AI powered data anonymization platform enabling you to unlock the value of your data without compromising on sensitive data security.
Nymiz can anonymize (remove personal identifiers) or pseudonymize (replace personal identifiers with fake identifiers or pseudonyms) data before it’s processed by ChatGPT. This means ChatGPT can generate insights, answer questions, or train on datasets without ever accessing sensitive information directly.
Nymiz help ensure that use of ChatGPT complies with data protection laws such as GDPR, CCPA, and others by processing only non-identifiable data. This is crucial for businesses that operate across borders where different privacy laws may apply.
Nymiz may offer various techniques for de-identifying data, ranging from simple masking to more complex cryptographic methods, depending on the level of security and data usability required.
Nymiz can read data in 102 languages besides English and Spanish.
Anonymization and pseudonymization add a layer of security, making data less valuable to hackers and reducing the impact of potential data breaches.
Minimizing the amount of personal data you process reduces the risk of data breaches and the associated legal and reputational damages.
Helping businesses like yours maintain compliance and protect client data
Don’t let your data be a liability. Secure it with Nymiz.
Nymiz can transform original data into a synthetic version where direct and indirect identifiers are replaced or obfuscated. This process ensures that the data cannot be traced back to any individual.
When you want to leverage ChatGPT for insights, analytics, or to generate content based on your data, using the synthetic version ensures privacy and compliance.
Ensures compliance with data protection regulations (like GDPR, CCPA) by removing personal data from datasets.
Reduces the risk of data breaches involving personal information, as the data being processed or analyzed no longer contains sensitive information.
Unlike traditional anonymization techniques that may degrade data quality, synthetic data aims to maintain the statistical integrity of the original dataset, ensuring that analyses and machine learning models remain effective.
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