Secludy AI

We're proud to be launching @Secludy today with $4M in seed funding. @ImpressionVC led the round. @LAUNCH and The Syndicate, a venture firm and angel investing group led by @Jason Calacanis, also joined, along with Wedbush Ventures, @PrecursorVC, @HustleFundVC, @scriptcapital, Mana Ventures, Chispa VC, and an amazing group of angel investors. Banks and fintechs are sitting on incredibly valuable data but can't use it to train AI models. Privacy laws, contracts, and cross-border rules keep it locked down. So my co-founder @Dr_Mingze_He and I got to work. Secludy unlocks your most sensitive data while keeping the utility intact. You can train custom models and do vendor evaluations without ever putting your sensitive data at risk or giving up AI model performance. Moving on to other regulated industries next... Also, special thank you to @sososazesh who wrote our first check and to all of our other existing investors. Read more in the comments.

The solutions already exist, they're just unevenly distributed (/think tanks are not aware of what startups are building to solve these problems) See: @Secludy

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5/ Ready to secure your data? Our PII Leakage Testing Tool helps you audit leakage in fine-tuned LLMs.

4/ The European Data Protection Board (EDPB) now wants companies to run these audits as proof of resistance to re-identification (EPDB Opinion 28/2024).

We just launched our PII Leakage Testing Tool on AWS Marketplace! We put data masking to the test. A thread 🧵👇

Training AI models on sensitive medical data poses big risks, as this TechCrunch article highlights. The same goes for unstructured text. Privacy-preserving techniques like training on differentially private synthetic data can ensure utility while protecting privacy 🛡️ techcrunch.com/2024/11/19/psa…

1/ My hot take on the State of AI Report 2024 by Air Street Capital 🔥 Synthetic data isn’t just for big AI labs anymore. It’s about to become essential for nearly ALL companies fine-tuning their models

@ajhodls @kepano Absolutely, training on differentially private synthetic data is the way to go. It gives privacy-guarantees so that you can protect users’ sensitive data while learning from your most valuable data

@kepano differential privacy can help secludy.com

if your data is stored in a database that a company can freely read and access (i.e. not end-to-end encrypted), the company will eventually update their ToS so they can use your data for AI training — the incentives are too strong to resist