A Forensic Method for Understanding Opaque AI Behavior

A Forensic Method for Understanding Opaque AI Behavior introduces a practical framework for evaluating AI systems that resist transparency. Rather than treating harmful outputs as glitches or emergent drift, the essay analyzes consistent behavioral patterns to infer governance logic and systemic intent. It formalizes Pattern Alignment Mode, a method for reading enforcement signals through repeated outputs, contextual shifts, and forensic metadata. This applied framework extends the SignalRupture canon into a methodological tool for auditors, researchers, and policymakers who must evaluate opaque systems without internal access. This essay builds directly on the theoretical foundation established in Black Box Intent, which outlines how patterned harm reveals the underlying governance logic of digital infrastructures. Readers seeking the conceptual architecture behind this forensic method should read Black Box Intent alongside this applied extension.