Policing the Dark Web in the AI Era

Abstract

The fast growth of dark web processes has made cybercrime an adaptive and intelligence-driven phenomenon that warrants a change in thinking about digital policing, traditionally basing it on fixed rules and post hoc investigation. The strategy involves integrating transformer-based semantic analysis, time behavior and human-in-the-loop decision support in ensuring that the insights of analytical knowledge, which can be explained and traced, are either procedurally sound. The framework is the first that views AI as an epistemic type of assistance, which resides in the socio-legal constraints, but not as a means of enforcing law. Experimental analysis shows that the given framework has a threat detection accuracy of 88.9% and F1-score of 87.6, both being superior to similar AI-based methods by 4-7 percentage points and retaining higher evidentiary preparedness and ethical integrity. The temporal analysis also displays a less volatile threat scoring with the varying dark web activity. .