AI Governance Framework

The human governance layer

AI can generate tests, flag anomalies, and predict failures — but humans must govern the process. Five principles sit between raw AI capability and trusted, scalable QA.

Hover each layer to explore

Human decision gate
Final validation, sign-off, and accountability — always human-owned
Always on
5
Auditability & traceability Who did what, when, and why
Every AI-generated test decision must be logged, explainable, and traceable to a human approval. No black boxes in production QA.
4
Bias & fairness review Checking what AI might miss
AI models can encode biases from training data. Human reviewers validate that AI-selected test cases cover edge cases and minority user paths.
3
Risk-based prioritisation Humans set the stakes
AI can rank test coverage gaps — but humans decide which failures are acceptable and which would be catastrophic for the business.
2
Contextual validation Domain expertise AI lacks
Testers interpret AI outputs through the lens of business rules, regulatory context, and user expectations that no model fully understands.
1
Scope & intent definition Humans set the mission
AI executes within boundaries humans define. Test objectives, coverage targets, and quality criteria must be set by people — not inferred by models.
Foundation
AI Testing Engine
Test generation · Anomaly detection
Predictive analytics · Auto-healing

Framework adapted from QA governance best practices. Hover layers to explore each principle.