Risk Assessment and Impact Analysis
Specification
Determine the impact of business disruptions and risks to establish criteria for developing business continuity and operational resilience strategies and capabilities. Review and update the risk assessment and impact analysis at least annually or upon significant changes.
Threat coverage
Architectural relevance
Lifecycle
Data collection, Data curation, Data storage, Resource provisioning, Team and expertise
Not applicable
Not applicable
AI applications, Orchestration
Operations, Maintenance, Continuous monitoring
Archiving, Model disposal, Data deletion
Ownership / SSRM
PI
Owned by the Cloud Service Provider (CSP)
The Cloud Service Provider (CSP) is responsible for the design, development, implementation, and enforcement of the control to mitigate security, privacy, or compliance risks associated with cloud computing (processing, storage, and networking) technologies in the context of the services or products they develop and offer. The CSP is responsible and accountable for implementing the control within its own infrastructure/environment. The CSP is responsible for enabling the customer and/or upstream partner to implement/configure the control within their risk management approach. The CSP is accountable for ensuring that its providers upstream implement the control related to the service/product developed and offered by the CSP.
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
Auditing guidelines
1. Business Impact Analysis and Risk Assessment: Confirm that the organization performs a Business Impact Analysis (BIA) to identify critical business processes and the potential effects of disruptions, explicitly considering the critical dependencies such as applications, IT systems, infrastructure, vendors, and human resources. These dependencies play a crucial role in understanding the impact of business disruptions. Verify that the BIA quantifies impacts in terms of financial loss, reputational damage, operational downtime, regulatory non-compliance, and other relevant metrics. Ensure that risk assessments identify both internal and external threats that could lead to business disruptions, explicitly including any risks related to AI systems, cloud infrastructure, and third-party vendors. Check that risks are analyzed in terms of likelihood, impact, and existing controls to gauge residual risk and the effectiveness of current mitigation strategies. 2. Establishing Impact Criteria for Strategies: Verify that clear criteria are established based on the BIA and risk assessment outcomes. Criteria may include Recovery Time Objectives (RTO) and Recovery Point Objectives (RPO), financial thresholds (e.g., cost implications of downtime), criticality of business functions and interdependencies, compliance and regulatory implications, stakeholder expectations and service level agreements (SLAs), and risk appetite. Ensure that the organization has defined its risk appetite, which will guide decisions on accepting certain and risks versus the cost of implementing risk treatment plans. Assess how the impact criteria are used to determine the prioritization of business continuity strategies and resilience capabilities, confirm that criteria help in deciding which functions require immediate restoration versus those that can be scheduled for later recovery, check that the criteria are embedded within a broader operational resilience framework, ensuring alignment with overall business strategy and risk management, review documented methodologies that connect risk levels with tailored resilience strategies (e.g., scaling up resources, alternative service delivery, backup systems). 3. Governance and Continuous Improvement: Ensure that senior management is involved in establishing and approving the impact criteria and associated resilience strategies. Verify that there are documented discussions or meeting minutes demonstrating management’s role in this process. Confirm that the impact criteria and related strategies are reviewed periodically or whenever significant changes (internal or external) occur, especially considering the changing technology landscape (e.g., AI systems, cloud architecture). Check for evidence of feedback loops from past incidents, tests, or drills that inform updates to the criteria and risk treatment strategies. 4. Evidence and Observations Checklist: Business Impact Analysis (BIA) reports and risk assessment records: policy documents outlining criteria for evaluating business disruption impacts, detailed methodologies linking disruption impacts to resilience strategies, management approval documents and review meeting minutes, change logs or update records reflecting periodic reviews and revisions, and risk appetite documentation specifying thresholds for acceptable risk. From CCM: 1. Examine the policy to determine business impact and the criteria for developing business continuity. 2. Evaluate the process to review and approve the policy.
Standards mappings
42001: All A.10 (to ensure feedback of stakeholders on risks) 42001: A.2.2 AI policy 42001: A.2.3 Alignment with other organizational policies 42001: A.2.4 Review of the AI policy 42001: 5.1 Leadership and commitment 42001: 7.1 Resources 42001: 8.2 AI risk assessment
Addendum
The organization should conduct an impact analysis of business disruptions related to AI systems, including failures in model outputs, pipeline availability, or supporting services. Based on this analysis, the organization should define criteria for business continuity strategies, fallback capabilities, and operational resilience planning. This analysis should be reviewed at least annually or upon significant changes in the AI environment.
Article 9
Addendum
Determine the impact of business disruptions and risks to establish criteria for developing business continuity and operational resilience strategies and capabilities.
GV-4.2-003 MS-2.6-005
Addendum
Although there is coverage for third-party system impacts to be documented and verify that GAI system architecture can recover from negative impacts (which could be disasters), NIST AI 600-1 does not specifically speak to establishing business continuity and disaster recovery strategies, nor does it speak to creating risk and impact documentation or the annual review and update of this documentation.
C4 PC-01 SR-02 SR-03 C5 BCM-02
Addendum
N/A
AI-CAIQ questions (2)
Is the impact of business disruptions and risks determined to establish criteria for developing business continuity and operational resilience strategies and capabilities?
Is the risk assessment and impact analysis, reviewed and updated at least annually or upon significant changes?