Key Recovery
Specification
Define, implement and evaluate processes, procedures and technical measures to assess the risk to operational continuity versus the risk of the keying material and the information it protects being exposed if control of the keying material is lost, 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, 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 defines, implements, and evaluates processes, procedures, and technical measures to assess the tradeoff between operational continuity and the risk of key exposure in the event of keying material loss, including provisions for legal and regulatory requirements. 2. Confirm that the CSP conducts periodic risk assessments that evaluate key recovery scenarios across services (e.g., KMS, HSM, encrypted storage, model logs) and considers the impact of recovery failure or compromise on cloud-based encryption and AI workloads. 3. Review whether the CSP classifies keys by service function (e.g., storage encryption, database access, service tokens, model encryption) and includes all critical key types in recovery planning. 4. Validate that CSP recovery procedures include secure backups of keying material, protected with encryption, access controls, and mechanisms to prevent unauthorized access or misuse. 5. Confirm that key recovery processes are tested regularly in cloud environments (e.g., zonal failover, backup restore, automated KMS validation) to verify resilience without exposing sensitive data. 6. Review whether recovery actions require multi-party approvals, secure workflows, and documented justification (e.g., split knowledge, quorum-based authorization, break-glass access). 7. Verify that systems supporting key recovery operations are protected by strict access controls, least privilege enforcement, and tamper-evident logging. 8. Confirm that CSP key recovery considerations are integrated into enterprise risk management, service continuity planning, and compliance frameworks (e.g., ISO 27001, SOC 2, GDPR). 9. Validate that keys supporting AI-related functions (e.g., prompt encryption, inference data protection, model signing) are included in recovery strategies and are assessed for post-recovery data integrity risk. 10. Review whether the CSP coordinates with upstream technology providers (e.g., cryptographic library vendors, HSM manufacturers) and downstream consumers (e.g., APs, AICs) to define shared responsibilities, notify of key recovery events, and maintain cryptographic trust continuity. From CCM: 1. Examine if the organization has defined processes and procedures for handling the operational risk of compromised keys. 2. Determine if the key recovery process fulfills the organization and external business / operational continuity requirements. 3. Evaluate the significance of technical and organizational measures as per the key management lifecycle.
Standards mappings
No Mapping for ISO 42001 ISO 27001: A.8.24 Clause 6.1.2 ISO 27002: 8.24 5.7
Addendum
Add a control mandating AI systems to define, implement, and evaluate processes, procedures, and technical measures to assess operational continuity risks versus keying material exposure risks if control is lost, including legal and regulatory provisions, addressing ISO 42001:2023’s gap in specific key-related risk trade-offs, enhancing ISO 27001 (A.8.24, Clause 6.1.2) and ISO 27002 (8.24, 5.7).
No Mapping
Addendum
Cover the AICM control.
No Mapping
Addendum
No (explicit/implicit) reference to the recovery of cryptographic keys is made in the NIST AI 600-1 standard.
OIS-06 CRY-04 OPS-18
Addendum
N/A
AI-CAIQ questions (1)
Are processes, procedures, and technical measures to assess operational continuity risks (versus the risk of losing control of keying material and exposing protected data) defined, implemented, and evaluated, including provisions for legal and regulatory requirement provisions?