Encryption Change Cost Benefit Analysis
Specification
Manage and adopt changes to cryptography-, encryption-, and key management-related systems (including policies and procedures) that fully account for downstream effects of proposed changes, including residual risk, cost, and benefits analysis.
Threat coverage
Architectural relevance
Lifecycle
Data storage, Resource provisioning
Guardrails
Re-evaluation
Orchestration, AI Services supply chain, AI applications
Operations, Maintenance, Continuous monitoring, Continuous improvement
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. Verify that the CSP maintains a documented process for managing changes to cryptographic, encryption, and key management systems, including updates to infrastructure services (e.g., KMS, HSM), platform APIs, and customer-facing controls. 2. Confirm that proposed changes are reviewed and approved through formal governance mechanisms (e.g., architecture review boards, compliance committees, service design review). 3. Review whether each CEK-related change includes a cost-benefit analysis that considers trade-offs between security improvements, operational cost, performance, compliance alignment, and customer impact. 4. Validate that residual risks introduced by CEK changes are documented, evaluated, and either mitigated or accepted with appropriate justification. 5. Confirm that the downstream impact of proposed changes is assessed, particularly for services used by tenants (e.g., changes to key generation algorithms, access controls, or BYOK capabilities). 6. Verify that relevant internal stakeholders, including platform security, service owners, operations, legal, and customer support, are engaged in planning, review, and approval of CEK-related changes. 7. Review whether version tracking, rollback procedures, and documentation are maintained for all cryptographic changes, including infrastructure upgrades and tenant-impacting service modifications. 8. Validate that CEK changes are monitored post-implementation to confirm that intended security or performance benefits are realized, and validate that negative side effects (e.g., degraded service, integration breakage) are promptly addressed. 9. Confirm that lessons learned from prior CEK changes (e.g., audit findings, incident postmortems, customer feedback) are documented and factored into future risk and cost-benefit evaluations. 10. Verify that changes affecting upstream dependencies (e.g., cryptographic libraries, cloud hardware providers) and downstream consumers (e.g., APs, OSPs, AICs) are reviewed for compatibility and communicated where relevant. From CCM: 1. Obtain a copy of the change management policy and procedures. Confirm that these documents include assessment of impact on downstream effects, including residual risk, cost, and benefit analysis. 2. Examine recent changes made to cryptography-, encryption-, and key management-related systems (including policy and procedures), and confirm that these changes include an account of downstream effects of proposed changes, including residual risk, cost, and benefits analysis. 3. Confirm that the changes have been reviewed and approved by appropriate management.
Standards mappings
No Mapping for ISO 42001 ISO 27001: A.8.32 ISO 27002: 8.32 8.24
Addendum
Add a control requiring AI systems to manage and adopt changes to cryptography-, encryption-, and key management-related systems (including policies and procedures), explicitly accounting for downstream effects, residual risk, cost, and benefits analysis, with guidance on impact assessment and review, enhancing ISO 27001 (A.8.32) and ISO 27002 (8.32) for AI-specific cryptographic change management.
No Mapping
Addendum
Cover changes to cryptography, encryption, and key management systems (including policies and procedures) that fully account for downstream effects of proposed changes, including residual risk, cost, and benefits analysis.
No Mapping
Addendum
No (implicit/explicit) reference to cryptography, encryption, or key management is made in the NIST AI 600-1 standard, let alone to the management and adoption of changes to related systems.
CRY-01 CRY-04 DEV-03 DEV-07
Addendum
No explicit mentioning of "... the downstream effect of .... "
AI-CAIQ questions (1)
Are changes to cryptography-, encryption- and key management-related systems, policies, and procedures, managed and adopted in a manner that fully accounts for downstream effects of proposed changes, including residual risk, cost, and benefits analysis?