As artificial intelligence continues to expand into sensitive domains like healthcare, finance and government, data privacy and security are becoming critical bottlenecks. Whether it be due to regulatory compliance or IP protection imperatives, the question keeps popping up: how can we adopt AI without compromising the data confidentiality?
The Solution: Confidential AI
Confidential AI is the practice of keeping data protected at every stage of AI inference, including during processing, retrieval, and generation. Unlike traditional systems that secure data only when it's stored or in transit, Confidential AI ensures data is never exposed, even while it is actively being used by the model.
This matters because modern AI systems compute on a lot of data. They transform documents into embeddings, query those embeddings in vector databases, and return generated responses. Each of these steps introduces a point where this data is exposed in its unsecured form. If that data contains sensitive information - such as user content, protected health information, or proprietary business - this risk of exposure translates to significant monetary risks.
Confidential AI addresses this by removing the need to trust the infrastructure. It operates on systems where privacy and protection are designed directly into the architecture, not added on as an afterthought. It's not just a technology, it's a new way of building AI systems based on explicit boundaries, minimal trust, and verifiable controls. It reflects a growing need among teams and users to retain ownership over their data, especially as AI becomes a core part of their business.
With Confidential AI, teams can build retrieval-augmented generation systems that respond to internal documents without exposing those documents to the underlying database. They can deploy chat interfaces over sensitive business data without revealing content to shared infrastructure. They can deploy AI without running afoul of privacy regulation.
How Confidential AI Works
Confidential AI ensures that sensitive data is never exposed, even when actively processed by AI systems. It accomplishes this by combining two essential technologies: Trusted Execution Environments (TEEs) and Confidential Vector Databases.
1. Trusted Execution Environments (TEEs)
TEEs are secure, hardware-level enclaves that isolate and encrypt data during processing. Think of a TEE as a sealed, opaque box embedded directly in your CPU or GPU—outsiders can observe the box, but they can't see what's inside.
Here's how it works:
- Create the Enclave: Your CPU or GPU sets aside a special encrypted memory region that even the operating system and cloud providers can't access. Data is decrypted only once it's inside this secure enclave.
- Verify the Code (Remote Attestation): Before sending sensitive data into the enclave, your systems verify that it's running precisely the code it should. This prevents anyone from substituting malicious software or tampering with your workloads.
- Secure Processing: Once inside, data is securely processed in plaintext. But anyone outside sees nothing but encrypted gibberish—even if they have root access or physical control of the hardware.
Widely used TEEs today include Intel SGX, AMD SEV-SNP, and Nvidia Confidential GPUs, ensuring broad compatibility across platforms. This class of technology is more broadly known as Confidential Computing.
2. Confidential Vector Database
While TEEs solve the data processing security, there's still a critical gap: how data, especially AI embeddings, are stored and searched. Typical vector databases handle embeddings in plaintext, meaning sensitive information is still vulnerable.
CyborgDB closes this critical gap. It’s a Confidential Vector Database specifically designed to securely manage and search vector embeddings without ever decrypting them outside the enclave.
Here's how it protects your data:
- Encrypting Data from the Start: Embeddings and data points are encrypted immediately upon creation, never existing in plaintext outside of secure hardware enclaves.
- Secure Vector Search: CyborgDB uses cryptographic techniques allowing secure, privacy-preserving approximate nearest-neighbor searches directly over encrypted vectors. This ensures sensitive data remains protected even during querying operations.
- Attested End-to-End Security: Each query and response carries attestation data—cryptographic proof verifying it originated from an enclave running trusted code. Only authorized, verified enclaves can decrypt and utilize these responses.
The result? Teams can confidently deploy AI systems using sensitive information for retrieval-augmented generation (RAG), semantic search, and more without exposing data.
Bringing It All Together
Confidential AI's real power emerges when you combine these two parts:
- The TEE ensures data privacy and integrity during active processing.
- The Confidential Vector DB secures embeddings and queries, keeping sensitive data confidential across the entire AI pipeline.
With these combined, your AI workflow becomes end-to-end secure, eliminating the blind spots that traditional security methods can't address. Whether you're handling proprietary business data, sensitive healthcare records, or personal user information, Confidential AI provides peace of mind
CyborgDB is easy to evaluate and integrate. It supports common storage backends like PostgreSQL and Redis, and offers lightweight Python and C++ clients for development.
Try out CyborgDB for free:
# Install cyborgdb-lite
pip install cyborgdb-lite
Follow our Quickstart Guide for full step-by-step instructions.
Whether you are building internal tools or deploying user-facing AI systems, Cyborg helps you do it securely and responsibly without compromising on performance or developer experience.
