SEF · Security Incident Management, E-Discovery, & Cloud Forensics

SEF-02Cloud & AI Related

Service Management Policy and Procedures

Specification

Establish, document, approve, communicate, apply, evaluate and maintain policies and procedures for the timely management of security incidents. Review and update the policies and procedures at least annually, or upon significant 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

Data storage

Development

Guardrails, Supply Chain

Evaluation

Validation/Red Teaming, Re-evaluation

Deployment

Orchestration, AI Services supply chain, AI applications

Delivery

Operations, Maintenance, Continuous monitoring, Continuous improvement

Retirement

Data deletion, Archiving, 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

Shared Application Provider-AI Customer (Shared AP-AIC)

The AP and AIC both share responsibility and accountability 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 offer and consume.

Implementation guidelines

[All Actors]
1. Define a Service Management Policy that addresses secure, reliable, and compliant service operations across the AI/ML lifecycle.

2. Document lifecycle processes including deployment, scaling, deprecation, and emergency patching of AI systems.

3. Embed security, privacy, and resilience considerations into operational workflows.

4. Ensure the policy includes business continuity expectations during service disruptions related to AI components.

Auditing guidelines

1. Confirm the AP has documented policies and procedures to ensure timely response of incidents. 

2. Verify timely management expectations have been established and are based on business needs (e.g., regulations, contracts, incident severity level, ability to retrieve cloud data).

3. Review dependencies and partners which could impact the ability of the CSP to respond to the planned timelines 

4. Confirm regular audits of service management effectiveness and timely response to incidents.

5. Validate audit findings and lessons learned are addressed.

6. Verify documented training provided for service management procedures.

Standards mappings

ISO 42001Partial Gap

42001: 5.2
42001: A.2.2
42001: A.2.3
42001: A.2.4
42001: A.8.4
42001: B.2.1
42001: B.2.2
42001: B.2.3
42001: B.2.4
42001: B.8.4
27001: 5.1
27001: 5.2
27001: 7.3
27001: 7.4
27001: 7.5
27001: 9.1
27001: 9.3
27001: A.5
27002: 5
27001: A.16.1.2
27002: 16.1.2
27001: A.16.1.5
27002: 16.1.5

Addendum

Include operational controls for implementing, managing, and executing security incident response.

EU AI ActFull Gap

No Mapping

Addendum

Include requirements for policies and procedures covering Security Incident Management, E-Discovery, Cloud Forensics, and AI forensics with annual reviews.

NIST AI 600-1No Gap

GV-1.5-002
MG-2.3-001
MG-2.4-002
MG-2.4-003

Addendum

N/A

BSI AIC4No Gap

C4 PC-02
C5 SIM-01
C5 SP-02

Addendum

N/A

AI-CAIQ questions (2)

SEF-02.1

Are Service Management Policies and Procedures established, documented, approved, communicated, applied, evaluated, and maintained for the timely management of security incidents?

SEF-02.2

Are Service Management Policies and Procedures reviewed and updated at least annually, or upon significant changes?