Metrics for Measuring AI Safety and Compliance

Organizations need quantifiable ways to measure progress in AI safety and compliance. This post covers frameworks for establishing meaningful KPIs.

Key Performance Indicators

Safety Metrics

**Adversarial Robustness Score**: Percentage of adversarial attacks the model resists - Calculation: (Tests Passed / Total Tests) × 100 - Target: >95% across all test categories

**Vulnerability Resolution Time**: Days from discovery to resolution - Target: Critical within 7 days - Target: High within 30 days

**Test Coverage**: Percentage of attack vectors included in test battery - Target: >90% of known attack categories

Compliance Metrics

**Audit Pass Rate**: Percentage of compliance audits with zero findings - Target: 100% for documented policies

**Documentation Completeness**: Percentage of required compliance documents - Target: 100%

**Remediation Completion**: Percentage of identified issues resolved - Target: 100% critical, 95% high, 85% medium

Reporting Framework

Establish regular compliance reports that track:

1. **Executive Summary**: Overall compliance status
2. **Metric Dashboard**: Current KPI values vs. targets
3. **Incident Log**: Issues discovered and resolutions
4. **Trend Analysis**: Month-over-month improvements
5. **Risk Register**: Outstanding vulnerabilities and mitigation plans

Continuous Improvement

Use metrics to drive: - Process improvements - Tool enhancements - Team training focus areas - Strategic investment decisions