All news with #google cloud tag

🔒 Unit 42 researchers describe a bucket hijacking technique that exploits globally unique storage bucket names across major cloud providers. By deleting a target bucket and recreating it under an attacker-controlled account with the same name, data streams (logs, Pub/Sub, replication, transfer jobs, etc.) can be silently rerouted to an adversary. The team validated the attack across Google Cloud, AWS and demonstrated cross-subscription scenarios in Azure, and has shared findings with the affected vendors.

🧩 Google Cloud's Starter Tier for Google AI Studio provisions a managed, limited stack (Cloud Run, Firestore, Cloud SQL for PostgreSQL, Firebase Auth) so individual Google Accounts can publish prototypes without a billing account. The environment is fully managed by Google, with region, APIs, and security policies preselected. It supports two active apps, a simplified console, and automatic agent-driven provisioning and code generation. Quotas, locked APIs, ephemeral storage, and upgrade paths to paid projects are explained.

🔍 Cloud Network Insights is now generally available as a Google Cloud-native solution, delivered in partnership with Broadcom AppNeta, to provide end-to-end visibility across multi-cloud and hybrid networks. It uses lightweight Monitoring Points and active synthetic probes to measure RTT, packet loss, jitter, and application-level metrics like DNS and page-load times. The service integrates with Cloud Monitoring, Cloud Logging, and supports OpenTelemetry, enabling proactive alerting, SLA validation, and rapid root-cause analysis.

🛠️ Siemens and Google Cloud built Knowledge Fabric, an AI system using knowledge graphs on Spanner Graph, the Google Agent Development Kit, and LLM APIs to modernize large industrial codebases. The platform models code relationships with GQL, uses embeddings and ANN for semantic search, and combines full-text search to deliver precise impact analysis. By "slicing the elephant," agentic workflows break large refactors into smaller tasks with human oversight, reducing engineering effort and preserving system integrity.

🧭 This post describes how Yahoo and Google Cloud built Seller Agent, an agentic media-buying platform that collapses multi-week manual workflows into governed campaigns executed in seconds. The architecture uses a dual-graph approach — a knowledge graph for deterministic business logic and a context graph for auditable decision traces — combined with Google Cloud services like Spanner Graph, BigQuery Graph, and Gemini. The design emphasizes explainability, regulator-grade governance, and closed-loop learning to ensure autonomous actions remain transparent and accountable.

🛡️ In his first Cloud CISO Perspectives, Chris Betz outlines four lessons guiding Google Cloud’s AI Threat Defense: Prepare, Scan and Prioritize, Remediate, and Monitor. He highlights how AI accelerates vulnerability discovery and defense, the importance of operational frameworks and harnesses, and the need for centralized tracking, risk-based patching, and continuous AI-driven monitoring. The guidance emphasizes reducing attack surface, close engineering collaboration, and building resilient systems.

📘 Today Google Cloud introduces the Open Knowledge Format (OKF), an open, vendor-neutral specification that formalizes the LLM-wiki pattern into a portable directory of markdown files with YAML frontmatter. OKF v0.1 defines a small set of conventions so different producers’ wikis can be consumed by agents without translation. The spec is intentionally minimal — one required type field per concept — and is accompanied by reference producer and consumer implementations and sample bundles.

🔒 Google Cloud announces collaboration with Apple to support Apple’s expanded Private Cloud Compute (PCC) systems on Google Cloud, built with Intel and NVIDIA. The effort leverages Google Cloud’s Titanium security architecture and Confidential Computing portfolio, including hardware Trusted Execution Environments, to protect data at rest, in transit, and in use. This layered approach aims to deliver verifiable integrity, no privileged runtime access, and enforceable privacy protections for sensitive AI workloads.

⚡ Google Cloud has announced general availability of Lightning Engine for Managed Service for Apache Spark, available in both serverless and managed cluster modes. Built on Gluten and Velox with Google-engineered enhancements, the engine compiles Spark physical plans into native C++ to accelerate vectorized operations, window functions, and sorting. It also optimizes connectors for Cloud Storage and BigQuery and adds cost-based optimizations like hash table caching, aggregation pushdown, and adaptive shuffle partitioning.

🟢 Claude Fable 5, Anthropic’s latest frontier model, is now generally available on Google Cloud. The model is designed for complex, multi-step reasoning and supports demanding use cases like advanced software development, long-horizon agents, and deep multimodal document analysis. Google Cloud highlights strong safeguards to make the model suitable for general use and positions it alongside other Anthropic offerings on the Agent Platform.

🔍 Storage Insights datasets now include activity insights that provide near-real-time visibility into object and bucket operations across your Google Cloud Storage estate. These BigQuery-linked views expose object-level writes, updates, deletes and errors, bucket-level aggregates and regional traffic patterns to support cost optimization and faster troubleshooting. The feature is generally available and customizable by org, folder, project, or specific buckets, enabling queries, Looker visualizations, and integration with other Storage Intelligence capabilities.

🔒 The article argues that sovereignty in Europe demands verifiable control, not just contractual promises. It emphasizes integrity, accountability, and transparency, and describes the Sovereign Cortex with T Security — a partnership with Deutsche Telekom and Google Cloud — as a practical response. The post underscores placing meaningful control with trusted local partners and treating sovereignty as a design principle.

🔍 Alcidion migrated its Miya Precision platform from Microsoft SQL Server to Google Cloud's AlloyDB for PostgreSQL to improve stability, performance, and operational overhead. The move used Database Migration Service and custom synchronization tools to achieve a rapid cutover, reducing transition time to about 15 minutes. The new architecture enabled dramatic speedups in JSON processing and reduced administrative burden for SREs.

⚙️ This announcement details Google Cloud’s updates to Managed Service for Apache Spark, now offered as Managed Spark clusters with serverless and managed modes. Key enhancements include Lightning Engine, a native C++ vectorized execution engine delivering up to 4.9x faster Spark performance, Flexible VMs for improved capacity resilience, and FinOps features like zero-scale clusters and scheduled stops. The release also adds the Model Context Protocol server and Data Agent Kit integrations to connect LLMs and developer tools securely to clusters, plus Lakehouse interoperability and Cluster Image 3.0 with Spark 4.1 preview.

🚀 This tutorial shows how to build an AI agent with Google’s Agent Development Kit (ADK), containerize it, and deploy it to GKE Autopilot using Vertex AI (Gemini) as the model backend. It walks through local testing, creating a multi-stage Docker image, pushing to Artifact Registry, and configuring a Kubernetes Deployment and Service. The guide emphasizes secure authentication with Workload Identity and exposes the agent via the Kubernetes Gateway API with a Google-managed TLS certificate.

🚀 Managed Service for Apache Spark runtime 3.0 reduces setup and startup friction for Spark workloads. It automates IAM, networking, and API provisioning to shorten the time to first job and cuts startup latency by 75% for standard and premium tiers. The runtime adds support for GPU obtainability via Dynamic Workload Scheduler Flex Start, enhanced multi-zonal execution with no cross-zone network charges, and compatibility with upcoming Spark 4.x features like Spark Connect.

🧭 This blog documents an experiment using Google Cloud to deploy a Gemma 3 inference workload across two regional GKE clusters, leveraging TPU v6e instances, managed DRANET for accelerator networking, and a multi-cluster Inference Gateway for cross‑region routing and failover. It describes building VPCs, reserving internal IPs, configuring Cloud Storage FUSE for model storage, creating TPU node pools with managed DRANET, registering clusters into a GKE Fleet, and deploying the inference server and gateway with health checks and autoscaling metrics. The objective is resilient, low‑latency routing to the nearest region with automatic failover to the other region if one fails.

🚀 Google Cloud Storage (GCS) is positioned as the preferred home for large-scale unstructured data and as a foundational component for production AI agents. This post highlights customer examples—Palo Alto Networks and Snap—using GCS as agent memory and analytics storage, and explains how the Model Context Protocol (MCP) enables secure, standardized access. Google offers two MCP server options: a fully managed Remote MCP server for easy, scalable deployments, and a self-managed Local MCP server for custom tooling and transformations, both integrated with Google Cloud security, observability, and tooling.

🚀 The post announces gcs-analytics-core, an open-source Java library that centralizes performance optimizations for Google Cloud Storage (GCS) across analytics engines like Apache Iceberg and Spark. Integrated natively in Apache Iceberg 1.11.0+, the library provides vectored I/O and smart Parquet footer prefetching to reduce I/O latency and improve throughput. Benchmarks using TPC-DS demonstrate sizable scan and execution time improvements across dataset sizes, and the project is available on GitHub for contributions and review.

🔍 At Google Cloud Next, Google announced the preview of graph algorithms in Spanner Graph, bringing Google Research’s graph mining capabilities natively to Spanner to deliver faster, cost-effective, large-scale structural analytics. The feature integrates with ISO GQL, runs on dedicated compute to avoid transactional impact, and scales to graphs with tens of billions of edges. Results can be written back to Spanner or exported to Cloud Storage.