What Happens to Data Inside AI Agents
Summary: Conan Yu, co-founder of Rena Labs, argues that AI agents create a data-in-use problem because conventional encryption protects data in storage and transit but agents need it decrypted in memory, where it can be exposed to application code, logs, debuggers, infrastructure operators, or a compromised host. His work uses trusted execution environments (TEEs) across Intel TDX, AMD SEV, confidential GPU environments, and multiple clouds, paired with remote attestation so a client or key service gets signed evidence of the environment and measured software before releasing credentials or keys.
What's compelling: Yu's three-party financial model shows the stake: a principal can assess a trading firm's liquidity, spread, volume, or risk without receiving raw order-book data that would reveal the firm's entire proprietary strategy - but only if attestation, not a privacy policy, is what releases the key. Yu notes TEEs cannot repair vulnerable code, secure compromised clients, guarantee availability, or eliminate hardware and side-channel risks, so the question a security leader is left with is what attestation actually measures and who verifies it.
Read the original at The Hacker News ↗
- Topics
- Privacy
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