UEM-11Cloud & AI Related

Data Loss Prevention

Specification

Configure managed endpoints with Data Loss Prevention (DLP) technologies and rules in accordance with a risk assessment.

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

Guardrails

Evaluation

Not applicable

Deployment

AI applications

Delivery

Operations, Maintenance, Continuous monitoring, Continuous improvement

Retirement

Data deletion

Ownership / SSRM

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 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.        Deploy endpoint DLP agents on all devices handling sensitive AI data across MP, OSP, AP, and AIC, enforcing consistent protection for model code, training data, PII, etc.
        2.        Align DLP policy and data classification among stakeholders, ensuring consistent enforcement (e.g. blocking key patterns, file types).
        3.        Monitor DLP agent status via UEM and share clear metrics (agent coverage %, incidents, response times) among parties to maintain visibility and encourage program improvement.
        4.        Conduct privacy impact assessments (PIAs / DPIAs) before enabling DLP for sensitive or regulated data to verify legal compliance, especially under GDPR or equivalent data protection regimes.
        5.        Tailor DLP controls based on user roles and data sensitivity, adjusting monitoring and blocking thresholds accordingly to uphold the principle of least privilege.
        6.        Educate users and administrators on DLP objectives, mechanisms, and reporting pathways to support compliance and reduce accidental exposures.
        7.        Maintain a joint incident response process for DLP alerts, prompt notification, investigation, and containment across stakeholders to quickly address potential data loss.

Auditing guidelines

1. Verify that the CSP has a documented DLP Policy for both endpoint and cloud workloads, approved by governance, defining sensitivity tiers, roles, and review intervals.

2. Inspect the policy to confirm it mandates centralized data classification, structured inventories, and use of CSP‑native or integrated DLP services on all managed endpoints.

3. Confirm the policy requires automated scanning of data in motion and at rest including API transfers, container volumes, file uploads, against classification‑based rules and application whitelisting.

4. Verify that the policy specifies real‑time responses (blocking, encryption, alerting), integration with security event management, and defined SOPs for handling DLP violations.

5. Review system outputs (classification inventories, DLP configuration snapshots, violation logs, remediation tickets, compliance dashboards, and audit trails) to ensure compliance across endpoints and workloads.

From CCM:
1. Examine the organization's data loss policy.
2. Examine the policies on configuration of such controls.
3. Determine if such controls are driven by risk assessments.
4. Determine if such controls are in place and evaluated as effective.

Standards mappings

ISO 42001Partial Gap

No Mapping for ISO 42001
ISO 27001 A.8.1

Addendum

No ISO 42001 controls support UEM-11 topic of DLP, especially not configured on endpoint devices

EU AI ActFull Gap

No Mapping

Addendum

Amend Article 10, or issue a technical annex, to specify that Data Loss Prevention (DLP) technologies must be implemented for endpoints handling high-risk AI data.

NIST AI 600-1No Gap

MG-3.2-005

Addendum

N/A

BSI AIC4No Gap

C4 DM-02
C5 AM-05

Addendum

N/A

AI-CAIQ questions (1)

UEM-11.1

Are managed endpoints configured with data loss prevention (DLP) technologies and rules in accordance with a risk assessment?