Personal and Sensitive Data Awareness and Training
Specification
Provide employees with access to sensitive organizational and personal data with appropriate security awareness training and regular updates in organizational procedures, processes, and policies relating to their professional function relative to the organization.
Threat coverage
Architectural relevance
Lifecycle
Team and expertise
Supply Chain
Not applicable
AI Services supply chain
Not applicable
Not applicable
Ownership / SSRM
PI
Shared Cloud Service Provider-Model Provider (Shared CSP-MP)
The CSP and MP 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.
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 Cloud Service Provider-Model Provider (Shared CSP-MP)
The CSP and MP 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
Shared across the supply chain
Shared control ownership refers to responsibilities and activities related to LLM security that are distributed across multiple stakeholders within the AI supply chain, including the Cloud Service Provider (CSP), Model Provider (MP), Orchestrated Service Provider (OSP), Application Provider (AP), and Customer (AIC). These controls require coordinated actions, communication, and governance across all involved parties to ensure their effectiveness.
Implementation guidelines
Auditing guidelines
1. Confirm that personnel with access to sensitive cloud infrastructure or AI workloads (e.g., model hosting environments, customer data, orchestration logs) receive security training, for example, cloud engineers managing AI model deployment must complete secure access and infrastructure hardening training. 2. Check for documented training policies and access-role mappings. For example, requiring platform administrators and DevOps engineers to complete annual cloud security certifications before accessing production systems. 3. Verify that training is completed and regularly updated to reflect evolving cloud and AI risks. For example, it may include topics like multi-tenant isolation, prompt leakage in hosted models, and secure API gateway configurations. 4. Ensure training is tailored to specific roles (e.g., cloud engineers, site reliability engineers, AI platform operators). For example, SREs receiving incident response training, while AI platform teams focus on secure model lifecycle management. 5. Interview staff to confirm awareness of responsibilities and recent updates. For example, ask a cloud operator how they manage access to model logs and whether they are aware of the latest infrastructure patching policy. 6. Review how updates are communicated, such as through internal security bulletins, DevOps briefings, or monthly cloud governance newsletters that highlight changes in cloud security practices and AI hosting protocols.
Standards mappings
42001: A.2.3 Alignment with other organizational policies 42001: 5.3 Roles responsibilities and authorities 42001: 7.3 Awareness 42001: A.3.2 AI Roles and responsibilities 42001: A.4.6 Human Resource 27001: 7.3 Awareness 27001: A.5.1 Policies for information security 27001: A.5.10 Acceptable use of information and other associated assets 27001: A.6.3 Information security awareness education and training 27002: 5.1 Policies for Information Security 27002: 5.10 Acceptable use of information and other associated assets 27002: 6.3 Information security awareness education and training
Addendum
N/A
Article 4 Article 17
Addendum
The EU AI Act is missing organization-wide training or procedural updates. It does not cover access to sensitive data more broadly (e.g., personal data, trade secrets).
MP-4.1-003
Addendum
N/A
HR-03
Addendum
N/A
AI-CAIQ questions (1)
Are employees with access to sensitive organizational and personal data, provided with appropriate security awareness training and regular updates in organizational procedures, processes, and policies, relating to their professional function relative to the organization?