Production and Non-Production Environments
Specification
Separate production and non-production environments.
Threat coverage
Architectural relevance
Lifecycle
Data storage, Resource provisioning
Training, Design, Guardrails
Validation/Red Teaming, Re-evaluation
Orchestration, AI Services supply chain, AI applications
Operations, Maintenance, Continuous monitoring, Continuous improvement
Data deletion
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 Orchestrated Service Provider-Application Provider (Shared OSP-AP)
The OSP and AP 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. Examine Cloud Service Provider (CSP) policies and procedures that keep production and non-production environments separate to prevent unauthorized access and unintentional interactions. 2. Verify clear roles for cloud Shared Responsibility Model (e.g., between the cloud provider, cloud consumers and cloud partners). 3. Verify role-based access control (RBAC) and least-privilege principle for all environments, restricting production access to only authorized personnel. 4. Verify procedures to ensure production data is not used in non-production environments unless properly sanitized or anonymized to protect sensitive information. 5. Verify the change promotion process from development and testing to production, ensuring proper approvals, and documentation to maintain system integrity. 6. Verify that monitoring and logging are implemented across all environments to detect unauthorized access or changes, and that logs are regularly reviewed and maintained securely. 7. Verify that segregation controls are regularly reviewed and updated to address evolving threats and business needs, ensuring continuous protection of the production environment.
Standards mappings
ISO/IEC 42001:2023 - No mapping ISO/IEC 27001:2022 - A.8.31 ISO/IEC 27001:2022 - 8.27 ISO/IEC 27002 - 8.28
Addendum
No environment segregation No Technical implementation guidance No Control objectives for deployment pipeline security No Environment-specific roles or access policies No Lifecycle-phase enforcement
No Mapping
Addendum
Full control would have to be added because the EU AI Act does not address these concerns. Add, "Separate production and non-production environments."
No Mapping
Addendum
No separate environments for production and non-production.
C4 PF-05 C4 DQ-06 C4 SR-06 C5 DEV-10
Addendum
N/A
AI-CAIQ questions (1)
Are production and non-production environments kept separate?