AICM AtlasCSA AI Controls Matrix
DCS · Datacenter Security
DCS-15Cloud-Specific

Equipment Location

Specification

Keep business-critical equipment away from locations subject to high probability for environmental risk events.

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

Not applicable

Development

Not applicable

Evaluation

Not applicable

Deployment

Not applicable

Delivery

Operations

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 except AIC]
1. Providers should isolate critical equipment that contains data centres that perform business sensitive tasks, such as training, data processing away from environment related risks such as flooding, tornadoes, etc.

2. Providers should implement a strategic approach to select equipment location that minimizes environmental failure risks to critical systems. 

3. Conduct comprehensive environmental and geological risk assessments before selecting datacenter locations, ensuring facilities avoid flood zones, seismic risk areas, and regions prone to extreme weather events. Implement geographic diversity with distributed infrastructure across multiple regions, preventing any single environmental event from disrupting essential AI services. 

4. Establish continuous environmental monitoring with predictive capabilities to anticipate potential hazards. 

5. Develop formal business continuity plans with specific protocols for equipment protection during environmental emergencies. Regularly review and update risk profiles considering evolving climate patterns.

Auditing guidelines

1. Examine the policy relating to environmental risk.

2. Determine if locations are assessed and classified for probability of environmental risk.

3. Determine if business-critical equipment is identified.

Standards mappings

ISO 42001No Gap
42001: A.4.2 Resource Documentation
42001: A.4.5 System and computing resources
42001: A.5.4 Assessing AI Systems Impact on Individuals or Groups of individuals
42001: A.5.5 Assessing societal impacts of AI systems
42001: A.2.3 Alignment with other organizational policies
27001: A.7.5 - Protecting against physical and environmental threats
27001: A.7.8 - Equipment siting and protection
27002: 7.5 - Protecting against physical and environmental threats
27002: 7.8 - Equipment siting and protection
Addendum

N/A

EU AI ActFull Gap
No Mapping
Addendum

Require organizations to assess environmental risks to facilities housing business-critical equipment and to ensure such equipment is placed in low-risk locations. This should be integrated into the risk management obligations (Article 9), documented in the technical documentation (Annex IV), and reviewed periodically to reflect changes in environmental risk profiles.

NIST AI 600-1Full Gap
No Mapping
Addendum

Keep business-critical equipment away from locations subject to high probability for environmental risk events.

BSI AIC4No Gap
PS-01
Addendum

N/A

AI-CAIQ questions (1)

DCS-15.1

Is business-critical equipment segregated from locations subject to a high probability of environmental risk events?