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Google has introduced Private AI Compute, a privacy-enhancing technology designed to process AI queries securely in the cloud while maintaining on-device-level privacy guarantees. The system leverages Trillium TPUs and Titanium Intelligence Enclaves, utilizing AMD-based Trusted Execution Environments (TEEs) to encrypt and isolate memory, ensuring that user data remains inaccessible to Google and protected from physical exfiltration attacks. The infrastructure supports mutual attestation and encrypted communication between trusted nodes, with strict controls to prevent unauthorized access and ensure that only validated workloads are executed.
To assess the security of this new platform, Google engaged NCC Group for a comprehensive review, including architectural analysis and detailed cryptographic assessments of key components such as the Oak Session Library, attestation mechanisms, and the T-Log system. The review also covered configuration and source code analysis to evaluate the robustness of the system's privacy and security controls. This independent evaluation highlights Google's efforts to provide strong privacy assurances for cloud-based AI processing, addressing concerns about data confidentiality and integrity in modern cloud environments.
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Google introduced Private AI Compute, a new architecture designed to process AI workloads with privacy protections intended to provide on-device-level confidentiality for supported use cases, including Pixel-related features. Multiple reports describe the launch as a security-focused enhancement to how sensitive AI requests are handled.
NCC Group released a public report reviewing Google’s Private AI Compute design and security properties. The publication provided third-party analysis of the platform’s privacy and security model following its introduction.
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Apple said some **Apple Intelligence** requests that exceed on-device limits will now run in Google Cloud under an expanded **Private Cloud Compute (PCC)** architecture, while simpler tasks continue to be processed locally and other cloud workloads remain on Apple-operated servers. The company said its newer model stack includes on-device `AFM 3 Core` and `AFM 3 Core Advanced`, Apple-hosted `AFM 3 Cloud` and `ADM 3 Cloud`, and `AFM 3 Cloud Pro`, which runs on Google-owned NVIDIA hardware for more demanding queries. Apple said the privacy model is unchanged despite the move to third-party infrastructure, with protections built around stateless processing, non-targetability, no privileged runtime access, and verifiable transparency. To enforce those guarantees on Google Cloud, Apple said it is using **NVIDIA Confidential Computing**, **Intel TDX**, Google’s **Titan** security chip, Apple-signed software attestation, and a cryptographically verifiable append-only ledger of approved hardware; the company also plans to publish PCC binaries and extend researcher access through the **Apple Security Bounty** program.
Jun 29, 2026
Google DeepMind has piloted a double-blind evaluation framework for its proprietary **Gemini 2.5 Flash Lite** model, designed to prevent benchmark leakage and contamination. External evaluators can submit confidential prompts, benchmarks, and scoring code without exposing them to Google, while Google keeps model weights and inference code confidential; the pilot is intended to validate the testing method rather than publish model-performance scores. The project involves the Singapore AI Safety Institute, OpenMined, AVERI, and MLCommons, using private benchmarks within a cryptographically protected environment. Its architecture combines Google Cloud Confidential Space, an NVIDIA H100 Confidential GPU, Intel TDX-encrypted host memory, remote attestation, and OpenMined PySyft controls. The safeguards go beyond contractual confidentiality and zero-logging practices, but the model still requires trust in Google-operated attestation and signing systems, the cloud platform, and underlying hardware vendors.
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Palo Alto Networks and Google Cloud have announced an expanded, multibillion-dollar partnership aimed at embedding advanced security capabilities directly into Google Cloud’s AI and infrastructure services. The agreement, reportedly valued at nearly $10 billion over several years, will see Palo Alto Networks’ Prisma AIRS platform integrated with Google Cloud offerings such as Vertex AI and Agent Engine, providing end-to-end protection for AI workloads, data, and development tools. This collaboration is a response to the increasing frequency of attacks on AI infrastructure, with Palo Alto Networks reporting that 99% of surveyed organizations experienced at least one attack on their AI systems in the past year. The partnership is designed to address the complexity of securing hybrid and multicloud environments as enterprises rapidly scale AI deployments. By aligning Palo Alto’s network and AI security solutions with Google Cloud’s infrastructure, the companies aim to deliver a unified security experience, reduce operational complexity, and ensure consistent enforcement of security policies. The deal also involves Palo Alto Networks migrating key internal workloads to Google Cloud, further deepening the strategic relationship between the two firms.
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