How Can AI Stay Private? Lattica’s $3.25M Funding Pushes Fully Homomorphic Encryption into the Spotlight Can AI process sensitive data without ever exposing it? This is the question Tel Aviv-based startup Lattica is attempting to answer as it steps out of stealth mode with $3.25 million in pre-seed funding. The company’s mission: solve one of artificial intelligence’s most persistent privacy challenges using Fully Homomorphic Encryption (FHE). Lattica Breaking Down Lattica’s Funding and Investor Interest Lattica’s pre-seed funding, led by Konstantin Lomashuk’s Cyber Fund, includes participation from notable investors like Sandeep Nailwal, co-founder of Polygon Network and Sentient: The Open AGI Foundation. The $3.25 million pre-seed injection positions Lattica to scale its cloud-based platform, which promises secure AI computation by enabling queries over encrypted data—without ever decrypting it. Investor interest signals a rising demand for privacy-enhancing technologies in AI, particularly in industries where compliance with data protection regulations is non-negotiable. According to Cisco’s 2025 AI Briefing, security remains a top concern, with 34% of CEOs citing it as a barrier to wider AI adoption. According What Makes Lattica’s Approach Different? FHE has long been hailed as the "holy grail" of cryptography, offering a way to compute on encrypted data. Yet, due to performance inefficiencies, it has largely remained a theoretical solution. Lattica addresses this challenge through its Homomorphic Encryption Abstraction Layer (HEAL), which standardizes and accelerates FHE operations across various hardware environments, including GPUs, TPUs, and ASICs. Dr. Rotem Tsabary, founder and CEO, explained, “We’re enabling practical FHE by developing a solution that is tailor-made for neural networks.” “We’re enabling practical FHE by developing a solution that is tailor-made for neural networks.” With a background in lattice-based cryptography from the Weizmann Institute, Tsabary’s vision leverages both hardware and software optimization to bridge the gap between secure computation and scalable AI deployment. Industry Focus: Healthcare and Finance in the Crosshairs Lattica’s platform is especially relevant to sectors like healthcare and finance, where sensitive data handling is both a regulatory and operational concern. Applications range from encrypted financial transactions to secure analysis of medical data for research purposes. Sandeep Nailwal commented, “Lattica’s product-first approach fundamentally transforms sensitive data processing in the AI ecosystem. Advances in the machine learning stack are significantly boosting FHE performance.” “Lattica’s product-first approach fundamentally transforms sensitive data processing in the AI ecosystem. Advances in the machine learning stack are significantly boosting FHE performance.” The startup’s own survey within the FHE community revealed that 71% believe FHE adoption will depend on combining hardware and software—validating Lattica’s hybrid approach. own survey within the FHE community Market Implications and Final Thoughts Lattica’s emergence reflects a broader trend: the increasing pressure on AI providers to ensure data privacy at all costs. As regulatory environments tighten globally, and as AI becomes further embedded in critical infrastructure, solutions like FHE may shift from niche research to mainstream necessity. The success of Lattica will depend not just on its technology, but on its ability to deliver on performance promises where others have stalled. If HEAL truly provides the acceleration needed, Lattica could be at the forefront of a privacy-first AI revolution. FHE has always sounded too good to be practical. Lattica’s hybrid model is ambitious but timely. The funding, though modest by AI startup standards, could offer enough runway to prove viability. The pressure is now on Lattica to show measurable performance gains that can persuade industries long wary of AI’s privacy risks. Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYO Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYO Vested Interest Disclosure: Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYO business blogging program business blogging program

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Why Lattica’s $3.25M Bet on Fully Homomorphic Encryption Could Change AI Privacy Forever

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