Model Integrity Checks
Specification
Regularly calculate and compare checksums using cryptographic hashes of model checkpoints to detect unauthorized modifications. Apply at least annually based on the level of risk, or after any change of hands.
Threat coverage
Architectural relevance
Lifecycle
Data collection, Data curation, Data storage
Training, Design
Validation/Red Teaming, Evaluation, Re-evaluation
Orchestration, AI Services supply chain, AI applications
Operations, Maintenance, Continuous monitoring
Archiving, 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 implementation of data integrity monitoring for stored models, using checksums or other methods. 2. Assess procedures for detecting unauthorized modifications to stored model files. 3. Confirm procedures for alerting and responding to integrity check failures. 4. Verify the frequency of integrity checks aligns with the level of risk. 5. Examine mechanisms to ensure data is protected against tampering and potential security vulnerabilities.
Standards mappings
No Mapping for ISO 42001 27002: 8.26 Application security requirements
Addendum
ISO 42001 does not cover MDS-08 topic of using checksums with cryptographic hashes of models, at least annually.
Article 15 (5)
Addendum
N/A
GV-4.3-001 MS-2.7-005
Addendum
NIST AI 600-1 speaks to policies to measure cryptographic methodologies and measure the reliability of such, however, it does not speak to specifically to the MDS-08 guidance to "calculate and compare checksums using cryptographic hashes" regularly.
C4 DQ-03 C4 DM-02 C5 PSS-07
Addendum
N/A
AI-CAIQ questions (2)
Are checksums regularly calculated and compared using cryptographic hashes of model checkpoints to detect unauthorized modifications?
Are these measures applied at least annually based on the level of risk, or after any change of hands?