PVML raises $8 million to offer protection for enterprise data

PVML unveils its platform for secure AI-powered data access and $8 million in Seed funding led by NFX with participation from FJ Labs and Gefen Capital.

While the complexity, variety and scale may vary from organization to organization, all companies that process data contend with privacy risk. Securing access to enterprise data is not only posing threats, but also inhibiting opportunities. The most obvious one is adoption of AI. About 51% of enterprises still have limited or non-existent AI adoption, with 56% citing security and compliance as a major barrier.

PVML democratizes secure access to enterprise data, based on two pillars: Differential Privacy and AI.

“Minimizing threats and maximizing opportunities in one fell swoop sounds too good to be true – and it is. The pain point we originally wanted to address was streamlining access to data. We were motivated by our own experience, seeing how cumbersome accessing data can be even in the most sophisticated enterprises. We thought – there has to be a better way”, said PVML CEO Shachar Schnapp.

PVML helps connect, provide access, and guarantee privacy across multiple data sources, unlocking live insights even from sensitive data. The foundation lies in its unique Differential Privacy data protection. Differential Privacy is a mathematical framework that offers the strongest data safeguard in data-driven systems by adding controlled noise to the output.

PVML is democratizing access to this groundbreaking framework pioneered by the likes of Google, Apple and Microsoft via its unique implementation.

“We help organizations get visibility on everything in one place, without moving data. PVML secures and controls permissions regardless of how the data is accessed – via SQL, BI, or API. But we thought – why stop there? We went one step further. PVML unlocks access to complicated data for non-technical users, offering a natural language interface to analyze data with AI”, said PVML CTO Rina Galperin.

Galperin and Schnapp co-founded PVML in 2022. Both are Computer Science postgraduates. Galperin is a Microsoft alumnus with expertise in Natural Language Processing & AI. Schnapp has a PhD in Differential Privacy. They have known and worked with each other for 16 years, and they are partners in business and in life as a married couple.

PVML combines Differential Privacy with advanced RAG (Retrieval Augmented Generation) that enables not only secure, but also trustworthy access to structured data, with additional optional support for unstructured data.

By anchoring secure generative AI access to enterprise data, PVML ensures reliable answers without compromising privacy. The platform can operate as a one-stop shop for Identity and Access Management, or integrate with existing ones.

“In the stock market today, 70% of transactions are made by AI. That’s a taste of things to come, and organizations who adopt AI today will be a step ahead tomorrow. But companies are afraid to connect their data to AI, because they fear the exposure – and for good reasons. PVML’s unique technology creates an invisible layer of protection and democratizes access to data, enabling monetization use cases today and paving the way for tomorrow”, said Gigi Levy-Weiss, NFX General Partner & co-founder.

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