Performance Monitoring

Purpose

This plan defines what aspects of the AI Management System (AIMS) are monitored and measured, the methods used, and the frequency of evaluation to ensure continuing suitability, adequacy, and effectiveness per ISO/IEC 42001:2023 Section 9.1.

Scope

This monitoring plan applies to all components of Rygen’s AIMS including:

• AIMS objectives performance
• AI system performance and effectiveness
• Risk management effectiveness
• Process conformity and effectiveness
• Stakeholder satisfaction

Monitoring and Measurement Framework

AIMS Objectives Monitoring

AI System Performance Monitoring

What is Monitored:

• Individual AI system performance metrics (accuracy, reliability, availability)
• User adoption and satisfaction
• Incident frequency and resolution
• Model drift and degradation

Methods:

• Automated evaluation reports (X1, Corsair evaluation systems)
• Performance benchmarking
• User feedback collection
• Incident tracking and analysis

Frequency:

• Continuous: System availability and basic performance
• Monthly: Detailed performance analysis
• Quarterly: Comprehensive system review

Risk Management Effectiveness

What is Monitored:

• Risk treatment implementation status
• Residual risk levels
• New risk identification
• Control effectiveness

Methods:

• Risk register review and updates
• Treatment plan progress tracking
• Incident correlation analysis
• Control testing and validation

Frequency:

• Monthly: Risk register updates
• Quarterly: Comprehensive risk review
• As needed: Risk reassessment for changes

Process Conformity and Effectiveness

What is Monitored:

• AIMS process adherence
• Documentation currency and completeness
• Training effectiveness
• Communication effectiveness

Methods:

• Process audit and review
• Documentation review
• Training records analysis
• Stakeholder feedback

Frequency:

• Ongoing: Process execution tracking
• Quarterly: Process effectiveness review
• Annually: Comprehensive process audit

Data Collection and Analysis Methods

Data Sources

Primary Sources:

• AI system evaluation reports
• Risk register
• Incident reports
• Performance monitoring systems
• User feedback and surveys

Secondary Sources:

• Management review minutes
• Audit reports
• Training records
• Client communications

Analysis Methods

• Trend Analysis: Track performance over time to identify patterns
• Gap Analysis: Compare actual vs. target performance
• Root Cause Analysis: Investigate performance deviations
• Comparative Analysis: Benchmark against previous periods
• Statistical Analysis: Use appropriate statistical methods for data interpretation

Reporting and Communication

• Monthly Reports: Operational metrics to technical teams
• Quarterly Reports: Strategic metrics to management
• Annual Reports: Comprehensive AIMS performance assessment
• Ad-hoc Reports: Incident-based or issue-specific analysis

Evaluation Schedule

Continuous Monitoring

• System availability and basic performance metrics
• Incident detection and response
• Risk indicator tracking

Periodic Evaluation

• Weekly: Operational performance review
• Monthly: Detailed performance analysis and reporting
• Quarterly: AIMS objectives review and management review
• Annually: Comprehensive AIMS effectiveness evaluation

Triggered Evaluation

• Following significant incidents
• After major system changes or deployments
• Upon stakeholder requests or concerns
• When regulatory requirements change

Performance Evaluation Criteria

Effectiveness Indicators

• Objective Achievement: Meeting defined AIMS objectives
• Process Maturity: Consistent and reliable process execution
• Stakeholder Satisfaction: Positive feedback from internal and external stakeholders
• Continuous Improvement: Evidence of learning and adaptation

Adequacy Indicators

• Resource Sufficiency: Adequate resources to meet objectives
• Scope Coverage: Complete coverage of AI systems within scope
• Risk Management: Effective identification and treatment of risks
• Compliance: Meeting all applicable requirements

Suitability Indicators

• Strategic Alignment: Support for business objectives
• Context Relevance: Appropriate for organizational context
• Scalability: Ability to grow with the organization
• Sustainability: Long-term viability and maintainability

Documentation and Records

Required Documentation

• This monitoring plan (maintained in Confluence)
• Monitoring and measurement procedures
• Data collection templates and forms
• Analysis and evaluation reports
• Evidence of monitoring and measurement results

Record Retention

• Performance data: 3 years minimum
• Evaluation reports: 3 years minimum
• Analysis records: 3 years minimum
• Management review inputs: Permanent

Roles and Responsibilities

• Principal AI Engineer: Overall responsibility for monitoring plan implementation and effectiveness
• AI Governance Committee: Review monitoring results and approve plan changes
• CTO: Strategic oversight and resource allocation
• Team Leads: Provide data and support monitoring activities in their areas
• All Staff: Contribute to data collection and provide feedback on AIMS effectiveness

Continuous Improvement

This monitoring plan will be:

• Reviewed quarterly for effectiveness
• Updated when AIMS scope or objectives change
• Enhanced based on lessons learned and feedback
• Aligned with evolving standards and best practices

Integration with Management Review

Results from this monitoring plan provide direct input to the management review process, ensuring:

• Data-driven decision making
• Evidence-based evaluation of AIMS performance
• Identification of improvement opportunities
• Strategic alignment of AIMS activities

Revision History