UEM-13Cloud & AI Related

Remote Wipe

Specification

Define, implement and evaluate processes, procedures and technical measures to enable the deletion of company data remotely on managed endpoint devices.

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 storage

Development

Not applicable

Evaluation

Not applicable

Deployment

AI applications

Delivery

Operations, Maintenance, Continuous monitoring

Retirement

Data deletion

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

Owned by the Orchestrated Service Provider (OSP)

The Orchestrated Service Provider (OSP) 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 OSP 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 OSP is responsible for enabling the customer and/or upstream partner in the implementation/configuration of the control within their risk management approach. The OSP is accountable for ensuring that its providers upstream (e.g MPs) implement the control as it relates to the service/product the develop and offered by the OSP. This refers to entities that create the technical building blocks and management tools that enable AI implementation. This can include platforms, frameworks, and tools that facilitate the integration, deployment, and management of AI models within enterprise workflows. These providers focus on model orchestration and offer services like API access, automated scaling, prompt management, workflow automation, monitoring, and governance rather than end-user functionality or raw infrastructure. They help businesses implement AI in a structured and efficient manner. Examples: AWS, Azure, GCP, OpenAI, Anthropic, LangChain (for AI workflow orchestration), Anyscale (Ray for distributed AI workloads), Databricks (MLflow), IBM Watson Orchestrate, and developer platforms like Google AI Studio.

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. Ensure all managed endpoints across the supply chain (MP, OSP, AP, AIC) are configured for remote wipe via the UEM solution, and verify this capability on each device that stores sensitive or proprietary AI data, so any lost or compromised endpoint can be sanitized immediately.

2. Define a unified set of criteria and an approval workflow for triggering a remote wipe event - for instance, agreeing that a device reported stolen or showing signs of active compromise warrants an immediate wipe - so that every stakeholder follows the same decision thresholds and no time is lost deliberating in a crisis.

3. Establish cross-notification protocols for remote wipe actions: if one organization initiates a remote wipe on an endpoint (e.g., a model provider wiping a laptop that had AI customer data), they must promptly inform all other relevant parties, enabling those parties to revoke credentials or monitor for related suspicious activity stemming from that device.

4. Regularly rehearse and validate remote wipe processes on non-production devices for each stakeholder. Share the results (such as wipe success rate and time to complete) in joint security meetings to build confidence that all parties can effectively destroy data on a compromised device and to refine procedures as needed.

Auditing guidelines

1. Verify that the CSP has a documented process for remote wipe of corporate and BYOD endpoints, covering inventory, geolocation tracking, alerting for untraceable devices, and remote wipe capability.

2. Confirm that remote wipe features are enforced at the device level and cannot be disabled by users.

3. Inspect whether wipe operations are limited to authorized personnel and include secure data removal techniques to prevent incomplete wipes or data leakage.

4. Review implementation evidence such as endpoint inventories, tracking logs, remote wipe execution reports, and periodic test documentation.

5. Verify that the CSP routinely tests remote wipe procedures across all supported endpoint types and maintains testing evidence for audit and compliance purposes.

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 managed endpoints, as identified.
3. Examine policy and procedures for evidence of review, with respect to effectiveness.

Standards mappings

ISO 42001Partial Gap

No Mapping for ISO 42001
ISO 27001 A.8.1

Addendum

No ISO 42001 controls support UEM-13 topic of remote wipe, especially not configured on endpoint devices

EU AI ActFull Gap

No Mapping

Addendum

Include a technical annex specifying that remote data wipe capabilities must be implemented for endpoints handling high-risk AI data.

NIST AI 600-1Full Gap

No Mapping

Addendum

NIST AI 600-1 would need to expand beyond its current scope by incorporating controls or subcategories that explicitly address endpoint data protection and device management, which are currently outside the profile’s coverage.

BSI AIC4No Gap

C4 SR-06
C5 AM-05

Addendum

N/A

AI-CAIQ questions (1)

UEM-13.1

Are processes, procedures, and technical measures defined, implemented, and evaluated to enable remote company data deletion on managed endpoint devices?