Google has unveiled HEIR, an open-source compiler designed to help AI models perform inference on encrypted data.
HEIR stands for Homomorphic Encryption Intermediate Representation and is part of Google’s Private Computing Toolkit. The company says the technology can convert pre-trained AI models that normally work with unencrypted data so they can operate on encrypted inputs instead.
The announcement marks a significant step in Google’s effort to make privacy-preserving AI more practical. Instead of forcing users to expose sensitive information to an AI system, homomorphic encryption allows computations to happen while the underlying data remains encrypted.
How homomorphic encryption changes AI
Most encryption protects information while it is stored or transferred. The problem is that software usually needs to decrypt that information before it can perform useful computations.
Fully homomorphic encryption, or FHE, takes a different approach. It allows a computer to perform calculations directly on encrypted information and return an encrypted result that can later be decrypted by an authorized user.
That changes the privacy equation for AI. A service could potentially process sensitive information without seeing the underlying data, reducing the amount of private information exposed during computation.
Google points to several potential applications, including private recommendations, fraud detection and threat monitoring. The company says FHE could also help AI systems process sensitive information in industries such as healthcare and finance, where privacy requirements can make conventional cloud processing difficult.
What Google’s HEIR actually does
HEIR is not a new AI model, chatbot or standalone Google service. Instead, it is an open-source compiler toolchain and development platform that helps developers adapt programs and machine-learning models for homomorphic encryption.
Google says HEIR can take pre-trained AI models designed for ordinary, unencrypted inputs and convert them to operate on encrypted inputs. That removes some of the manual work that would otherwise require specialized cryptography expertise.
The project is built around MLIR, a compiler framework that allows programs to be represented and transformed across different levels. HEIR uses that foundation to support different homomorphic-encryption schemes and hardware backends.
Google’s broader goal is even more ambitious. The company says it wants HEIR to become a “one-click solution” that allows developers without specialized cryptography knowledge to add encrypted inference to production applications.
Google has already demonstrated private AI inference
The strongest part of Google’s announcement is that HEIR is not being presented only as a theoretical research project.
Google demonstrated four private-inference applications compiled with HEIR. They include a deep-learning recommendation model, a credit-card fraud detector, network-threat detection and a hotword detection system.
The examples show how encrypted AI could work in practical situations. A recommendation system could process private user information without exposing those features to the service provider, while a fraud detector could analyze sensitive transaction data without directly revealing it.
The network-threat example goes even further. Google says its system can detect anomalies in encrypted network traffic without revealing the contents of the network packets to the service provider.
The hotword detector offers another example. An audio-triggered AI agent could potentially recognize a wake word while keeping the underlying audio recordings private.
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The technology still comes with a cost
Despite the demonstrations, encrypted AI is not yet equivalent to conventional AI in terms of performance.
Google acknowledges that homomorphic encryption comes with a nontrivial computational cost. Performing calculations on encrypted information can require significantly more processing than performing the same calculations on ordinary data.
That makes performance one of the biggest challenges for the technology. Modern AI models can require enormous amounts of computation, so running large workloads entirely through FHE remains difficult.
Google is working to reduce that burden through both software and hardware. HEIR provides the compiler infrastructure, while the company’s broader FHE ecosystem includes work with hardware accelerators designed to speed up encrypted computation.
The company says the cost of homomorphic encryption is already falling. Its goal is to continue improving the technology until encrypted computation becomes practical across a much wider range of applications.
Why private AI matters
The timing of Google’s work is important because AI systems are moving into areas that involve increasingly sensitive information.
Hospitals want to use AI with medical records. Banks need models that can analyze financial transactions. Companies want AI systems to work with proprietary documents and internal data.
Encryption can protect this information when it is stored or transferred. But conventional AI systems still need access to the underlying information to process it, creating a difficult balance between usefulness and privacy.
FHE offers another option. If AI can perform inference while the input remains encrypted, organizations could potentially gain the benefits of cloud-based AI without exposing sensitive information in the same way.
Google wants encrypted AI to become easier to use
Google’s larger objective is not simply to demonstrate that encrypted inference is possible. The company wants to make the technology accessible enough for ordinary developers to use.
That is why HEIR’s compiler approach matters. Developers should not need to become cryptographers every time they want to build an application that processes encrypted information.
Google has already worked with companies developing hardware accelerators for homomorphic encryption, including Belfort, Niobium, Cornami and Optalysys. The company says HEIR has also become a research platform through collaborations with universities including Georgia Tech, Carnegie Mellon, UC Santa Barbara, Purdue and others.
Those efforts suggest Google sees FHE as more than a niche security technology. The company is trying to build an ecosystem around it, from software and research to specialized hardware.
The future of encrypted AI
HEIR does not mean that every AI model can suddenly run privately on encrypted data at normal speeds. The technology still faces significant performance and engineering challenges, especially for larger and more complex models.
What has changed is that Google now has working demonstrations showing that encrypted AI inference can be built with existing machine-learning models. That makes the discussion less about whether the technology is possible and more about how far it can scale.
If Google and the wider FHE community can continue reducing the cost of encrypted computation, privacy-preserving AI could become much more practical. That would be particularly important for industries where sensitive data has traditionally limited how much AI can be deployed.
For now, HEIR represents an important piece of that effort. Google is not simply trying to build more powerful AI; it is working on ways for AI to become useful without requiring sensitive data to be exposed first.
