The TGIC Drift Detector v2 introduces a universal, physics-inspired framework for identifying early signs of model drift in modern AI systems. Unlike traditional statistical or distribution-matching techniques, TGIC measures the internal temporal stability of a model by tracking how quickly its state changes relative to its expected baseline time-scale. By combining Stability , Drift , and the derived Time Field , TGIC computes a real-time “gap-signal” that exposes hidden deviations long before they manifest in accuracy degradation. The method is model-agnostic, unsupervised, architecture-independent, and capable of detecting subtle internal failures even when no labeled data or performance metrics are available. TGIC v2 formalizes this approach into a practical, deployable law-driven drift detection mechanism that strengthens AI reliability, prevents silent failure, and offers a unified way to monitor complex systems under changing conditions.