TideCloak – Decentralized IAM for security and user sovereignty

Hacker News

Hey HN! After 6 years of R&D, our small team is excited to share our project TideCloak - an IAM designed to help developers move fast without worrying about catastrophic breaches or overpowered admins with keys to the kingdom. Traditional IAMs rely on centralized authority - admins, root certificates, and decryption keys - which create glaring vulnerabilities in a breach. To address this, we’ve integrated Keycloak (Red Hat’s IAM) with a decentralized key architecture powered by our (academically validated) Ineffable Cryptography. Here’s the idea: keys are split across a decentralized network (our Cybersecurity Fabric) so no one ever holds the full key. Even in a breach or F$%k up, there’s no unchecked authority exposed. Right now, TideCloak uses the Cybersecurity Fabric as an IdP, meaning users authenticate without their credentials being stored or shared. Essentially, users bring their own authority - without needing to trust anyone else to keep it safe. Coming soon: - Identity Governance Administration to prevent super admin abuse. - User-sovereign digital assets, where assets are secured with unique decentralized keys to protect against mass breaches. We’ve just launched a free developer sandbox, and we’d love your feedback: https://github.com/tide-foundation/tidecloak-gettingstarted It’s still early stages, and your input will help us improve. Thanks for taking a look - ask us anything!

Visit product View on Hacker NewsDec 19, 2024

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting

78%78% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Product HuntOn track for Day 1 leaderboard · Strong signals: user · Missing: mac, agents, macos

76%76% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: excited, ide, io · Missing: https docs, just released, exist

70%70% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Strong signals: soon, users · Missing: plus, platform, intuitive

44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: users · Missing: mobile apps, ios, personal

25%25% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

10%10% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: crypto · Missing: web3, chat, cryptocurrency

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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