Governance Program Policy and Procedures
Specification
Establish, document, approve, communicate, apply, evaluate and maintain policies and procedures for an information governance program, which is sponsored by the leadership of the organization and related to AI systems as well. Review and update the policies and procedures at least annually.
Threat coverage
Architectural relevance
Lifecycle
Team and expertise
Guardrails
Evaluation, Validation/Red Teaming
Orchestration, AI Services supply chain
Operations, Continuous monitoring, Continuous improvement
Archiving, Data deletion
Ownership / SSRM
PI
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
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 Customer (AIC)
The Customer (AIC) is 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 services or products they consume.
Implementation guidelines
Auditing guidelines
1. Policy Examination a. Verify that the organization has a documented and approved information governance policy that applies to its AI-optimized infrastructure and services. b. Confirm that the policy is aligned with organization's responsibilities for managing customer data, model hosting, training artifacts, and related AI workloads across tenants. c. Ensure the policy reflects leadership sponsorship and applies to internal organization's operations as well as support for tenant-level AI data governance requirements. 2. Policy Assessment a. Review the policy to confirm that roles and responsibilities are clearly defined for AI-related data isolation, secure provisioning, and lifecycle management in multi-tenant environments. b. Assess whether the policy includes provisions for handling AI-specific data assets (e.g., training datasets, model outputs, telemetry, service metadata. and supports transparency and accountability for customer-facing services. c. Verify that governance policies provide enabling features to support customer compliance needs, such as consent logging, clone detection, and transparency options, while maintaining clarity that ultimate compliance responsibility rests with the customer. 3. Evaluation and Review a. Determine whether the policy and procedures are reviewed and updated at least annually, or when significant changes occur that affect AI data governance processes, infrastructure, or platform services. Confirm that these reviews explicitly cover multi-tenant identity risks (e.g., clone activity, cross-customer exposure, memory sharing, and auditability of service-to-service data flows), with results documented in governance records. b. Confirm the review process includes participation from relevant stakeholders, such as platform engineering, cloud operations, compliance, and AI service teams. From CCM: 1. Examine the policy and/or procedures related to information governance programs to determine whether the organization has developed a comprehensive strategy for information governance. 2. Examine policies and procedures for evidence of review at least annually.
Standards mappings
42001: B.2.2 (AI policy) 42001; B.2.4 (Review of the AI policy) 42001: A.2.2 (AI policy) 42001: A.2.4 (Review of the AI policy) 42001: 5.2 (Assessing impacts of AI systems)
Addendum
N/A
Article 9 (Risk Management System) Article 16 (Obligations of Providers) Article 17 (Quality Management System) Article 19 (QMS Documentation)
Addendum
Review and update the policies and procedures at least annually, and require sponsorship by the leadership of the organization. Policy approval and communication.
GV-1.1-001 GV-4.1-001 GV-4.1-002 GV-4.1-003
Addendum
N/A
SP-01 SP-02
Addendum
N/A
AI-CAIQ questions (2)
Are policies and procedures established, documented, approved, communicated, applied, evaluated, and maintained for an information governance program that is sponsored by the leadership of the organization and related to AI systems as well?
Are policies and procedures for information governance program and related to AI systems reviewed and updated at least annually?