# Google releases HEIR open-source compiler for private AI inference on encrypted data

> Google has open-sourced HEIR, a compiler toolchain built to make private AI inference actually practical. The name stands for Homomorphic Encryption Intermediate Representation, and the idea behind it is quietly radical: run a trained model directly on encrypted data, produce an encrypted result, and never decrypt anything on the server. The user gets their answer, and the company running the model never sees the underlying input. Homomorphic encryption has been theoretically possible for years, but converting real models to run under it has been painstaking, expert-only work. HEIR aims to automate much of that translation, cutting the manual effort cryptographers have to pour in. Google frames the practical payoffs as things like recommendations, fraud detection, and on-device hotword recognition that never expose raw user data. Released as part of Google's Private Computing Toolkit, it is the kind of infrastructure story that rarely trends but could reshape what privacy-preserving AI looks like, especially in regulated fields like health and finance where the data can never leave in the clear.

_Section: [Daily AI Updates](https://www.wortins.com/daily-ai) · Source: Google · Published Sunday, August 16, 2026_

## Wortins' read

Google has open-sourced HEIR, a compiler toolchain built to make private AI inference actually practical. The name stands for Homomorphic Encryption Intermediate Representation, and the idea behind it is quietly radical: run a trained model directly on encrypted data, produce an encrypted result, and never decrypt anything on the server. The user gets their answer, and the company running the model never sees the underlying input. Homomorphic encryption has been theoretically possible for years, but converting real models to run under it has been painstaking, expert-only work. HEIR aims to automate much of that translation, cutting the manual effort cryptographers have to pour in. Google frames the practical payoffs as things like recommendations, fraud detection, and on-device hotword recognition that never expose raw user data. Released as part of Google's Private Computing Toolkit, it is the kind of infrastructure story that rarely trends but could reshape what privacy-preserving AI looks like, especially in regulated fields like health and finance where the data can never leave in the clear.

## Source

[Read the full story at Google](https://blog.google/security/how-google-is-making-private-ai-practical-with-homomorphic-encryption)

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