AICM AtlasCSA AI Controls Matrix
DCS · Datacenter Security
DCS-14Cloud-Specific

Secure Utilities

Specification

Secure, monitor, maintain, and test utilities services for continual effectiveness at planned intervals.

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 test utility services that power data centres that perform sensitive tasks such as training, data processing, inference to ensure continual effectiveness on a periodic basis.

2. Providers should establish comprehensive environmental control systems that address the specific environmental conditional such as temperature and humidity requirements of AI computing hardware. 

3. Implement multi-tiered monitoring systems with real-time dashboards and automated alerts for deviations that might impact system performance. 

4. Develop documented emergency protocols for environmental failures, including graceful degradation and workload shifting capabilities. 

5. Create redundant cooling and power systems with N+1 redundancy for critical AI workloads. 

6. Conduct regular effectiveness testing and maintenance of all environmental controls, with quarterly performance analysis to optimize for both reliability and energy efficiency.

Auditing guidelines

1. Confirm the existence of the policy and procedures relating to utilities services.

2. Confirm that the control effectiveness of utilities services is conducted at periodic intervals.

3. Determine if utility services logs are maintained and reviewed periodically.

4. Determine if testing of the utilities services is included in the CSP contract with the customer.

Standards mappings

ISO 42001No Gap
42001: 8.1 Operational Planning and Control
42001: 8.2 AI Risk Assessment
42001: 8.3 AI Risk Treatment
42001: 8.4 AI System Impact Assessment
42001: 9.1 Monitoring
Measurement
Analysis
and Evaluation
42001: A.4.5 System and computing resources
42001: A.2.3 Alignment with other organizational policies
27001: A.7.11 Supporting utilities
27002: 7.11 Supporting utilities
Addendum

N/A

EU AI ActFull Gap
No Mapping
Addendum

Require documented procedures to secure, monitor, maintain, and test utility services supporting AI systems; periodic reviews and tests to ensure utility services remain effective and resilient; and integration of utility continuity risks into the risk management framework.

NIST AI 600-1Full Gap
No Mapping
Addendum

Secure, monitor, maintain, and test utilities services for continual effectiveness at planned intervals.

BSI AIC4No Gap
PS-06
BCM-04
Addendum

N/A

AI-CAIQ questions (1)

DCS-14.1

Are utility services secured, monitored, maintained, and tested at planned intervals for continual effectiveness?