Surveillance System
Specification
Implement, maintain, and operate datacenter surveillance systems at the external perimeter and at all the ingress and egress points to detect unauthorized ingress and egress attempts.
Threat coverage
Architectural relevance
Lifecycle
Not applicable
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 relating to datacenter surveillance. 2. Determine if the policy includes ingress, egress and external perimeter to detect unauthorized access. 3. Determine if procedures include activities and actions against unauthorized personnel in the premises. 4. Review and determine if items identified in surveillance system logs for the premises have been actioned in accordance with policy and procedures. 5. Determine if logs are maintained and reviewed appropriately.
Standards mappings
42001: 8.1 Operational Planning and Control 42001: A.2.3 Alignment with other organizational policies 27001: A.7.4 - Physical security monitoring 27002: 7.4 - Physical security monitoring
Addendum
N/A
No Mapping
Addendum
Require providers and deployers of high-risk AI systems to implement, maintain, and operate surveillance systems covering the external perimeter and all ingress and egress points of facilities housing such systems. These requirements should include regular maintenance and testing, integration into risk management, and documentation of implemented measures as part of compliance evidence.
No Mapping
Addendum
Implement, maintain, and operate data center surveillance systems at the external perimeter and at all the ingress and egress points to detect unauthorized ingress and egress attempts.
PS-03
Addendum
N/A
AI-CAIQ questions (1)
Are datacenter surveillance systems at the external perimeter and at all the ingress and egress points, implemented, maintained, and operated to detect unauthorized ingress and egress attempts?