Secure Area Authorization
Specification
Allow only authorized personnel access to secure areas, with all ingress and egress points restricted, documented, and monitored by physical access control mechanisms. Retain access control records on a periodic basis as deemed appropriate by the organization.
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 and procedures relating to access to secure areas. 2. Determine if the policy includes ingress and egress points to service and delivery areas. 3. Determine if procedures include activities and actions against unauthorized personnel in the premises. 4. Confirm that existence, review, and retention of Access logs for secure areas are aligned with policy and procedures.
Standards mappings
42001: 42001: 8.1 Operational Planning and Control 42001: A.2.3 Alignment with other organizational policies 27001: A.7.1 Physical security perimeters 27001: A.7.2 - Physical entry 27001: A.7.3 Securing offices rooms and facilities 27001: A.7.4 Physical security monitoring 27002: 7.1 Physical Security perimeters 27002: 7.2 (a b) - Physical entry 27002: 7.3 Securing offices rooms and facilities 27002: 7.4 Physical security monitoring
Addendum
N/A
No Mapping
Addendum
The EU AI Act does not cover the DCS-09 control related to Secure Area Authorization. While the Act emphasizes AI system security and risk management, it lacks explicit requirements for formal authorization processes controlling access to secure physical areas housing AI infrastructure. Such authorization protocols are critical for preventing unauthorized physical access but are outside the scope of the EU AI Act.
No Mapping
Addendum
Allow only authorized personnel access to secure areas, with all ingress and egress points restricted, documented, and monitored by physical access control mechanisms. Retain access control records on a periodic basis as deemed appropriate by the organization.
PS-04
Addendum
N/A
AI-CAIQ questions (2)
Are solely authorized personnel able to access secure areas, with all ingress and egress areas restricted, documented, and monitored by physical access control mechanisms?
Are access control records retained periodically, as deemed appropriate by the organization?