AICM AtlasCSA AI Controls Matrix
I&S · Infrastructure Security
I&S-08Cloud & AI Related

Network Architecture Documentation

Specification

Identify and document high-risk environments.

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

Resource provisioning, Data storage

Development

Not applicable

Evaluation

Not applicable

Deployment

Orchestration, AI applications, AI Services supply chain

Delivery

Operations, Continuous monitoring, Maintenance

Retirement

Not applicable

Ownership / SSRM

PI

Owned by the Cloud Service Provider (CSP)

The Cloud Service Provider (CSP) is responsible for the design, development, implementation, and enforcement of the control to mitigate security, privacy, or compliance risks associated with cloud computing (processing, storage, and networking) technologies in the context of the services or products they develop and offer. The CSP is responsible and accountable for implementing the control within its own infrastructure/environment. The CSP is responsible for enabling the customer and/or upstream partner to implement/configure the control within their risk management approach. The CSP is accountable for ensuring that its providers upstream implement the control related to the service/product developed and offered by the CSP.

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

[All Actors]
1. Define High-Risk Environments.

2. Establish criteria to classify environments as high-risk.

3. Include data sensitivity, criticality, and regulatory impact in classification.

4. Network Documentation Framework.

5. Maintain up-to-date network topology diagrams.

6. Document segmentation strategies and security zones.

7. Review and Update Documentation.

8. Perform periodic audits to validate network architecture documentation.

9. Ensure updates align with infrastructure changes and emerging threats.

Auditing guidelines

1. Verify the Cloud Service Provider has comprehensive documentation identifying and detailing high-risk environments.

2. Confirm regular updates and reviews of network architecture documentation (e.g., cloud security baselines, CSA Star certification, MITRE Cloud Matrix).

3. Check availability and accessibility of architecture documentation to authorized personnel.

4. Ensure documentation aligns with current network configurations and practices.

5. Validate documented processes for identifying and managing changes to network architecture.

6. Confirm training provided to responsible personnel for maintaining accurate documentation.

Standards mappings

ISO 42001No Gap
ISO/IEC 42001:2023 - B.6.2.3
ISO/IEC 42001:2023  A.5.12 / 5.13
ISO/IEC 42001:2023 A.8.22
ISO/IEC 42001:2023  9.1
ISO/IEC 42001:2023 Clause 6.1.2;
27001 A.5.4
27001 A.5.9
A.8.9; Inventory and classification of environments
27001 A.5.35
27002 5.9
27002 8.28
27002 5.17 Documentation and review of controls for sensitive or critical areas
Addendum

N/A

EU AI ActNo Gap
Article 11
Addendum

Very similar controls. No change needed.

NIST AI 600-1Partial Gap
Appendix A.1.2
Addendum

The AICM control dictates that high-risk environments are documented, which should be addressed by risk assessments required by NIST AI 600-1 in Appendix A1.2. The NIST AI 600-1 control should dictate that high-risk environments be uncovered through risk-assessments.

BSI AIC4No Gap
C4 SR-02
C5 AM-06
C5 COS-07
C5 BCM-02
Addendum

N/A

AI-CAIQ questions (1)

I&S-08.1

Are high-risk environments identified and documented?