TORKING-X Metrics
A standardized 9-metric system for measuring AI governance health. Track your score over time to measure and improve governance.
Overview
TORKING-X provides a comprehensive, weighted scoring system that evaluates 9 dimensions of AI governance. Each metric is scored 0-100 and weighted based on its importance to overall governance health.
Your TORKING-X Score
View in DashboardExample score. Your actual score is calculated based on your configuration and usage.
Score Calculation
The overall TORKING-X score is a weighted average of all 9 metrics:
TORKING-X = (T × 0.10) + (P × 0.15) + (E × 0.10) + (Q × 0.15) +
(C × 0.15) + (S × 0.10) + (L × 0.10) + (M × 0.10) + (H × 0.05)| Score Range | Grade | Description |
|---|---|---|
| 90-100 | A | Excellent - Industry-leading governance |
| 80-89 | B | Good - Strong governance practices |
| 70-79 | C | Fair - Room for improvement |
| 60-69 | D | Needs Work - Significant gaps |
| <60 | F | Critical - Immediate action required |
The 9 Metrics
Tamper-Evidence
10% weightMeasures the integrity of your audit logs and chain verification.
Privacy Protection
15% weightEvaluates PII detection and redaction effectiveness.
Ephemeral Identity
10% weightAssesses session isolation and credential management.
Quality Guardrails
15% weightMeasures output validation and safety checks.
Circuit Breakers
15% weightEvaluates failure handling and recovery mechanisms.
Supply Chain
10% weightAssesses dependency security and verification.
Tool Safety
10% weightMeasures tool governance and permission controls.
Memory Integrity
10% weightEvaluates memory trust and drift detection.
HITL Enforcement
5% weightAssesses human oversight and approval workflows.
API Access
Retrieve your TORKING-X scores programmatically:
from tork import TorkClient
client = TorkClient(api_key="your_key")
# Get TORKING-X scores
scores = client.reports.get_torking_scores()
print(f"Overall Score: {scores['overall']}/100")
print(f"Grade: {scores['grade']}")
print()
print("Individual Metrics:")
print(f" T - Tamper-Evidence: {scores['tamperEvidence']}")
print(f" P - Privacy Protection: {scores['privacyProtection']}")
print(f" E - Ephemeral Identity: {scores['ephemeralIdentity']}")
print(f" Q - Quality Guardrails: {scores['qualityGuardrails']}")
print(f" C - Circuit Breakers: {scores['circuitBreakers']}")
print(f" S - Supply Chain: {scores['supplyChain']}")
print(f" L - Tool Safety: {scores['toolSafety']}")
print(f" M - Memory Integrity: {scores['memoryIntegrity']}")
print(f" H - HITL Enforcement: {scores['hitlEnforcement']}")
print()
print("Recommendations:")
for rec in scores['recommendations']:
print(f" - {rec}")Improving Your Score
The dashboard provides specific recommendations for each metric. Common improvements include:
- T:Enable chain verification for audit logs
- P:Add PII scanning to all agent interactions
- E:Use ephemeral credentials instead of long-lived tokens
- Q:Enable jailbreak detection and output validation
- C:Configure fallback chains for all providers
- S:Register and verify all dependencies
- L:Add governance policies for all tools
- M:Enable memory snapshots and drift detection
- H:Require HITL for critical actions