Build AI Solutions Impervious To Vector Inversion Using CyborgDB’s Encrypted Vector Search Vector databases are the backbone of modern AI — powering everything from RAG systems and semantic search to fraud detection. But with great power comes a huge risk: embeddings are fully invertible. That means if a database is breached, the original data can be reconstructed — from sensitive medical records and financial transactions to proprietary code. In this hackathon, we’re challenging innovators like you to build AI applications that handle sensitive data without the vulnerability to vector inversion that kills most AI projects in legal review. Your illustration of vector encryption employed in real world scenarios could help define the next generation of secure AI systems. Build with embeddings that stay encrypted in use. Create interesting AI solutions that could actually be deployed in production scenarios without the data security risks! Win prizes. We’ve built encryption-in-use for vector search — delivering sub-millisecond encrypted queries at billion-vector scale. So far, it’s only been tested internally. Now, we need your expertise to prove its utility and push it further. Whether you’re: Building RAG systems for healthcare, fintech, or enterprise — help us uncover integration edge cases. Doing real-time fraud detection — stress-test our latency claims. Integrating with LangChain or LlamaIndex — find the rough edges we missed. Deploying on Kubernetes or serverless — challenge our assumptions. Task Your challenge is to employ CyborgDB's encrypted vector database using the given themes and provide actionable feedback that helps us refine the product. Deliver practical, actionable insights that directly shape the future of secure vector search. Your feedback is as valuable as your code.
