HushHush Data MaskingHushHush
|
Soflab G.A.L.L.Soflab Technology Sp. z o.o.
|
|||||
Related Products
|
||||||
About
Today’s businesses face significant punishment if they do not meet the ever-increasing privacy requirements of both regulators and the public. Vendors need to keep abreast by adding new algorithms to protect sensitive data such as PII and PHI. HushHush stays at the forefront of privacy protection (Patents: US9886593, US20150324607A1, US10339341) with its PII data discovery and anonymization tool workbench (also known as data de-identification, data masking, and obfuscation software). It helps you find your and your customer's sensitive data, classify it, anonymize it, and comply with GDPR, CCPA, HIPAA / HITECH, and GLBA requirements. Use a collection of rule-based atomic add-on anonymization components to configure comprehensive and secure data anonymization solutions. HushHush components are out-of-the box solutions designed to anonymize both direct identifiers (SSN, credit cards, names, addresses, phone numbers, etc.) as well as indirect identifiers, with both fixed algorithms.
|
About
The Soflab G.A.L.L. application is designed to anonymize sensitive data in non-production environments, enabling the generation of high-quality synthetic data that remains consistent with real data and supports reliable testing. At the same time, it ensures full protection of sensitive information, effectively preventing data leaks.
Reduced data breach risk by replacing real data with artificial equivalents and detecting sensitive or erroneous records. Lower legal and financial exposure while protecting customer transactional data. Unified anonymization across non-production systems ensures a consistent data model and preserved production relationships. Synthetic data, generated from key production attributes, maintains statistical consistency for BI and AI. A central test data repository enables controlled reuse, lowers maintenance costs, accelerates deployments (up to 5 days), and supports simulation and reusable scenarios.
|
|||||
Platforms Supported
Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook
|
Platforms Supported
Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook
|
|||||
Audience
For establishments that want to de-identify their customers' complex data
|
Audience
All which have to deal with anonymization sensitive data in non-production environments and to ensure full protection of sensitive information by preventing data leaks.
|
|||||
Support
Phone Support
24/7 Live Support
Online
|
Support
Phone Support
24/7 Live Support
Online
|
|||||
API
Offers API
|
API
Offers API
|
|||||
Screenshots and Videos |
Screenshots and VideosNo images available
|
|||||
Pricing
No information available.
Free Version
Free Trial
|
Pricing
No information available.
Free Version
Free Trial
|
|||||
Reviews/
|
Reviews/
|
|||||
Training
Documentation
Webinars
Live Online
In Person
|
Training
Documentation
Webinars
Live Online
In Person
|
|||||
Company InformationHushHush
Founded: 2013
United States
mask-me.net
|
Company InformationSoflab Technology Sp. z o.o.
Founded: 2008
Poland
soflab.pl/en/
|
|||||
Alternatives |
Alternatives |
|||||
|
|
||||||
|
|
||||||
|
|
||||||
Categories |
Categories |
|||||
Compliance Features
Archiving & Retention
Artificial Intelligence (AI)
Audit Management
Compliance Tracking
Controls Testing
Environmental Compliance
FDA Compliance
HIPAA Compliance
Incident Management
ISO Compliance
OSHA Compliance
Risk Management
Sarbanes-Oxley Compliance
Surveys & Feedback
Version Control
Workflow / Process Automation
GDPR Compliance Features
Access Control
Consent Management
Data Mapping
Incident Management
PIA / DPIA
Policy Management
Risk Management
Sensitive Data Identification
HIPAA Compliance Features
Access Control / Permissions
Audit Management
Compliance Reporting
Data Security
Documentation Management
For Healthcare
Incident Management
Policy Training
Remediation Management
Risk Management
Vendor Management
|
||||||
Integrations
No info available.
|
Integrations
No info available.
|
|||||
|
|
|