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@article{190794,
        author = {AMARNATH M},
        title = {The Virtual Gaze: Decoding the Paradox of Elastic Evidence and Multi-Tenant Jurisdiction in 2026 Cloud Forensics},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {8},
        pages = {4150-4156},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=190794},
        abstract = {The migration of global data infrastructures to virtualized environments has necessitated a fundamental evolution in Digital Forensic Science (DFS). This article examines the transition from the traditional "box-at-the-scene" model to a modern, log-centric approach dictated by the complexities of cloud architecture. As physical hardware becomes increasingly abstracted through Amazon Web Services (AWS), Microsoft Azure, and Google Cloud, investigators must navigate unique challenges such as multi-tenancy, data fragmentation, and the inherent ephemerality of serverless instances. The study details a three-dimensional forensic framework—Technical, Organizational, and Legal—to address these complexities. It explores critical methodologies including snapshot analysis, API-based telemetry collection, and remote live forensics, which are essential for preserving the "Order of Volatility" in non-persistent environments. Furthermore, the article analyzes the "Jurisdictional Thicket," highlighting the legal deadlock between the extraterritorial reach of the U.S. CLOUD Act and the privacy mandates of the EU’s GDPR and Brazil’s LGPD. Ultimately, the paper argues that the future of DFS relies on a synthesis of AI-driven data reconstruction and a deep understanding of the Shared Responsibility Model. By mastering these virtualized frontiers, the forensic community ensures that the "binary witness" remains an objective narrator of human intent in an era of borderless, ephemeral data.},
        keywords = {Cloud Forensics, Shared Responsibility Model, Ephemeral Instances, Jurisdictional Thicket.},
        month = {January},
        }