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Qohash Recon

Sensitive data discovery tool for unstructured data in files and cloud storage

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Qohash Recon Description

Qohash Recon is a sensitive data discovery and classification tool that scans unstructured data across on-premises and cloud environments. The tool operates as a standalone executable that deploys on a server or virtual machine and scans network-accessible data sources without requiring internet connectivity. Recon identifies and classifies sensitive data elements within files rather than just scanning file metadata. It scans over 350 file types and supports various data sources including endpoints, file servers, OneDrive, SharePoint, Google Drive, Box, Amazon S3, Azure Blob Storage, Azure File Shares, and Google Cloud Storage. The tool can process files of virtually any size. The platform detects multiple types of sensitive data including Social Security Numbers, credit card numbers, bank account numbers, driver's license numbers, passport numbers, health insurance numbers, tax identification numbers, IBAN, SWIFT codes, IP addresses, and passwords across US, Canadian, and global data types. Recon assigns likelihood scores to detected data elements and ranks risk according to sensitive data type. Users can configure the tool to prioritize specific types of sensitive data based on business requirements. The tool provides detailed explanations for each detection and enables remediation actions directly within the interface. The solution supports both on-demand and scheduled scanning, with the ability to generate compliance reports filtered by data type. It includes features for data cleanup, migration with permission checks, and recurring scan automation.

Qohash Recon FAQ

Common questions about Qohash Recon including features, pricing, alternatives, and user reviews.

Qohash Recon is Sensitive data discovery tool for unstructured data in files and cloud storage developed by Qohash. It is a Data Protection solution designed to help security teams with Cloud Security, Compliance, Data Classification.

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