What is Production Model Audit?

Success metrics include system uptime, model performance stability, deployment velocity, and operational cost efficiency.

Audit model performance against documented acceptance criteria and SLAs. Review fairness metrics across protected groups. Verify model documentation is current and complete. Check that training data governance requirements are met. Validate monitoring and alerting coverage. Review access controls and security posture. Assess compliance with applicable regulations like EU AI Act or MAS guidelines. Verify incident response procedures exist and are tested. A comprehensive audit covers technical, operational, and governance dimensions.

Conduct full audits annually for high-risk models and semi-annually for critical customer-facing models. Run lightweight automated checks monthly covering performance metrics, documentation currency, and monitoring coverage. Trigger ad-hoc audits after significant model changes, incidents, or regulatory updates. For regulated industries, align audit frequency with regulatory expectations. MAS and HKMA typically expect at least annual reviews of AI models used in financial decisions.

Internal audits should involve someone outside the model development team to provide independent perspective. Include a risk or compliance representative for regulated models. For high-stakes models, consider engaging external auditors with ML expertise. The audit team should understand both the technical aspects like model performance and fairness and governance aspects like documentation and access controls. Rotate auditors periodically to bring fresh perspectives and prevent blind spots from familiarity.

Audit model performance against documented acceptance criteria and SLAs. Review fairness metrics across protected groups. Verify model documentation is current and complete. Check that training data governance requirements are met. Validate monitoring and alerting coverage. Review access controls and security posture. Assess compliance with applicable regulations like EU AI Act or MAS guidelines. Verify incident response procedures exist and are tested. A comprehensive audit covers technical, operational, and governance dimensions.

Conduct full audits annually for high-risk models and semi-annually for critical customer-facing models. Run lightweight automated checks monthly covering performance metrics, documentation currency, and monitoring coverage. Trigger ad-hoc audits after significant model changes, incidents, or regulatory updates. For regulated industries, align audit frequency with regulatory expectations. MAS and HKMA typically expect at least annual reviews of AI models used in financial decisions.

Internal audits should involve someone outside the model development team to provide independent perspective. Include a risk or compliance representative for regulated models. For high-stakes models, consider engaging external auditors with ML expertise. The audit team should understand both the technical aspects like model performance and fairness and governance aspects like documentation and access controls. Rotate auditors periodically to bring fresh perspectives and prevent blind spots from familiarity.

Audit model performance against documented acceptance criteria and SLAs. Review fairness metrics across protected groups. Verify model documentation is current and complete. Check that training data governance requirements are met. Validate monitoring and alerting coverage. Review access controls and security posture. Assess compliance with applicable regulations like EU AI Act or MAS guidelines. Verify incident response procedures exist and are tested. A comprehensive audit covers technical, operational, and governance dimensions.

Conduct full audits annually for high-risk models and semi-annually for critical customer-facing models. Run lightweight automated checks monthly covering performance metrics, documentation currency, and monitoring coverage. Trigger ad-hoc audits after significant model changes, incidents, or regulatory updates. For regulated industries, align audit frequency with regulatory expectations. MAS and HKMA typically expect at least annual reviews of AI models used in financial decisions.

Internal audits should involve someone outside the model development team to provide independent perspective. Include a risk or compliance representative for regulated models. For high-stakes models, consider engaging external auditors with ML expertise. The audit team should understand both the technical aspects like model performance and fairness and governance aspects like documentation and access controls. Rotate auditors periodically to bring fresh perspectives and prevent blind spots from familiarity.