Endpoint Management
Specification
Define, implement and evaluate processes, procedures and technical measures to enforce policies and controls for all endpoints permitted to access systems and/or store, transmit, or process organizational data.
Threat coverage
Architectural relevance
Lifecycle
Data collection, Data curation, Data storage, Resource provisioning
Design, Training, Guardrails
Evaluation, Validation/Red Teaming, Re-evaluation
Orchestration, AI Services supply chain, AI applications
Operations, Maintenance, Continuous monitoring, Continuous improvement
Archiving, Data deletion, Model disposal
Ownership / SSRM
PI
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.
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 Orchestrated Service Provider-Application Provider (Shared OSP-AP)
The OSP and AP 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. Verify that the CSP has implemented technical measures to enforce endpoint management controls, including inventory, configuration, and access policies for devices accessing organizational systems. 2. Confirm that risk assessments are conducted to define acceptable endpoint types for system access or data storage, with compensating controls where needed. 3. Verify centralized configuration enforcement using standardized configuration management tools for managed endpoints. 4. Inspect whether the CSP enforces prevention of security control circumvention (e.g., jailbreaking, rooting) using technical detective and preventive controls integrated with centralized management systems. 5. Review hardening measures for unmanaged endpoints, including secure default configurations, encryption, disabling unnecessary services, and network segmentation to mitigate risks. From CCM: 1. Examine procedures for adequacy, currency, communication, and effectiveness. 2. Determine the extent and applicability of the processes, procedures, and technical measures over applicable endpoints, as identified. 3. Examine policy and procedures for evidence of review, with respect to effectiveness.
Standards mappings
No Mapping for ISO 42001 ISO 27001 - A.8.1 ISO 27001 - A.8.26
Addendum
No ISO 42001 controls support UEM-05 topic of processes to enforce controls for endpoints
No Mapping
Addendum
Define, implement, and evaluate processes, procedures, and technical measures to enforce policies and controls for all endpoints.
MG-2.2-001 MS-2.7-001 MG-2.4-004
Addendum
N/A
C4 SR-06 C5 AM-05
Addendum
N/A
AI-CAIQ questions (1)
Are processes, procedures, and technical measures defined, implemented and evaluated, to enforce policies and controls for all endpoints permitted to access systems and/or store, transmit, or process organizational data?