Google DeepMind adds secure server-side memory to Private AI Compute
Google's Private AI Compute team detailed a new persistent memory layer for its platform, keeping encryption keys on user devices while data is decrypted only inside cloud secure enclaves. The architecture combines hardware-enforced enclaves, end-to-end encrypted channels, and per-user databases, aiming to give assistants cross-device continuity without Google accessing user data.
Sources and evidence
Attributed quotes
“Today, we are sharing how we will bring private, server-side memory to our Private AI Compute platform.”
“By combining hardware-enforced secure enclaves, encrypted channels, and per-user databases shielded by device-derived encryption keys, this architecture ensures your data stays fully private and under your control.”
Summary last validated Sep 28, 2026
Reader actions
Report an issue
Use this for an incorrect summary, wrong source, duplicate story, or wrong category. Submissions are private and do not change the story automatically.
Related coverage
- infrastructure
PyTorch details session-aware agentic inference with NVIDIA Dynamo
PyTorch published a blog post describing session-aware agentic inference with NVIDIA Dynamo. It explains that agentic workloads differ from single-turn chat: an agent session can include a large initial prefill, repeated model calls, and parallel subagents, changing the traffic an inference server handles.
- infrastructure
AWS details multi-team GPU sharing on SageMaker HyperPod
AWS published a reference architecture for sharing one Amazon SageMaker HyperPod EKS cluster across multiple teams. It uses AWS IAM Identity Center for authentication, per-team SageMaker Domains and Kubernetes namespaces for isolation, HyperPod Task Governance for fairness, and namespace-level cost allocation for chargeback.
- infrastructure
MIT's Christina Delimitrou targets data center energy efficiency
MIT Associate Professor Christina Delimitrou is rethinking how large cloud computing systems operate to reduce the environmental impact of data centers. Her work focuses on making these facilities more energy efficient as their environmental threat grows.
- infrastructure
Amazon Quick and Bedrock Knowledge Bases add real-time document-level access control for RAG
AWS published guidance on enforcing document-level access controls for enterprise RAG using Amazon Quick and Amazon Bedrock Knowledge Bases. The approach verifies user permissions directly with authoritative sources such as SharePoint, Google Drive, and Confluence at query time, rather than relying on pre-indexed permission copies, so answers only draw on documents each user is authorized to see.
- infrastructure
NVIDIA proposes digital twins and AI agents to validate AI factory changes
NVIDIA published a developer blog describing how digital twins and AI agents can validate changes in AI factories. The post notes these facilities combine GPUs, CPUs, switches, DPUs, and SuperNICs with schedulers, orchestration services, security controls, and a fast-changing software stack, making it hard to confirm infrastructure, software, and policies work together for target workloads.