Conan Yu of Rena Labs argues that AI agents need verifiable protections for data-in-use, since conventional encryption does not stop sensitive information from being exposed while it is processed in memory. His work uses trusted execution environments and remote attestation to help protect confidential AI inference, private financial analysis, and other workloads without relying on trust alone. #ConanYu #RenaLabs #TrustedExecutionEnvironments #RemoteAttestation
Keypoints
- AI agents can expose private data while processing decrypted information in memory.
- Trusted execution environments isolate code and data from the host, OS, and administrators.
- Remote attestation proves that a workload is running in an approved environment.
- Yu’s three-party model keeps raw data, analysis logic, and client results separate.
- TEEs have limits, so builders must test logs, telemetry, debugging, and access paths for leaks.
Read More: https://thehackernews.com/expert-insights/2026/09/what-happens-to-data-inside-ai-agents.html