A pair of Airbnb alums is bringing intelligence and automation to data protection

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When Julie Trias and Elizabeth Nammour were moving together astatine Airbnb connected nan company’s information team, they had to woody pinch information dispersed crossed a assortment of sources, and that increasing sprawl led to challenges successful keeping information safe. The founders’ ain vexation pinch nan existing harvest of information protection options motivated them to motorboat a institution and build nan automated information protection instrumentality they wanted.

On Tuesday, that startup, Teleskope, announced a $5 cardinal seed investment.

“We tested a bunch of different devices to thief america understand, protect, delete and redact information astatine Airbnb, but what we came to recognize is that location wasn’t that instrumentality that could thief developers do this automatically,” Trias told TechCrunch.

That’s not to opportunity location were nary solutions, but nan ones that existed for illustration information classification devices generated a batch of mendacious positives and had scaling issues. “There wasn’t a instrumentality that could thief you spell from discovery to existent remediation, which is redacting nan data, isolating nan data, aliases taking immoderate benignant of action connected nan data.” The solution Teleskope has built enables customers to link to their various information sources, place delicate information crossed a assortment of information stores successful an automated way, and isolate aliases delete it erstwhile necessary.

They presently person a fewer different usage cases: “We’re chiefly now trading to information teams, not conscionable a merchandise developer, but information governance engineers, who want to cleanable up their full information sets successful their information warehouse, aliases they want to cleanable 1 information group earlier they usage it for exemplary training, aliases they person aggregate information sets, and they request to delete information for a peculiar personification for compliance purposes,” she said.

The solution relies connected what Trias calls “a pipeline of models” pinch different ones coming into play, depending connected nan type of data. “So for example, we’ve trained a exemplary that’s really bully astatine classifying information successful earthy connection for illustration conversational types of files. We’ve trained a exemplary that useful really good pinch system tabular types of formats. We’ve trained a exemplary that tin categorize delicate information successful a codification guidelines record aliases a log file,” she said.

Trias says that successful spite of having nan inheritance and pedigree to build a merchandise for illustration this, they weren’t good versed successful nan world of task superior and really to transportation erstwhile they first launched nan institution — and female founding teams look a bigger challenge pinch investors successful general. “I deliberation nan hardest portion was that erstwhile we first started making VC calls, we had nary thought really to spell astir it. We didn’t moreover cognize what a creation partner was. We were pre-product, pre anything, and we didn’t cognize each nan VC lingo. And truthful we were very unprepared erstwhile we first took our first meetings pinch VCs,” she said.

They refined their position complete time, and were capable to find investors who sewage them and their vision. The seed backing was led by Primary Venture Partners pinch information from Lerer Hippeau and Plug and Play Ventures on pinch Essence VC, which led nan company’s pre-seed round.