Key Purpose
Specification
Manage cryptographic secret and private keys that are provisioned for a unique purpose.
Threat coverage
Architectural relevance
Lifecycle
Data storage
Guardrails
Not applicable
Orchestration, AI Services supply chain, AI applications
Operations, Maintenance, Continuous monitoring, 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
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
Shared Orchestrated Service Provider-Application Provider (Shared OSP-AP)
The OSP and AP 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.
Implementation guidelines
Auditing guidelines
1. Verify that the CSP assigns cryptographic keys and secrets (e.g., tenant keys, HSM-stored credentials, API tokens) to a unique purpose (e.g., encryption, signing, authentication) and that purpose separation is enforced across cloud services, secret management systems, and cryptographic APIs. 2. Confirm that each key and secret is mapped to its intended function (e.g., volume encryption, TLS, identity verification) and that this mapping is documented, reviewed periodically, and integrated into key management systems. 3. Verify that technical and procedural controls prevent the reuse of a single key or secret for multiple cryptographic purposes across different cloud services or layers. 4. Review cryptographic service configurations (e.g., KMS, HSM policies) to ensure that key and secret usage is restricted to their assigned purpose within compute, storage, or networking scopes. 5. Confirm that access to purpose-bound keys and secrets is limited to authorized personnel, services, or systems based on their designated function and aligned with the principle of least privilege. 6. Validate that secrets and keys used in AI-related services (e.g., encrypted inference logs, signed outputs, model container authentication) are provisioned for distinct functions and not shared across services or tenants. 7. Review whether key and secret metadata includes attributes (e.g., tags, labels, purpose descriptors) that specify intended use and that enforcement of these attributes is implemented across cloud key management and monitoring tools. 8. Confirm that infrastructure-as-code (IaC), orchestration templates, and cloud deployment scripts enforce key and secret purpose separation, rejecting configurations that apply multi-use materials. 9. Verify that logging and audit mechanisms capture purpose-related usage metadata and detect any misuse of keys or secrets outside their defined function, with alerting or remediation triggers in place. 10. Validate that keys or secrets exposed to upstream or downstream parties (e.g., APs, AICs) are purpose-scoped and contractually or technically restricted from being reused for unrelated tasks or services. From CCM: 1. Obtain copies of the policy and procedures detailing the management of secret and private cryptographic keys. 2. Identify cryptographic secret and private keys that have been provisioned for a unique purpose. 3. Ascertain that these keys are being managed in accordance with policy and procedures.
Standards mappings
No Mapping for ISO 42001 ISO 27001: A.8.24 ISO 27002: 8.24
Addendum
ISO 42001 does not mandate managing keys with a requirement for unique provisioning per purpose, a critical detail for secure AI operations. Add a control for this requirement, ensuring each key is assigned to a specific use case with documented policies and procedures, addressing the gap in ISO 42001:2023’s lack of specific key management requirements. Include guidance on key segregation and purpose-specific provisioning, enhancing ISO 27001 (A.8.24) and ISO 27002 (8.24) for AI-specific cryptographic integrity.
No Mapping
Addendum
Cover the AICM control.
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 requirement of managing cryptographic keys in line with their set purpose.
CRY-04
Addendum
N/A
AI-CAIQ questions (1)
Are cryptographic secrets and private keys that are provisioned for a unique purpose properly managed?