Cyborg

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Cyborg

@CyborgHQ

Privacy without Compromise

New York, NY Katılım Mayıs 2013
15 Takip Edilen96 Takipçiler
Cyborg
Cyborg@CyborgHQ·
Vector DBs store embeddings in plaintext. Embeddings are invertible—breach one, reconstruct medical records or codebases. Join our CyborgDB Encrypted Vector Search #Hackathon at #GTCDC: Build RAG with encryption-in-use! $10K prizes + collaborative dev. RSVP: tinyurl.com/mxj94cck #GTC2025
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Cyborg
Cyborg@CyborgHQ·
OWASP #8 identifies embedding inversion as a real threat. Traditional encryption-at-rest doesn't protect during queries. @cyborgHQ implements encryption-in-use with GPU-accelerated search. Stop by GTC Booth I-8 to see it running on the new Nvidia DGX Spark. @owasp @LLM_Top10 @nvidia #GTC2025
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Cyborg
Cyborg@CyborgHQ·
Vector databases quietly hold your most sensitive AI data, and unlike password hashes, embeddings are fully invertible. Database breach → reconstruct the original data. cyborg.co built CyborgDB to fix this. Stop by Booth I-8 to see us! #NVIDIAGTC #GTC2025 #GTCDC
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Cyborg
Cyborg@CyborgHQ·
Before you upload, ask: should we be encrypting this? CyborgDB is the first and only vector database proxy that delivers encryption-in-use, customer-controlled key management, and high-speed compatibility with existing systems: cyborg.co #VectorDB #RAG #InfoSec #AI
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Cyborg
Cyborg@CyborgHQ·
Your compliance team hasn't caught up yet. GDPR, HIPAA, SOC - none account for semantic similarity search across embedded data. "We can delete the document" doesn't help when its meaning is still encoded in high-dimensional space next to everything else.
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Cyborg
Cyborg@CyborgHQ·
Before you upload your docs to that vector database, consider: you're about to make your entire organizational knowledge searchable by anyone who compromises a single API key. There is a way to securely encrypt vector data, and you should.
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Cyborg
Cyborg@CyborgHQ·
Introducing CyborgDB — the first Confidential Vector Database built for secure, enterprise-grade AI. CyborgDB enables high-performance vector search over fully encrypted embeddings. The missing piece for Confidential AI. More info: cyborg.co/blog/introduci…
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NVIDIA AI Developer
NVIDIA AI Developer@NVIDIAAIDev·
We have teamed up with @CyborgHQ to enhance the security of vector databases using the RAPIDS cuVS library, an #opensource toolkit that accelerates vector search with state-of-the-art algorithms. Technical deep dive ➡️ developer.nvidia.com/blog/bringing-… This collaboration aims to bring GPU acceleration to Cyborg’s encrypted vector search engine, ensuring robust security without compromising performance.
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Cyborg
Cyborg@CyborgHQ·
Stealth BETA - our private, searchable cloud storage platform is now available on Web! Get 25GB of end-to-end encrypted storage for free: cyborg.co/beta?utm-sourc…
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Cyborg
Cyborg@CyborgHQ·
Encrypted search has been the missing element for the widespread adoption of end-to-end encryption. But what is it? Learn more: cyborg.co/blog/what-is-e…
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Cyborg
Cyborg@CyborgHQ·
Just because something's encrypted, doesn't mean it's private. Our Lead Software Engineer, @Alex_Helle_, explains why end-to-end encryption is the gold standard of data privacy: cyborg.co/blog/what-is-e…
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