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Fremont, CA: Conversations about machine learning, big data, and artificial intelligence are increasingly becoming associated with discussions about privacy and data protection. Now, a startup that is developing tools to make it easier for engineers to implement both at the same time is announcing a round of growth funding in order to continue expanding its operations.
Gretel AI, which allows engineers to create anonymized, synthetic data sets based on their actual data sets for use in analytics and training machine learning models, has secured a $50 million Series B round of funding, which will be used to propel the company to the next stage of development. The product, which is built as a SaaS but can also be accessed via APIs, is still in beta but will be available to the general public later this year.
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Anthos Capital is the round's leader, with Section 32, Greylock, and Moonshots Capital also taking part. Greylock led the previous round of funding in 2020, and the startup has secured $65.5 million to date.
The idea behind using synthetic data sets is that it allows an organization to eliminate the risk of data leakage that may contain personal information or other sensitive data. There are other solutions to the same problem that involve data encryption, but this can be a time-consuming, costly, and resource-intensive approach with scaling challenges.
Gretel.ai arose from the three co-founders' direct experiences as cybersecurity specialists at a variety of organizations, including IBM, AWS, Netscout, and the US military over the years.
"We always found that using the right permissions with data was always the bottleneck," commented Ali Golshan, the CEO who co-founded the company with Alex Watson (CPO) and John Myers (CTO). They could see that the longer-term issue would be a growing need and priority for data privacy. "As the world moves from the web to the immersive world of sensors and IOT we are transitioning into a world where people will share their data unconsciously or unknowingly. But humans are not meant to be mined."
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