UEM-14Cloud & AI Related

Third-Party Endpoint Security Posture

Specification

Define, implement and evaluate processes, procedures and technical and/or contractual measures to maintain proper security of third-party endpoints with access to organizational assets.

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

Data collection, Data curation, Data storage, Resource provisioning

Development

Training

Evaluation

Evaluation, Validation/Red Teaming, Re-evaluation

Deployment

Orchestration, AI Services supply chain, AI applications

Delivery

Operations, Maintenance, Continuous monitoring, Continuous improvement

Retirement

Archiving, Data deletion, Model disposal

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 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

[Applicable to all actors except CSP]
1. Require any third-party or contractor devices that access AI systems to meet the same endpoint security baseline as the primary stakeholders’ devices. All core participants should collectively mandate that external user endpoints have up-to-date patches, active anti-malware, host firewall enabled, and full disk encryption before they are granted access to project resources.

2. Enforce third-party endpoint compliance through technical controls: implement pre-access checks (e.g., via a zero-trust network access gateway or NAC) to validate that a vendor or partner device is managed and compliant with security policies. Whenever possible, have external users either use company-managed devices or enroll their equipment in a segregated UEM instance to continuously monitor their posture.

3. Include stringent endpoint security requirements in contracts or service agreements with third parties. For example, stipulate that consultants must adhere to your UEM policies, allow security audits of their devices if necessary, and report any device breach. All stakeholders should review these provisions together to ensure consistency and collectively follow up on any non-compliance.

4. Maintain shared visibility into third-party access: jointly keep an inventory of approved third-party personnel and their devices, and review this list in cross-organization security meetings. This ensures every stakeholder knows which external endpoints have access to their environments, enabling coordinated oversight and quick revocation of access for any device that falls below the agreed security standards.

Auditing guidelines

1. Verify that the CSP maintains documented agreements with third parties covering endpoint access controls, including provisions for identity management, endpoint isolation, security tool installation, secure communications, and defined contractual security responsibilities.

2. Confirm that contracts include detailed requirements for endpoint security, such as device types allowed, data confidentiality, compliance with legal requirements, patching, service levels, and reporting duties.
3. Inspect whether agreements mandate third-party security assessments, assign vendor-side security contacts, and define penalties for non-compliance.

4. Review implementation evidence such as endpoint access logs, vendor risk assessments, contract terms, monitoring reports, and meeting records between CSP and vendors.

5. Verify that third-party access and security are continuously monitored through automated tools, with prompt action on suspicious activities or policy violations.

From CCM:
  1. Examine procedures for adequacy, currency, communication, and effectiveness.
  2. Determine the organization's definition of third-party endpoints.
  3. Determine the extent and applicability of the processes, procedures, and technical measures over third-party endpoints.
  4. Examine policy and procedures for evidence of review, with respect to effectiveness.

Standards mappings

ISO 42001No Gap

ISO 42001 B.10.2
ISO 27001 A.5.21

Addendum

N/A

EU AI ActPartial Gap

Article 25

Addendum

Amend Article 25, or issue a technical annex, to mandate clear, enforceable security requirements for third-party endpoints.

NIST AI 600-1No Gap

GV-6.1-004
MG-3.1-001

Addendum

N/A

BSI AIC4No Gap

C4 SR-06
C5 AM-05

Addendum

N/A

AI-CAIQ questions (1)

UEM-14.1

Are processes, procedures, and technical and/or contractual measures defined, implemented, and evaluated to maintain proper security of third-party endpoints with access to organizational assets?