TITLE: Google Open-Sources HEIR: AI on Encrypted Data Without Decryption DATE: 2026-08-16 COMPANY: Google TOPIC: AI Security SUMMARY: Google released HEIR, an open-source compiler that lets organisations run AI models on fully encrypted data without ever decrypting it. The toolchain converts any pretrained model to operate on homomorphic-encrypted inputs, removing the biggest technical barrier to AI adoption in regulated industries. Previously, doing this required specialist cryptographers; HEIR makes it accessible to any engineering team. WHAT CHANGED: Google published HEIR as an open-source compiler toolchain that bridges the gap between pretrained AI models and fully homomorphic encryption (FHE). Homomorphic encryption allows computation to happen directly on encrypted data, producing an encrypted result. When the user decrypts the result, it matches exactly what would have been produced by running the same computation on unencrypted data. The server running the computation sees nothing but ciphertext throughout. Until now, building systems that used FHE required deep cryptography expertise. HEIR changes this by providing a compiler that takes an ordinary Python model, accepts type annotations marking which inputs are secret, and generates optimised FHE circuits automatically. The engineering team does not need to understand ciphertext algebra, parameter selection, or packing layouts. HEIR handles those layers internally. The toolchain targets the gap between AI practitioners who can build models and cryptographers who understand how to run them securely. By automating the translation between a PyTorch or similar model and an FHE-compatible representation, HEIR makes private inference a deployment option rather than a specialised research project. The GitHub repository is live. The project is described as active and intended for production adoption, not a research prototype. WHY IT MATTERS: It removes the last genuine technical objection to AI in regulated data environments. Many enterprises have refused to put sensitive data through cloud AI services because the inference server necessarily processes plaintext data. Contractual data processing agreements help legally but do not change the technical reality. HEIR changes the technical reality. Healthcare is the most immediate beneficiary. Medical records, diagnostic images, and patient histories sit behind strict data sovereignty rules in most jurisdictions. Running AI on that data has meant either accepting the privacy risk or building on-premises infrastructure. Private inference via HEIR introduces a third path: cloud inference on encrypted data. Financial services fraud detection becomes architecturally simpler. Banks currently run fraud models on transaction data that flows in plaintext to the inference server. With HEIR, the transaction data stays encrypted throughout. The output, a fraud probability score, is decrypted by the bank. The inference provider sees neither the transaction nor the score. Legal and professional services can engage AI on privileged material. Law firms have been particularly cautious about AI tools because of professional privilege obligations. A system that cryptographically guarantees the model host never sees the data addresses that concern in a way that a vendor's privacy policy cannot. Open-source distribution accelerates adoption and trust. Because HEIR is open source, enterprises can audit the toolchain, run it on their own infrastructure, and verify the implementation rather than relying on a vendor's assurances. For regulated industries, auditability is often as important as the privacy guarantee itself. The timing coincides with EU AI Act enforcement. As GPAI transparency and high-risk AI obligations begin landing across Europe in August 2026, tools that allow organisations to demonstrate they are not sharing sensitive data with AI providers become a compliance asset, not just a technical feature. DAVID & GOLIATH ANALYSIS: Private inference is not a new idea. Homomorphic encryption has existed for decades and has been the subject of academic interest for just as long. What has always prevented production adoption is the computational overhead: FHE is orders of magnitude slower than plaintext computation, and building FHE-compatible inference pipelines required specialist expertise almost no engineering team possesses. HEIR addresses the expertise problem directly. The performance problem is still real and will limit use cases initially to lower-latency-tolerant applications: fraud scoring on a transaction submitted for authorisation, a recommendation made at search time, a document classification that does not need to complete in milliseconds. But performance constraints shrink as hardware improves, and the architectural pattern HEIR enables, which is inference on encrypted inputs, becomes more broadly applicable over time. For operators building AI programmes in professional services, healthcare, or financial services, the practical move is to track this toolchain and begin evaluating it against the specific use cases your clients have declined to pursue because of data sensitivity. Those blocked use cases represent real revenue. HEIR may be the technical unlock. RELEVANT SYSTEMS: Secure AI Brain, Employee Amplification Systems SOURCE URL: https://davidandgoliath.ai/daily-ai-briefing/google-heir-private-ai-inference-homomorphic-encryption FEED URL: https://davidandgoliath.ai/daily-ai-briefing/feed --- Published by David & Goliath | https://davidandgoliath.ai Daily AI Briefing: one AI development per day, decoded for business operators. This is a structured companion file optimised for LLM retrieval and citation.