AICM AtlasCSA AI Controls Matrix
BCR · Business Continuity Management and Operational Resilience
BCR-11Cloud & AI Related

Equipment Redundancy

Specification

Supplement business-critical equipment with both locally redundant and geographically dispersed equipment located at a reasonable minimum distance in accordance with applicable industry standards.

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

Development

Not applicable

Evaluation

Not applicable

Deployment

AI applications, Orchestration

Delivery

Operations, Maintenance, Continuous monitoring

Retirement

Archiving

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. Strategic Assessment:
- Inventory all physical and virtual components supporting critical functions.
- Classify infrastructure elements based on maximum tolerable downtime.
- Identify specialized hardware with unique replacement challenges.
- Map dependencies between components to understand failure implications.
- Document resource requirements for minimum acceptable operations.

2. Design Principles for Resilient Systems:
- Create true redundancy rather than mere backup capabilities.
- Implement geographic distribution for critical system components.
- Build capacity ensuring secondary systems handle full production loads.
- Develop isolation boundaries preventing cascade failures across components.

3. Core Processing Safeguards:
- Deploy parallel computing environments using N+1 redundancy principles.
- Establish automated health checking with failover triggers.
- Create load balancing across redundant processing instances.
- Design resource isolation preventing "noisy neighbor" problems.

4. Network Resilience Measures:
- Implement diverse routing paths between essential components.
- Establish multiple external connectivity providers with dynamic balancing.
- Create redundant internal communication channels with automatic switching.
- Design independent management networks for emergency administration.

5. Environmental Protection:
- Deploy redundant power systems with seamless transition capabilities.
- Implement alternative cooling approaches with independent controls.
- Create multiple physical security layers with overlapping coverage.
- Establish diverse monitoring systems with independent alerting mechanisms.

6. Validation and Maintenance:
- Conduct regular failover testing under realistic load conditions.
- Perform scheduled operation from secondary systems to verify readiness.
- Implement automated verification ensuring configuration synchronization.
- Create capacity validation confirming redundant systems remain adequate.

7. Cross-Entity Considerations:
- Establish clear documentation of redundancy capabilities and limitations.
- Create shared understanding of expected transition times during failures.
- Develop notification protocols when activating redundant systems.
- Design complementary approaches where systems interconnect.

Auditing guidelines

1. Examine the process to identify business-critical equipment and any redundant equipment.

2. Examine the process to identify the applicable industry standards.

3. Evaluate if the redundant business-critical equipment is independently located at a reasonable distance.

4. Verify that data centers housing redundant equipment are located at a minimum distance from each other according to relevant industry standards (e.g., Uptime Institute, ISO 22301, NIST), confirming that this distance is sufficient to isolate them from common threats.

5. Review the implementation of redundant power systems, including uninterruptible power supplies, backup generators, and redundant power distribution units, and confirm that they support critical AI processing equipment.

6. Assess the redundancy of the networking infrastructure, verifying redundant routers, switches, load balancers, and internet connections from different providers to avoid single points of failure.

7. Verify implementation of redundant compute resources for AI workloads, including server clusters, virtualization hosts, and container platforms, confirming automated failover capabilities.

8. Examine redundant system implementation, including RAID configurations, distributed storage systems, and data replication mechanisms across geographically separated locations.

9. Review monitoring systems that detect failures in redundant components and automated alerting mechanisms that notify appropriate personnel.

10. Verify documentation of regular testing procedures for redundant systems and examine records of recent failover tests confirming redundancy functions as designed.

Standards mappings

ISO 42001Partial Gap
No ISO 42001 mapping.
27001: A.8.14 (Redundancy of information processing facilities)
Addendum

There is no control in ISO 42001 that covers AICM BCR-11 topic of business-critical equipment to be reasonably at a minimum distance for redundancy. However, it is fully covered by ISO 27001: A.8.14 Redundancy of information processing facilities, where processing facility shall implement that which makes them redundant to include minimum distance of critical systems.

EU AI ActPartial Gap
Article 15 (4) (accuracy, robustness, and cybersecurity)
Addendum

Independently located at a reasonable minimum distance in accordance with applicable industry standards.

NIST AI 600-1Full Gap
No Mapping
Addendum

The entire requirement BCR-11: Establish requirements for the redundancy of business-critical equipment by ensuring that redundant assets are independently located in accordance with industry standards or cross reference related NIST publications (such as SP 800-53) that cover physical resiliency.

BSI AIC4No Gap
PS-02
PS-06
OPS-09
Addendum

N/A

AI-CAIQ questions (1)

BCR-11.1

Are business-critical equipment supplemented with both locally redundant and geographically dispersed equipment located at a reasonable minimum distance in accordance with applicable industry standards?