On August 14, 2026, Google showcased HEIR. It is an open-source compiler toolchain. It helps developers run AI models on encrypted data.
Do you handle sensitive customer information? This method lets cloud servers process private inputs. The servers never see the raw text or numbers. The project connects standard AI workflows with homomorphic encryption. You can read more in Google’s announcement.
Where encrypted AI fits today
Usually, teams must pick between three choices. They send plain data to cloud APIs. They run models locally on user devices. Or they set up secure enclaves.
HEIR offers another path. It converts standard pretrained models. Then, the models compute directly on encrypted inputs.
Inside the public Google HEIR repository, Google shares runnable code for five sample workloads:
- Credit-card fraud detection: Spotting suspicious transactions without exposing account details.
- Kitsune network-anomaly system: Checking network traffic while keeping payload contents private.
- Deep-learning recommendation models: Giving personal suggestions while user preferences stay hidden.
- Audio hotword spotting: Finding trigger words without saving voice recordings.
- MNIST classification: Running standard digit recognition on encrypted images.
The real performance catches
Google says that four peer-reviewed publications use HEIR. It also points to partnerships with hardware-accelerator companies. That performance data comes directly from Google’s single-threaded CPU tests. No independent third party has verified these numbers under live production conditions.
Real-world performance remains a major question mark. Google openly admits that homomorphic encryption brings a heavy compute overhead. Converting models efficiently still takes skilled cryptographers. It is not a quick fix. Future speedups from hardware accelerators are also planned, rather than ready today.
How to test it yourself
Privacy comes at a price. Do not treat HEIR as an immediate replacement for standard cloud setups. Instead, test it for narrow workloads where privacy rules block normal cloud hosting.
You can test the toolchain safely on your own machines. Clone the Google HEIR repository. Pin the software revision. Next, run one published demo on a fixed CPU. Measure compile time, memory usage, latency, and model accuracy against your unencrypted baseline. Compare the real performance cost before committing your team to a larger migration.