Key Archival
Specification
Define, implement and evaluate processes, procedures and technical measures to manage archived keys in a secure repository requiring least privilege access, which include provisions for legal and regulatory requirements.
Threat coverage
Architectural relevance
Lifecycle
Data storage
Not applicable
Not applicable
Orchestration, AI Services supply chain, AI applications
Operations, Maintenance, Continuous monitoring
Archiving, Model disposal
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. Verify that the CSP defines processes, procedures, and technical measures to securely archive cryptographic keys that are no longer in active use but must be retained, including provisions for legal and regulatory requirements (e.g., customer BYOK, cloud-native encryption keys, and service-level key retention policies). 2. Confirm that archived keys are stored in secure key repositories (e.g., cloud KMS, HSM-backed vaults) that enforce encryption at rest and apply access control restrictions. 3. Review whether least privilege access controls are enforced for archived key repositories, with access granted only to roles with approved responsibilities related to compliance, legal, or service continuity. 4. Validate that access to archived keys is gated through approval workflows and that all access attempts are logged with metadata including requester, timestamp, and access rationale. 5. Confirm that archived keys are logically segregated from active key inventories and are not usable for encryption, decryption, or signing operations. 6. Review the CSP’s key retention policy to ensure that archived keys are stored for durations aligned with applicable legal, contractual, or industry obligations (e.g., PCI DSS, GDPR, HIPAA). 7. Verify that the CSP conducts periodic reviews of archived key inventories to assess continued retention requirements and identify candidates for destruction. 8. Confirm that technical safeguards are implemented to prevent unauthorized recovery, duplication, or reactivation of archived keys. 9. Validate that archived keys supporting AI-related functions (e.g., encrypted logs of prompts, AI model outputs, API tokens) are included in the CSP’s key archival scope and follow defined retention procedures. 10. Review whether the CSP coordinates with upstream providers (e.g., hardware KMS vendors) and downstream consumers (e.g., APs, AICs) to ensure archived key dependencies are documented, monitored, and incorporated into shared retention strategies. From CCM: 1. Confirm the existence of a documented and valid process for key archival. 2. Verify that the key archival process implements least privilege throughout the key archival cycle. 3. Establish whether the storage medium is secure, as per internal and external requirements.
Standards mappings
No Mapping for ISO 42001 ISO 27001: A.8.24 A.8.2.1 ISO 27002: 8.24 8.4
Addendum
Add a control mandating AI systems to define, implement, and evaluate processes, procedures, and technical measures to manage archived keys in a secure repository with least privilege access, including legal and regulatory provisions, addressing ISO 42001:2023’s gap in key archiving requirements, enhancing ISO 27001 (A.8.24, A.8.2.1) and ISO 27002 (8.24, 8.4).
No Mapping
Addendum
Cover the AICM control.
No Mapping
Addendum
No (explicit/implicit) reference is made in the NIST AI 600-1 standard as to the requirement of defining, implementing, or evaluating processes, procedures, and/or technical measures for the management of archived keys.
CRY-01 CRY-04 PSS-08
Addendum
N/A
AI-CAIQ questions (1)
Are processes, procedures, and technical measures to manage archived keys in a secure repository (requiring least privilege access) defined, implemented, and evaluated including provisions for legal and regulatory requirements?