Fully Homomorphic Encryption (FHE) for AI prompts offers privacy for the encrypted data itself, but often fails to protect surrounding metadata. Developers may mistakenly believe encrypting the prompt body guarantees session privacy, overlooking that request shape, timing, model choice, and output logs can still reveal user workflows. A robust privacy strategy requires examining the entire system boundary, not just the encrypted fields, to identify all potential points of data leakage. AI
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IMPACT Highlights critical privacy considerations for AI systems using FHE, urging developers to look beyond encrypted data to protect user workflows.
RANK_REASON This is a technical explanation and analysis of a privacy concept, not a product release or research milestone.
