Nymiz offers AI-driven data anonymization and redaction software that replaces sensitive information with synthetic data, tokenization, or asterisks, ensuring compliance with GDPR and other privacy regulations. This technology enables organizations to securely share data for analysis and machine learning while maintaining data usability and context.
Funding
$7.6M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
SVFounders
Product
Problem
Organizations face increasing challenges in sharing and utilizing data for analysis and machine learning due to stringent data privacy regulations like GDPR, CPRA, and HIPAA. Traditional methods of data protection are often insufficient, leaving sensitive information vulnerable to exposure and hindering data-driven innovation.
Solution
Nymiz offers an AI-driven data anonymization and redaction platform that enables organizations to securely share and leverage data while maintaining compliance with global privacy regulations. The platform employs a range of techniques, including synthetic data generation, tokenization, pseudonymization, and redaction via asterisks, to replace sensitive information. Nymiz ensures that data remains usable and contextually relevant for analysis, machine learning, and other applications, without compromising individual privacy. The platform supports 102 languages and offers both reversible and irreversible workflows tailored to specific use cases and organizational goals. Nymiz can be deployed as a SaaS solution, via API integration, on-premises, or through IT consulting services.
Target Audience
Nymiz targets organizations across various sectors, including legal, finance, healthcare, and research, that require secure data sharing and analysis while adhering to strict data privacy regulations.
Features
- AI-powered identification and anonymization of personal information in 102 languages
- Versatile substitution methods including synthetic data, tokenization, and asterisks
- Customizable anonymization workflows to fit specific use cases
- Both reversible (pseudonymization) and irreversible (anonymization) options
- SaaS, API, on-premises, and IT consulting deployment options
- Integration with Large Language Models (LLMs) for secure data analysis and machine learning
- Compliance with GDPR, CPRA, HIPAA, and other privacy regulations