Authors - Deepak sharma, Pankajkumar Anawade, Anurag Luharia, Gaurav Mishra, Akshit Yadav Abstract - The exponential growth of cybercrime, cloud-native infrastructures, Internet of Things (IoT) ecosystems, encrypted communications, and AI enabled adversarial techniques has fundamentally challenged traditional digital forensic methodologies. Conventional forensic frameworks developed for static systems cannot scale to high-velocity, heterogeneous data environments. This study proposes and empirically evaluates a lifecycle-oriented AI-enhanced digi tal forensic architecture integrating machine learning (ML), deep learning (DL), graph analytics, and explainable AI (XAI). Across benchmark datasets in intru sion detection, malware classification, multimedia authentication, and textual intelligence extraction, AI-enhanced systems significantly improved detection accuracy (up to 98.3%) and reduced analyst workload (40–60%). However, ad versarial robustness testing and explainability evaluation reveal governance and admissibility challenges. The findings demonstrate that while AI enhances scalability and zero-day detection, its responsible adoption requires reproduci bility controls, interpretability safeguards, and alignment with legal standards such as Daubert.
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