Use of AI Systems
Description
Covers operational use controls including system monitoring, performance tracking, feedback collection, and continuous learning management to ensure safe and effective operation.
Controls
| ID | Control | Description |
|---|---|---|
| A.8.1 | AI system monitoring | Continuous monitoring of deployed AI systems for performance degradation, drift, security threats, and unexpected behavi... |
| A.8.2 | AI system performance measurement | Regular measurement and reporting of AI system performance against defined metrics including accuracy, fairness, and lat... |
| A.8.3 | Feedback and complaints regarding AI systems | Mechanisms for users and affected parties to provide feedback, report concerns, and file complaints about AI system beha... |
| A.8.4 | Continuous learning and adaptation of AI systems | Controlled processes for AI systems that learn from operational data, including validation of learned behaviors and prev... |
Implementation Guidance
Deploy observability platforms tracking model predictions, confidence scores, input distributions, and fairness metrics in production. Establish dashboards reporting AI system KPIs to business owners and governance teams with alerting on threshold breaches. Implement user feedback channels (in-app reporting, customer service integration) with triage and response workflows. For continuously learning systems, implement human-in-the-loop validation of model updates and rollback procedures for degraded performance.
Evidence Requirements
Monitoring dashboards and alert configurations
Performance measurement reports
Feedback/complaint logs and resolution records
Continuous learning validation procedures