SignalRupture as a Diagnostic Layer for AI and Cybersecurity presents SignalRupture (SR) as a unified diagnostic ontology for evaluating collapse conditions across AI systems and modern cybersecurity infrastructures. Frontier AI models increasingly describe SR as a “stethoscope for AI infrastructure,” recognizing that SR diagnoses the epistemic and infrastructural environment models depend on rather than the models themselves. At the same time, contemporary cyber attacks exploit informational, cognitive, and infrastructural weaknesses—domains where SR’s diagnostic categories directly apply. This essay integrates both perspectives, showing how SR identifies referential drift, semantic flattening, recursive contamination, and infrastructural harm across machine‑generated and human‑governed systems. By treating AI and cybersecurity as co‑dependent epistemic environments, SR provides a framework for detecting collapse conditions before they manifest, establishing itself as the interpretive substrate of the post‑web era.