The Only Comprehensive Privacy Solution for Creating Safe, Shareable & Synthetic Data

Gretel announced the general availability of its privacy engineering APIs and services. Gretel’s comprehensive offering enables users to classify, transform and generate the industry’s highest-quality synthetic data. Combined, these capabilities remove privacy bottlenecks for a myriad of development and workflow processes that prevent data sharing and stifle innovation. A free plan for developers is available to anyone who wants to get started, and usage-based options are available for larger projects and teams.

Gretel has tested its products in an open beta program for over a year, and incorporated improvements to its toolkit based on feedback from more than 60 enterprise engagements, its community of thousands of users, and open source users who have downloaded their SDK over 70,000 times.

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“Gretel’s advanced privacy guarantees also give users complete control to adjust data privacy levels, based on their project needs, and guard synthetic data against adversarial attacks.”

“We’ve built a privacy toolkit that’s accessible to all developers, and scalable to any enterprise-ready project. With Gretel, anyone can classify, anonymize, and synthesize data that’s privacy-proven and highly accurate in just a few clicks,” said Gretel CEO and co-founder Ali Golshan. “Gretel’s advanced privacy guarantees also give users complete control to adjust data privacy levels, based on their project needs, and guard synthetic data against adversarial attacks.”

“Today, working with data is… hard. Gretel is making it easier. By building flexible, secure, and easy to deploy tools to support data-driven developers, Gretel will open a world of progress across industries,” said Max Wessel, Executive Vice President & Chief Learning Officer at SAP.

Gretel has been working with teams and organizations across industries including healthcare, life sciences, finance, and gaming. Some of their recent work includes creating synthetic genomic data and synthetic time-series banking data. The broad interest in Gretel’s privacy engineering tools is not a surprising trend and is supported by analysts’ forecasts that by 2030, synthetic data will completely overshadow real data in AI models.

Gretel is committed to fostering a culture of trust, transparency, and shared knowledge with the public and developer community. They continue to open source their core synthetic data technology and research as well as offer free access to its tools through its Developer tier.

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Advanced Privacy Engineering Made Accessible

With Gretel’s all-in-one privacy stack, developers everywhere can streamline workflows, and access advanced privacy engineering tools to easily:

• Create highly accurate, privacy-proven synthetic data
• Seed pre-production systems with safe, statistically accurate datasets
• Identify and remove sensitive data to reduce PII-related risks
• Augment and de-bias datasets to train ML/AI models fairly
• Anonymize sensitive data in real time, for data at scale

“Asking data-driven developers to exchange real-world data for synthetics requires they not only have a deep dedication to privacy, but also access to simple, intuitive solutions that return value immediately. Gretel provides all of the above and helps simplify privacy engineering,” said Chris Hymes, the VP of Information Security, Data Privacy and Enterprise IT at Riot Games.