Confidential AI Conversations

Why Trusted Execution Environments are the only practical solution for private LLM inference.

Most AI chat systems were designed for convenience, not confidentiality.

Messages are sent in plaintext, decrypted in memory, logged for debugging, and often retained indefinitely. This makes traditional AI chats fundamentally incompatible with sensitive use cases.

vAilam is built on a different assumption: servers should not be trusted with user data.

vAilam Confidential Inference Architecture

User DeviceEncrypts messageClient-sideEncrypted payloadvAilam ServerTrusted Execution Environment (TEE)LLM InferencePlaintext exists only hereEncrypted response

Plaintext is decrypted only inside the Trusted Execution Environment. Outside the enclave, memory is encrypted and inaccessible to the OS, hypervisor, cloud provider, or operators.

The Core Privacy Failure

To generate a response, a language model must process your prompt in plaintext.

In most systems, this plaintext exists in normal server memory. That means it is accessible to operating systems, hypervisors, administrators, monitoring tools, and attackers after a breach.

Encryption in transit is irrelevant once data is decrypted in memory.

Privacy failures don’t happen on the network. They happen in memory.

Trusted Execution Environments

A Trusted Execution Environment (TEE) is a hardware-backed secure enclave that isolates code and data from the rest of the system.

Memory inside the enclave is encrypted. Execution is isolated. Even privileged software cannot inspect what happens inside.

The server runs the computation — but cannot see the data.

Confidential LLM Inference in vAilam

  1. Messages are encrypted on the client
  2. Encrypted data is transmitted to the server
  3. Decryption happens only inside a TEE
  4. The LLM runs entirely within the enclave
  5. Responses are encrypted before leaving the enclave
  6. No plaintext is logged or stored

Why Not Fully Homomorphic Encryption?

Techniques like fully homomorphic encryption and secure multi-party computation are theoretically elegant.

In practice, they are far too slow and expensive for real-time large language model inference.

TEEs provide the best balance between confidentiality, performance, and deployability today.

vAilam does not rely on privacy policies or promises.

Confidentiality is enforced by architecture. If the system cannot see your data, it cannot misuse it.