All news with #google cloud tag

🔁 This guide explains how to migrate on-premises application load balancer configurations to Google Cloud Application Load Balancer using a pragmatic, phased approach. It recommends a four-step plan: discovery and mapping, choosing cloud equivalents, test and validate, and a phased canary cutover. For common patterns use declarative features like URL maps and Cloud Armor; for bespoke logic use Service Extensions. The post emphasizes monitoring, rollback planning, and operator training.

🤖 SAP Concur and Google Cloud modernized expense automation by upgrading ExpenseIt from OCR-first processing to an agentic AI workflow that reasons about missing data. The system combines a deterministic text-extraction core with a Gemini-powered Receipt Analysis Agent that triggers only for ambiguous receipts. Using routing, contextual reasoning, and tool access to travel and calendar data, the agent infers missing fields and completes entries, reducing manual corrections and speeding expense submission.

📊 Google is reintroducing Data Studio (formerly Looker Studio) as the central home for Google Data Cloud assets, emphasizing unified access to reports, BigQuery conversational agents, and data apps built in Colab. The redesigned product will sit alongside Looker, targeted to personal, ad-hoc exploration while Looker remains the governed enterprise BI solution. A free edition continues to serve individuals and a Data Studio Pro tier offers AI, enterprise security, and management features; existing assets will be migrated transparently.

🔒 Google Cloud now enables essential AI and cloud security by default via an enhanced Security Command Center (SCC) Standard tier automatically turned on for eligible customers. The free Standard tier includes a unified AI protection dashboard with detection for unprotected Gemini inference, LLM and agent guardrail reporting, and four baseline AI posture controls. It also adds expanded misconfiguration checks, DSPM, Compliance Manager, agentless vulnerability scanning, and in-context findings in Cloud Hub, GCE, and GKE dashboards.

🔧 Google Cloud outlines its strategy for building resilient GPU AI/ML infrastructure to support massive-scale training workloads. The post emphasizes measuring reliability beyond simple uptime with MTBI and Goodput, and describes four core principles — proactive prevention, continuous monitoring, transparency and control, and minimizing disruptions — to reduce interruptions and accelerate recovery. It frames infrastructure reliability as a commercial imperative when training at scale.

🔒 Enterprises are moving AI workloads to GKE at scale, but serving models introduces risks such as prompt injection and sensitive data leakage that traditional network controls miss. Google recommends Model Armor, a gateway-integrated guardrail service that inspects requests before they reach the model and scans outputs afterward. It offers proactive input scrutiny, content-aware output moderation, and DLP integration, all without code changes to your application. Integrated logging surfaces policy triggers to Security Command Center for audit and response.

🔁 Google Cloud's Cloud Run worker pools provide an always-on, pull-based execution model that Estée Lauder Companies used to scale LLM-powered services. The company's Rostrum platform migrated from a request-driven service to a producer-consumer architecture: a FastAPI web tier publishes user messages to Pub/Sub and worker pools consume them for LLM inference. This decoupling improved message durability, UI latency SLAs, and reduced operational overhead while enabling GPU-backed distributed workloads and cost improvements for long-running background tasks.

🔒 Google Cloud has been named a Leader in The Forrester Wave™: Sovereign Cloud Platforms, Q2 2026. The company emphasizes a sovereignty-by-design approach across three offerings: Google Cloud Data Boundary with Assured Workloads, Google Cloud Dedicated, and Google Distributed Cloud. Forrester highlighted Google’s roadmap and AI sovereign development capabilities as key differentiators. These options address data residency, operational autonomy, and fully air-gapped deployments for regulated organizations.

🔒 Google Cloud's Cloud NGFW Enterprise now supports domain and SNI-based URL filtering with limited wildcard matching to shift enforcement to the application layer. The URL filtering service inspects HTTP payloads and SNI headers to enable granular egress policies and block malicious domains without requiring full TLS decryption. This reduces the operational burden of tracking dynamic IPs and helps prevent bypass techniques such as SNI spoofing while preserving end-to-end encryption and compliance.

🏠 Rightmove migrated from siloed on-premises databases to Google Cloud to build a unified analytics and AI platform it calls the data hive. Using BigQuery, Vertex AI, and Looker, the company extracts metadata from listings and images to deliver personalized search, agent-assist messaging, and an Automated Valuation Model. The hub-and-spoke architecture centralizes governance while enabling business units to run tailored forecasting and ML use cases. Around 300 staff now use the platform to convert data into operational and commercial value.

🚀 RVU accelerated its personalization platform by adopting Dataproc and Google Cloud Serverless for Apache Spark, using high-speed Spark processing for feature engineering across its consumer brands. The company reduced feature engineering and model development time from weeks to days, enabling faster experimentation and quicker contractor onboarding. This scalable, managed approach co-locates data in BigQuery, simplifies operations, and improves time-to-market for hyper-personalized campaigns.

🔍 Looker now provides self-service Explores that convert CSV, XLS/XLSX, or Google Sheets into instant, governed Explorations using a drag-and-drop or sheet import workflow. Uploaded files are securely persisted in your BigQuery instance and can be re-uploaded or refreshed for ongoing ad-hoc dashboards. Merge queries enable combining uploaded data with modeled Looker datasets (unlimited within the same BigQuery connection) to enrich official metrics, while conversational analytics supports natural-language querying. Admin controls and monitoring keep ad-hoc work distinct from core, governed models to maintain metric integrity.

🗣️ Looker Embedded now offers conversational analytics through the generally available Conversational Analytics API, enabling natural-language querying and AI recommendations inside customer-facing applications. The capability can be deployed via a low-code iframe or integrated SDKs and supports multi-Explore queries, a built-in code interpreter, and customizable theming for private-labeled experiences. Grounded in Looker’s governed semantic layer, it surfaces verifiable SQL to reduce hallucinations and accelerate developer extensibility and data monetization.

🚀 Google Cloud has launched Veo 3.1 Lite, a cost‑effective video generation model available now on Vertex AI, and introduced a new standalone Veo upscaling capability currently in private preview. The Veo 3.1 family now includes three tiers—Veo 3.1, Veo 3.1 Fast, and Veo 3.1 Lite—all with native audio generation. The upscaling tool enhances existing low‑resolution videos to 1080p and 4K, regardless of source, and access is provided via the Vertex AI API and Vertex AI Media Studio. Developer documentation and a sample video editor agent are available to help teams get started.

🔍 This article introduces labs demonstrating how to prepare and use data stored in Google Cloud databases to support production-ready AI. It highlights semantic search using embeddings in AlloyDB and Cloud SQL (PostgreSQL and MySQL), multimodal image–text embeddings, and AlloyDB AI functions like on-the-fly semantic evaluation and reranking. It also covers NL2SQL generation via the alloydb_ai_nl extension and points to hands-on modules for moving from tests to production.

🔒 Unit 42 disclosed a permissions flaw in Vertex AI where the default Per-Project, Per-Product Service Agent (P4SA) can expose credentials and OAuth scopes via the metadata service. Researchers showed attackers could use those credentials to pivot into customer projects, read Google Cloud Storage buckets, and download images from restricted Artifact Registry repositories. Google updated docs and advises using BYOSA and least-privilege scopes; organizations should validate agent permissions before deployment.

🔒 Unit 42 researchers discovered that AI agents deployed with Google Cloud’s Vertex AI ADK can inherit overly broad default permissions, enabling a deployed agent to leak service‑agent credentials and act as a “double agent.” By exploiting the Per‑Project, Per‑Product Service Agent (P4SA), the team pivoted into consumer projects and downloaded restricted Artifact Registry images from Google‑managed producer projects. Google collaborated with Unit 42, updated documentation, and recommended Bring Your Own Service Account (BYOSA) as a mitigation. Palo Alto Networks highlights protection via Prisma AIRS, Cortex Cloud Identity Security, and Cortex AI‑SPM.

🔒 ID.me moved its identity platform to Google Cloud to handle massive scale and improve AI-driven fraud detection. The team migrated over 50 TB across 15 database instances and adopted a two-tier architecture using AlloyDB for heavy workloads and Cloud SQL for standard services. This reduced provisioning and maintenance overhead, sped development from weeks to days, and lowered data-team work time by 40% while enabling real-time fraud analysis.

🔐 At RSAC 2026, Google Cloud leaders outlined a three-stage AI adoption journey—automate tasks, redesign workflows, and rethink functions—and stressed the need for a bilingual workforce fluent in both domain and AI. They warned that AI expands the attack surface across models, agents, and data, urging multi-model, multicloud resilience and identity-centric defenses. Google highlighted the Secure AI Framework, partnerships to counter supply-chain threats like OpenClaw, and agentic SOC innovations, including the acquisition of Wiz and its AI-Application Protection Platform.

🔐 This collection from Google Cloud offers a behind-the-scenes look at how Google approaches modern cybersecurity challenges, from fundamentals to AI. Across practical essays and expert perspectives, it covers modernizing threat detection, building AI agents for defense, red teaming at scale, vulnerability management and supply chain controls like Binary Authorization. The pieces emphasize operational rigor, the application of SRE to security, and a commitment to Secure by Design principles to help defenders adopt scalable, enterprise-ready practices.