Framework

Three Layers of AI Safety Testing

A staged approach to validating whether a model is safe: first studying its internal alignment and mechanistic interpretability, then testing it against synthetic scenarios in controlled evaluations, and finally observing its behavior in real-world deployment, since a model can pass the first two layers yet still reveal problems only once used broadly by real users.