DIGITAL ISNAD AND AI AUTHENTICITY: APPLYING HADITH SCIENCE TO MODERN CYBERSECURITY AND DEEPFAKES
China China 
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AI Authenticity, Deepfake Detection, Digital Isnad
Abstract
The rapid proliferation of artificial intelligence–generated content, particularly deepfakes, has intensified concerns regarding digital authenticity and trust in contemporary information ecosystems. Existing cybersecurity approaches predominantly rely on detection-based mechanisms that often fail to address the epistemological dimensions of authenticity, especially the traceability of information sources and transmission pathways. This study introduces the concept of Digital Isnad by drawing on the methodological rigor of hadith science as an alternative framework for verifying digital content authenticity. The purpose of this research is to develop a conceptual model that integrates principles of isnad such as chain continuity, narrator credibility, and cross-verification into modern AI-driven cybersecurity systems. A qualitative–conceptual design was employed, supported by interdisciplinary analysis combining hadith studies, artificial intelligence, and cybersecurity literature. Data were derived from peer-reviewed publications, classical texts, and documented cases of deepfake incidents, analyzed through conceptual mapping and comparative synthesis. The findings indicate that Digital Isnad provides a multi-layered verification model that enhances authenticity by integrating metadata traceability, source credibility scoring, and ensemble validation techniques. Systems adopting these combined mechanisms demonstrate improved resilience against manipulated content compared to conventional detection-only approaches. The study concludes that authenticity in digital environments must be reconceptualized as a relational and process-oriented construct. Digital Isnad offers a novel interdisciplinary framework with significant implications for advancing trustworthy and robust cybersecurity systems.
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