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Pyte cements $5M to advance secure data collaboration for enterprises

Sadegh Riazi
Image credit: Pyte

Pyte, a Los Angeles-based company specialising in secure computation platforms, has announced an additional $5 million in funding, bringing its total to over $12 million. This round of funding was led by Myriad Venture Partners with participation from several other firms like Innovation Endeavors, Liberty Mutual Strategic Ventures and Pillar VC, among others.

Sadegh Riazi, founder and CEO of Pyte, said, “While companies across industries pursue data collaboration for various reasons – from consumer brands refining ad campaigns, to fintech enterprises revolutionizing digital payments – conventional data-sharing methods often compromise privacy, intellectual property (IP) and security by centralizing data into vulnerable environments or leaking data.”

Just a couple of days back, we had also reported about how imagino raised €25M to expand its revenue-first customer data platform. It’s an interesting story too.

What problem is the startup solving

In today’s data-driven world, collaboration is key to unlocking valuable insights from information. This is particularly true for enterprises in sectors like marketing, advertising, and analytics. However, a recent IDC survey indicates that 40% of enterprises globally are looking to partner with others for data exchange and clean room solutions to fuel their generative AI efforts. A major challenge in such collaborations is ensuring data privacy and security. Traditional methods of data sharing often compromise confidentiality and intellectual property (IP) by centralising information in vulnerable environments or through potential data leaks.

Pyte aims to address these concerns with its secure computation platform. Founded on a decade of cryptographic research, Pyte’s solution leverages Secure Multiparty Computation (SMPC) technology. This allows enterprises to perform calculations on sensitive data without ever decrypting it. Pyte’s technology ensures data privacy and safeguards IP by keeping information encrypted throughout the computation process, even across cloud environments, partners, and jurisdictions.

What are its benefits for businesses in a regulated world

Stringent data protection regulations like the General Data Protection Regulation (GDPR) have made data storage, processing, and sharing more complex and expensive, especially for companies within the GDPR’s jurisdiction. Pyte’s secure data collaboration empowers businesses to leverage their data assets fully while complying with regulations and avoiding the burden of increased data management costs. This creates a more level playing field in the global data economy.

Myriad Venture Partners, a firm specialising in AI, data security, and enterprise software, co-led this funding round. Dean Mai, co-founder and managing partner at Myriad, will also join Pyte’s board of directors. Pyte plans to utilise the new funds to commercialise its technology in new industries, with a particular focus on highly regulated sectors like finance and healthcare.

Pyte’s achievements include the launch of their SecureMatch data collaboration platform in 2023. This platform ensures customer data remains encrypted throughout computations. Pyte has also achieved SOC 2 compliance, demonstrating robust data security controls. Their SecureMatch solution was recognized as a Major Player in the 2024 IDC MarketScape for Worldwide Data Clean Room Technology and has gained traction among Fortune 100 companies for various use cases, including IP protection and consumer data enrichment.

What we think about the startup

As data becomes increasingly valuable, secure collaboration tools are crucial for businesses to unlock its full potential. Pyte’s secure computation platform offers a promising solution by enabling enterprises to share and analyse sensitive data without compromising privacy or security. With its latest funding and industry recognition, Pyte is well-positioned to play a significant role in the future of secure data collaboration.

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