What is Libelle DataMasking?
Libelle DataMasking (LDM) stands out as a robust enterprise-grade solution aimed at the automated anonymization of sensitive personal data, such as names, addresses, dates, emails, IBANs, and credit card information, transforming them into realistic proxies that maintain logical consistency and referential integrity across a range of environments, including both SAP and non-SAP systems like Oracle, SQL Server, IBM DB2, MySQL, PostgreSQL, SAP HANA, flat files, and cloud databases. Capable of processing up to 200,000 entries each second and supporting parallel masking for large datasets, LDM utilizes a multithreaded architecture that guarantees efficient data reading, anonymization, and writing while delivering remarkable performance. The solution features over 40 preconfigured anonymization algorithms, which cover a diverse array of masking strategies for numbers, alphanumeric sequences, date modifications, and specific formats for names, emails, IBANs, and credit cards, along with customized templates crafted for SAP modules like CRM, ERP, FI/CO, HCM, SD, and SRM. Moreover, its scalability and adaptability position it as an ideal choice for organizations, regardless of size, that are aiming to bolster their data protection strategies. This comprehensive approach to data security not only enhances privacy but also ensures compliance with regulatory requirements, making LDM an essential tool for modern enterprises.
Integrations
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Company Facts
Company Name:
Libelle
Date Founded:
1994
Company Location:
Germany
Company Website:
www.libelle.com/products/datamasking/
Product Details
Deployment
SaaS
Windows
Mac
Linux
On-Prem
Training Options
Documentation Hub
Online Training
Webinars
On-Site Training
Video Library
Support
Standard Support
Web-Based Support
Product Details
Target Company Sizes
Individual
1-10
11-50
51-200
201-500
501-1000
1001-5000
5001-10000
10001+
Target Organization Types
Mid Size Business
Small Business
Enterprise
Freelance
Nonprofit
Government
Startup
Supported Languages
English
