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Description

Libelle DataMasking (LDM) is a powerful, enterprise-level solution designed for the automated anonymization of sensitive personal information, including names, addresses, dates, emails, IBANs, and credit card details, converting them into realistic substitutes that preserve logical consistency and referential integrity across both SAP and non-SAP environments such as Oracle, SQL Server, IBM DB2, MySQL, PostgreSQL, SAP HANA, flat files, and cloud databases. With the capability to handle up to 200,000 entries per second and facilitate parallel masking for extensive datasets, LDM employs a multithreaded architecture, ensuring efficient reading, anonymization, and writing of data with exceptional performance. The solution boasts over 40 predefined anonymization algorithms—including those for numbers, alphanumeric characters, date shifting, and various forms of masking for names, emails, IBANs, and credit cards—along with tailored templates specifically designed for SAP modules like CRM, ERP, FI/CO, HCM, SD, and SRM. Additionally, its scalability and flexibility make it suitable for organizations of all sizes looking to enhance their data security measures.

Description

Naix is an innovative tool powered by AI that specializes in the anonymization and redaction of documents, effectively eliminating personally identifiable information in a fast, precise, and secure manner. Utilizing advanced techniques such as machine learning, natural language processing, and linguistic modeling, it is capable of identifying a wide range of sensitive data, including company names, legal forms, personal names, locations, email addresses, URLs, dates, identification numbers, images, and financial figures. This versatile tool is compatible with various file formats, including PDFs, Word and Excel documents, emails, scanned files, and images, and offers support for multiple languages, specifically English, German, and French. Naix streamlines the majority of the anonymization process while also providing manual redaction options to address any exceptions that may arise. Additionally, users can implement predefined pseudonyms to maintain context when complete anonymization is not feasible. The user-friendly interface guides individuals seamlessly from the initial document upload through to the review of findings and the generation of a secure output file, ensuring an efficient workflow throughout the process. Overall, Naix represents a comprehensive solution for organizations seeking to protect sensitive information while maintaining operational integrity.