AICM AtlasCSA AI Controls Matrix
BCR · Business Continuity Management and Operational Resilience
BCR-04Cloud & AI Related

Business Continuity Planning

Specification

Establish, document, approve, communicate, apply, evaluate and maintain a business continuity plan based on the results of the operational resilience strategies and capabilities.

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

Data storage, Resource provisioning, Team and expertise

Development

Not applicable

Evaluation

Not applicable

Deployment

AI applications, Orchestration

Delivery

Operations, Maintenance, Continuous monitoring

Retirement

Archiving, Data deletion, Model disposal

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. Identify and Prioritize: Document essential functions and their acceptable interruption timeframes.

2. Establish Alternatives: Develop simplified operational modes for critical functions and alternative data sources when primary inputs are unavailable.

3. Define Clear Roles: Assign specific continuity responsibilities across organizational boundaries
Establish decision authority for activating emergency procedures.

4. Test Regularly: Conduct realistic exercises involving all necessary participants. Validate assumptions about resource availability during challenges.

5. Maintain Relevance: Review plans when significant system changes occur. Update procedures based on insights from exercises and real events.

Auditing guidelines

1. Confirm a documented and approved BCP is in place for cloud services supporting AI workloads, including core IaaS, PaaS, 
and ML services.

2. Verify the plan includes defined roles and communication procedures for service recovery impacting tenant AI operations.

3. Check that the BCP addresses region/zone-level failures for GPU/TPU clusters or specialized hardware used in AI training.

4. Ensure detailed fallback and rerouting strategies exist for managed AI services (e.g., Vertex AI, SageMaker, Azure ML).

5. Validate CSP’s plans for restoring tenant data, models, and configurations hosted on the platform.

6. Confirm BCP testing results are periodically shared with affected enterprise customers consuming AI services.

7. Ensure CSP evaluates AI-specific operational risks during annual BCP reviews and updates accordingly.

Standards mappings

ISO 42001No Gap
42001: A.4 Resources for AI systems
27001: A.5.30 ICT readiness for business continuity
Addendum

N/A

EU AI ActPartial Gap
Article 9 (risk management),
Article 15 (technical robustness),
Article 17 (quality system),
Article 16 (organizational responsibility),
Article 93 (post-market monitoring)
Addendum

Explicit requirements for business continuity planning, testing, or recovery capabilities

NIST AI 600-1Partial Gap
MG-2.3-001
Addendum

Clarify in the framework that incident response and recovery plans shall be integrated into a comprehensive business continuity plan that is updated at least annually or upon significant change.

BSI AIC4No Gap
BCM-03
Addendum

N/A

AI-CAIQ questions (1)

BCR-04.1

Is a business continuity plan - based on the results of operational resilience strategies and capabilities - established, documented, approved, communicated, applied, evaluated and maintained?