UEM-01Cloud & AI Related

Endpoint Devices Policy and Procedures

Specification

Establish, document, approve, communicate, apply, evaluate and maintain policies and procedures for all endpoints. Review and update the policies and procedures at least annually or upon significant system changes.

Threat coverage

Model manipulation

Data poisoning

Sensitive data disclosure

Model theft

Model/Service Failure

Insecure supply chain

Insecure apps/plugins

Denial of Service

Loss of governance

Architectural relevance

Physical infrastructure

Network

Compute

Storage

Application

Data

Lifecycle

Preparation

Resource provisioning

Development

Design, Training, Guardrails, Supply Chain

Evaluation

Not applicable

Deployment

Orchestration, AI applications

Delivery

Operations, Maintenance, Continuous monitoring

Retirement

Data deletion, Model disposal, Archiving

Ownership / SSRM

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

[Applicable to all actors except CSP]
        1.        Establish a UEM governance committee with representatives from MP, OSP, AP, and AIC to approve and review endpoint policies collaboratively.

        2.        Maintain a central repository for all endpoint policies and procedures, including onboarding/decommissioning steps, patching schedules, and technical policy baselines (e.g., device hardening configurations, DLP rules, secure boot/device integrity checks, access control policies, encryption standards, and remote wipe criteria).

        3.        Integrate endpoint policy enforcement into the UEM platform (e.g., with automated compliance checks, alerting on misconfigurations or non-compliance like jailbreaks/rooting, and enforcing policy-based access restrictions).

        4.        Implement monthly KRI/KPI reporting (e.g., percentage of compliant devices, encryption coverage, patch timeliness, anti-malware status), complemented by quarterly joint audits of endpoint compliance metrics and policy effectiveness to feed results back into policy refinement cycles.

Auditing guidelines

1. Verify documented, approved, communicated, and applied endpoint management policies covering internal corporate and production environments, BYOD, and third‑party devices. Ensure policies define scope, objectives, roles, responsibilities, approval workflows, and evidence of senior management oversight. Inspect for compliance with relevant standards and regulations (e.g., ISO/IEC 27001, CSA CCM).

2. Technical and Operational Provisions: Review policies for clear definitions of: endpoint inventory and ownership assignment; OS requirements and configuration management; approved services, applications, and compatibility matrices; encryption, anti‑malware, firewalls, DLP, remote wipe, and locate; privacy considerations for personal devices; and granular access controls and contractual SLA obligations for third‑party endpoints.

3. Application and Monitoring: Verify implementation evidence (inventories, logs, training, monitoring dashboards). Review certifications (e.g., ISO 27001, SOC 2) and reports for evidence of effectiveness. Confirm policy review cadence (at least annually or post‑change) and documented updates.

From CCM:
1. Examine policy for adequacy, currency, communication, and effectiveness.
2. Examine policy and procedures for evidence of review, at least annually.

Standards mappings

ISO 42001No Gap

42001-A.2.2
42001-A.2.4
27001-A.5.1

Addendum

N/A

EU AI ActFull Gap

No Mapping

Addendum

Establish, document, approve, communicate, apply, evaluate, and maintain policies and procedures for all endpoints. Review and update the policies and procedures at least annually.

NIST AI 600-1Partial Gap

GV-1.4-001
GV-4.1-001

Addendum

NIST AI 600-1 does not require policy establishment for UEM-01 topic of establishing policy for endpoints specifically, only for illegal content by AI systems and risk management, nor does it speak to annual review of policies or upon significant changes.

BSI AIC4No Gap

C4 PC-02

Addendum

N/A

AI-CAIQ questions (2)

UEM-01.1

Are policies and procedures established, documented, approved, communicated, applied, evaluated, and maintained for all endpoints?

UEM-01.2

Are the policies and procedures reviewed and updated at least annually or upon significant system changes?