Risk Based Planning Assessment
Specification
Perform independent audit and assurance assessments in response to significant changes or emerging risks and according to risk-based plans and policies.
Threat coverage
Architectural relevance
Lifecycle
Data collection, Data curation, Data storage
Design, Training, Guardrails
Evaluation, Validation/Red Teaming, Re-evaluation
AI Services supply chain, AI applications
Continuous improvement
Archiving, Data deletion, Model disposal
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 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
Auditing guidelines
1. Examine the process for determining the risks applicable to the organization's systems and environments. 2. Determine if a list of such risks is maintained and reviewed. 3. Determine if senior management exercises oversight over the applicable risks. 4. Determine if the audit plan is risk-based and if it is scheduled on an annual basis.
Standards mappings
42001: 9.2.1 General - Internal audit 42001: 9.2.2 Internal audit program 42001: 10.2 Nonconformity 27001: 9.2.1 General - Internal audit 27001: 9.2.2 Internal audit programme 27001: A.5.35 Independent review of information security 27001: A.5.36 Compliance with policies rules and standards for information security 27002: 5.35 Independent review of information security 27002: 5.36 Compliance with policies rules and standards for information security
Addendum
Add: A dedicated control requiring independent review or assurance A clause mandating risk-triggered audits (not just scheduled ones) A direct link between the audit program and AI-specific risk assessments. Until these enhancements are added, 42001 on its own presents a Partial Gap, which is closed when supplemented by ISO/IEC 27001 and 27002.
Article 9 (2) Article 9 (6) Article 43 (1) Article 43 (4) Article 93 Article 17
Addendum
Include structured, procedural, or independent assurance mechanisms.
GV-2.2-004 GV-6.1-006 MGP-1.1-003 MGP-1.2-004
Addendum
Needs to have a formal audit process, require independent assurance, and link controls to risk-based audit plans or policies.
COM-02 COM-03
Addendum
Risk-based planning not covered in policy
AI-CAIQ questions (1)
Are independent audit and assurance assessments performed in response to significant changes or emerging risks and according to risk-based plans and policies?