FileShadow, Inc. Unveils FileShadow Publish™; Providing Users with Content-Publishing Capabilities for Files Stored in the FileShadow Vault
Adobe (News - Alert) MAX-FileShadow, Inc. today announces FileShadow Publish™ - a new feature that allows users to publish collections of files from their FileShadow cloud vault by generating shareable links. FileShadow Publish is a simple and secure way for teams, professionals and individuals to curate, store and share cloud content.
With FileShadow Publish, users can share files with anyone, including people without FileShadow accounts, by sending a link to any FileShadow file, folder or collection. Users simply search, select and build the results into a collection that generates a unique shareable URL.
FileShadow Publish provides copyright protection to prevent unauthorized redistribution of users' shared files. The content owner is in complete control of who views, downloads and edits shared collections.
"FileShadow is expanding from a file-aggregation and archiving model to an intuitive, secure content aggregation and publishing platform, allowing users to increase productivity through secure internal and external collaboration by publishing easily shared links," said Tyrone Pike, president and CEO of FileShadow.
FileShadow will continue to build a metadata system for all files, regardless of their storage source, along with enhancing the sharing and collaboration model of its Publish system. The service provides a single, secure vault from which to search and access files by aggregating cloud storage accounts such as iClud, Adobe Creative Cloud, Adobe's Lightroom solutions, Box, Dropbox, Google (News - Alert) Drive, OneDrive and OneDrive for Business; local storage (macOS, Windows Desktops, Windows Virtual Desktops); and network and direct-attached storage (NAS/DAS) devices.
When a file is copied into the FileShadow vault, the service automatically generates searchable metadata for each file, including location (GPS), optical character recognition (OCR) of PDFs and machine learning (ML)-generated tags for images. By using machine learning, FileShadow provides superior search capabilities, allowing the user to find files quickly.
As a result of the automated tagging feature, users can quickly find images related to their search term. In a recent FileShadow update that provides custom tagging capabilities, users can adjust the terms associated with their files.
The FileShadow Service is free of charge for up to 100 GB of data. Subscriptions are available for more storage ($15/month for 1 TB; $25/month for 2 TB; each additional terabyte is $10/month). Subscriptions for FileShadow for Virtual Desktops are available for $25/month for 2 TB, and each additional terabyte is $10/month.
FileShadow is a service that aggregates files from multiple cloud sources, Windows Virtual Desktops, Windows PC and macOS desktops, Drobo, and other network and direct-attached storage (NAS/DAS) devices into one secure, reliable and searchable cloud vault. Compatible with Amazon WorkSpaces, Citrix (News - Alert) Virtual Apps and Desktops, HVE ConneXions VDI Solutions, IOXO Workspace Technology, Microsoft Windows Virtual Desktop, and VMware Horizon, FileShadow for Windows Virtual Desktop delivers thin-provisioned access to the user's vault.
Using machine learning, FileShadow provides superior indexing and searching capabilities. With FileShadow, users can quickly find any file with advanced search features such as file content, OCR of PDFs, GPS location and image searches. FileShadow is hosted on Google Cloud and IBM (News - Alert) Cloud with storage on IBM Cloud Object Storage (COS) and Wasabi's Hot Cloud Storage, providing "11 nines" of durability for optimal file protection. FileShadow supports multiple cloud storage sources, including Windows PC and macOS desktops (including iCloud Drive files and iCloud Photos), Drobo and other NAS/DAS devices, Adobe Creative Cloud, Adobe's Lightroom solutions, Box, Dropbox (News - Alert), Google Drive, OneDrive and OneDrive for Business.
Visit FileShadow.com for more information.
Utilizing Machine Learning to Predict Public Transportation Times