Adverse Event Response Training
Specification
Train datacenter personnel to safely manage adverse events, including but not limited to unauthorized ingress and egress attempts.
Threat coverage
Architectural relevance
Lifecycle
Team and expertise
Not applicable
Not applicable
Not applicable
Operations
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
Auditing guidelines
1. Examine the policy and procedures relating to activities and actions to perform in case of unauthorized access. 2. Examine the policy and procedures related to datacenter’s personnel training. 3. Determine if the training content is appropriate and approved by the organization. 4. Ascertain that appropriate datacenter personnel have completed all relevant training through review of training plans and records. Confirm that these have been completed in accordance with policy and procedures.
Standards mappings
42001: A.4.6 Human Resource 42001: A.2.3 Alignment with other organizational policies 27001: 7.5 Protecting against physical and environmental threats 27001: A.5.24 - Information security incident management planning and preparation 27001: A.6.3 - Information security awareness education and training 27001: A.6.8 - Information security event reporting 27002: 5.24 (d e) - Information security incident management planning and preparation 27002: A.6.3 Information security awareness education and training 27002: 6.8 (e) - Information security event reporting 27002: 7.5 Protecting against physical and environmental threats
Addendum
N/A
No Mapping
Addendum
The Act would need to require that organizations operating high-risk AI systems ensure datacenter staff are trained to recognize and respond to adverse physical security events. Documented training programs. Regular review and refresh of training. Training should tie into the organization’s risk management system and incident response procedures already required under Article 9 and Article 15, expanding their scope to include personnel and physical security events. Require that training records be maintained and included in the technical documentation.
No Mapping
Addendum
Train data center personnel to respond to unauthorized ingress or egress attempts.
HR-03
Addendum
N/A
AI-CAIQ questions (1)
Are data center personnel trained to safely manage adverse events, including but not limited to unauthorized ingress and egress attempts?