsibilities and activities related to LLM security that are distributed across multiple stakeholders within the AI supply chain, including the Cloud Service Provider (CSP), Model Provider (MP), Orchestrated Service Provider (OSP), Application Provider (AP), and Customer (AIC). These controls require coordinated actions, communication, and governance across all involved parties to ensure their effectiveness.
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. Conduct interviews with personnel responsible for documenting, maintaining, and communicating organizational change management policies, procedures, and standards (the Policies). 2. Inspecting Records and Documents: Obtain and review the change management Policies to ensure they are adequate for the organization to manage risks associated with applying changes to organizational assets. Verify that the Policies define the personnel or roles responsible for their dissemination, identify an official accountable for managing the Policies, specify the frequency of reviews and updates (annually), and outline events that necessitate policy updates. 3. Verify that the Policies are disseminated, are reviewed and updated at least annually or upon significant changes, are approved, and are communicated to relevant stakeholders.
Standards mappings
42001: A.6.2.3 Documentation of AI system design and development 42001: Clause 6.3 Planning changes 42001: Clause 8.1 Operational planning and control 42001: B.6 AI system life cycle 42001: B.6.1.3 Processes for responsible design and development of AI systems 42001: B.6.2.7 AI system technical documentation 42001: A.6.2.6 AI system operation and monitoring 27001: A.5.1 Policies for information security 27001: A.8.32 Change management
Addendum
N/A
Article 17
Addendum
The EU AI Act does not cover CCC-01 topic for General-Purpose AI Models or General Purpose AI Models with Systemic Risks.
No Mapping
Addendum
The entire requirement to “establish, document, approve, communicate, apply, evaluate, and maintain policies and procedures for change management” is missing in NIST AI 600-1.
C4 PC-02 C5 DEV-01 DEV-02 DEV-03
Addendum
N/A
AI-CAIQ questions (2)
Are policies and procedures for managing the risks associated with applying changes to assets owned, controlled or used by the organization, established, documented, approved, communicated, applied, evaluated, and maintained?
Are the policies and procedures reviewed and updated at least annually or upon significant changes?