Model Continuous Monitoring
Specification
Define, implement, and evaluate processes, procedures, and technical measures for continuous monitoring of model performance metrics over time to identify sudden shifts or unexpected changes in predictions that could degrade model performance.
Threat coverage
Architectural relevance
Lifecycle
Resource provisioning
Design
Re-evaluation, Evaluation
Orchestration, AI Services supply chain, AI applications
Operations, Maintenance, Continuous monitoring, Continuous improvement
Data deletion
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. Examine the CSP's infrastructure monitoring systems and how they track resource utilization related to hosted AI models. 2. Verify the alerting mechanisms for detecting anomalies in resource consumption or performance that could indicate issues. 3. Assess integration of monitoring data with incident response processes. 4. Examine if the infrastructure ensures the model has high-quality data that does not cause data poisoning.
Standards mappings
ISO 42001 A.6.2.6 - AI system operation and monitoring ISO 42001 B.6.2.6 - AI system operation and monitoring ISO 42001 9.3.2 - Management review inputs
Addendum
N/A
Article 15 (2) Article 15 (3)
Addendum
N/A
MG-4.1-007 MG-4.2-001 GV-1.3-001 GV-1.3-002 MS-2.3-001
Addendum
N/A
C4 BC-03 C4 PF-01 C4 PF-02 C4 PF-07
Addendum
N/A
AI-CAIQ questions (1)
Are processes, procedures, and technical measures defined, implemented, and evaluated for continuous monitoring of model performance metrics over time to identify sudden shifts or unexpected changes in predictions that could degrade model performance?