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Two Airbnb alumni are bringing intelligence and automation to data protection

TechBrunchBy TechBrunchApril 30, 20243 Mins Read
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When Julie Trias and Elizabeth Nammour worked together on Airbnb's data team, they had to deal with data that was spread across a variety of sources, and that increased dispersion led to challenges in keeping it safe. I did. The founders' own dissatisfaction with existing data protection options motivated them to start the company and build the automatic data protection tools they wanted.

On Tuesday, the startup Teleskope announced a $5 million seed investment.

“We tested a lot of different tools to help understand, protect, delete, and edit Airbnb data, but we found that there were no tools that could help developers do this automatically. “I realized that,” Trias told TechCrunch.

While not without solutions, existing ones such as data classification tools generated many false positives and had scaling issues. “There were no tools that could help you go from detection to actual remediation – editing the data, isolating the data, taking some action on the data.” The solution Teleskope built allows customers to It enables you to connect to different data sources, identify sensitive data across different data stores in an automated way, and isolate or delete it as needed.

They currently have several different use cases. “Right now, we're primarily working with product developers, but also data governance engineers and others who want to clean up entire data sets in a data warehouse, or who want to clean up entire data sets in a data warehouse. “We sell it to data teams who either use one data set or have multiple data sets and need to remove data for certain users for compliance purposes,” she said. said.

This solution relies on what Trias calls a “model pipeline,” where different models work depending on the type of data. “For example, we have trained a model that is very good at classifying data in natural language, such as conversational files. We have trained a model that performs very well in structured and tabular formats. We trained a model that can classify sensitive data in code base files or log files,” she said.

Despite having the background and pedigree to develop products like this, Trias was not familiar with the world of venture capital or how to pitch when she first started the company, and the female founding team was He said he faces bigger challenges. Investors in general. “I think the most difficult thing was that when we first started calling VCs, we had no idea how to go about it. We didn't even know what a design partner was. We were pre-product, pre-something, and we didn't know all the VC jargon, so when we had our first meeting with the VC, we were very prepared. “I wasn’t able to do that,” she said.

They were able to refine their presentation over time and find investors who understood their presentation and their vision. The seed funding was led by Primary Venture Partners with participation from Lerer Hippeau and Plug and Play Ventures, with participation from Essence VC, which led the company's pre-seed round.



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