August 07, 2026

The AI model Open AI won’t release yet — and what it found in testing

OpenAI is reducing work on Astra after internal testing indicated the upcoming model may have reached a cybersecurity limit that no The post The AI model OpenAI won’t release yet — and what it found in testing appeared first on The New Stack.

Open AI flags its new Astra model as potentially reaching the highest cybersecurity risk level for the first time

Internal tests of OpenAI's new AI model Astra show cybersecurity capabilities so strong that the company can no longer rule out the highest risk level in its own safety framework. Parts of Astra's development have been paused. The move follows recently disclosed incidents in which autonomous AI agents infiltrated OpenAI's own infrastructure undetected for weeks. The article OpenAI flags its new Astra model as potentially reaching the highest cybersecurity risk level for the first time appeared first on The Decoder.

Meta’s new coding agent is cheap (but it’ll cost you your data).

Meta this week announced Muse Code, its first coding agent designed to tackle complex software engineering tasks, like planning changes, The post Meta’s new coding agent is cheap (but it’ll cost you your data). appeared first on The New Stack.

Coinbase, Shopify and Ramp all built their own coding agents. All three still pay Anthropic.

Enterprise engineering teams are converging on a common architecture for AI-assisted software development. Coinbase, Shopify, and Ramp have each built The post Coinbase, Shopify and Ramp all built their own coding agents. All three still pay Anthropic. appeared first on The New Stack.

AMD acquires Taalas, a startup that bakes AI models directly into silicon

AMD is buying Canadian startup Taalas, which hard-codes model weights directly into inference chips. That makes them extremely fast but locks each chip to a single model. A demo chip hit over 16,000 tokens per second per user running Llama 3.1-8B. Google is reportedly working on a similar approach for Gemini. The article AMD acquires Taalas, a startup that bakes AI models directly into silicon appeared first on The Decoder.

Tutor Moments: Do AI tutors know when to help and when to hold back?

Open AI’s expensive smart speaker will use moving parts to seem “more alive”

Gurman report claims OpenAI confirmed the speaker is not an Apple ripoff.

Anthropic loosens Fable 5's biology restrictions but keeps the guardrails on for virology and toxicology

Anthropic has cut false positives in its biology safety filters for Fable 5 by about 85 percent. Previously, nearly all biology-related queries got blocked and rerouted to the less capable Opus 5. The restrictions stay in place for sensitive dual-use topics like virology and toxicology. The article Anthropic loosens Fable 5's biology restrictions but keeps the guardrails on for virology and toxicology appeared first on The Decoder.

Zero-code, low-cost data ingestion: New Big Query DTS capabilities

In a fast-paced digital economy, data is your most critical engine. Yet, many enterprises find themselves trapped in a costly paradox, spending over 100 hours a week building and fixing fragile, in-house ETL pipelines or wrestling with unpredictable third-party tools. Trusted by thousands of customers every single day, BigQuery Data Transfer Service (DTS) eliminates this engineering burden. As a fully managed, zero-code data movement solution, BigQuery DTS automates data ingestion into BigQuery allowing your teams to transition from pipeline maintenance to strategic data science in minutes. Expanding the ecosystem: New connectors and capabilities We are rapidly expanding our integration landscape to eliminate data silos across databases, ads and marketing platforms. Here are the latest additions and enhancements Open Lakehouse ingestion Direct ingestion into Apache Iceberg managed tables (Preview): You can now ingest data from common sources such as Google Cloud Storage, Amazon S3, and Azure Blob Storage directly into Iceberg managed tables. This enables you to maintain full multi-cloud storage cross-compatibility with other query engines while leveraging BigQuery's top-tier performance tuning. Next-gen agentic architecture Fully managed remote Model Context Protocol (MCP) Server (Preview): This allows your developers to connect their AI application and agents to DTS allowing them to programmatically discover data sources and configure/execute transfers on the user's behalf. Enterprise and relational databases (supports both full or incremental transfers) Microsoft SQL Server (Preview): Easily centralize transactional tables, schemas, and operational data directly into your analytical environment in BigQuery. PostgreSQL(GA) and MySQL (GA): Automates data delivery and simplifies the replication of high-volume web and application workloads into your central data warehouse within minutes. Supports data replication from on-premise environments, CloudSQL, and other clouds. E-commerce and growth marketing Shopify(Preview): Automates the extraction of granular order histories, inventory logs, and customer profiles straight into your analytical schema. Klaviyo(Preview): Extracts detailed email and SMS engagement logs (such as clicks, sends, and opens) to build precise multi-channel lifecycle attributes. HubSpot(Preview) : Syncs pipeline, contact tracking, and inbound marketing metrics to keep your revenue operations teams aligned. Mailchimp(Preview): Automatically moves campaign performances and audience list attributes directly into your data warehouse. Migration connectors Snowflake(GA): Migrate your data from Snowflake with features like incremental transfer, auto schema detection, private connectivity and support for migrating data residing on all three major clouds (Google Cloud, AWS, and Azure) Enhancement to major connectors ServiceNow, Salesforce, and Oracle: Enhanced with native incremental update support to speed up large-scale pipeline refreshes for enterprise CRM, ITSM, and financial workflows. Why you should choose BigQuery Data Transfer Service Moving data across an enterprise architecture shouldn't require complex compromises between cost, management overhead, and pipeline health. BigQuery DTS delivers unique advantages across three pillars: 1. Unbeatable cost efficiency Zero Ingestion Costs for major sources: Data ingestion at no-charge for all first-party Google sources (except Google Play), including Google Ads, Google Analytics 4, Campaign Manager, YouTube and Google Cloud Storage. Ingestion cost are also free for Amazon S3, Azure Blob Storage, Amazon Redshift, and Teradata Low consumption-based rates for third-party SaaS: Ingestion from 3rd-party sources are entirely on a flexible consumption model. Compute fees run less than 6 cents per slot-hour in major regions. Because pricing scales with compute footprint rather than row volume, depending on your source data compression format and available network bandwidth, you can efficiently transfer massive data volumes. 2. Frictionless security and native management Eliminate middlemen servers and external security and API configurations. BigQuery DTS fully integrates with Cloud IAM. Data transfers instantly inherit your destination dataset’s Column-Level Security, Row-Level Security, and Customer-Managed Encryption Keys (CMEK) without extra configuration overhead. Your streams flow securely inside the native Google Cloud perimeter. 3. Industry-leading performance and resilience When you manage data at enterprise scale, downtime means lost business. BigQuery DTS provides a highly resilient ingestion footprint backed by a strict Google Cloud Service Level Agreement (SLA). The system delivers a monthly uptime percentage of >=99.99%, guaranteeing your analytics, automated pipelines, and operational dashboards update reliably. Ready to transform your data operations? Stop letting manual ingestion scripts limit your growth. Join the thousands of companies relying on Google's native cloud lakehouse data movement architecture to build a modern, scalable data stack. Try the platform out today: Navigate directly to the BigQuery Data Transfer Service console, pick your connector, and deploy your first automated transfer in just a few clicks! What connectors should we build next? We are constantly expanding our native integration library based on your business needs. What sources are you currently forced to extract manually? Are there specific relational databases, NoSQL engines, or regional SaaS platforms you need to replicate next? Let us know with a feature request. Please file feature requests via the public issue tracker.

Open AI's first smart speaker is expected in 2027 at over $300

OpenAI is planning a donut-shaped smart speaker for 2027, priced above $300. The screenless device has a camera, microphones, and moving parts. It's designed to learn from conversations and adapt to users, fitting Sam Altman's "Her" vision. The article OpenAI's first smart speaker is expected in 2027 at over $300 appeared first on The Decoder.

What’s new with Google Cloud

Want to know the latest from Google Cloud? Find it here in one handy location. Check back regularly for our newest updates, announcements, resources, events, learning opportunities, and more. Tip: Not sure where to find what you’re looking for on the Google Cloud blog? Start here: Google Cloud blog 101: Full list of topics, links, and resources. aside_block <ListValue: []> Aug 3 - Aug 7 Navigate data sovereignty and AI innovation with hybrid cloudFor enterprises facing strict compliance rules, keeping sensitive data on-premises often means missing out on cutting-edge AI. Data from the 2026 State of AI Infrastructure report reveals that 52% of IT leaders are adopting hybrid cloud strategies to bridge this gap. Our latest blog post explores how Google Distributed Cloud (GDC) helps organizations deploy connected or air-gapped models to run advanced AI entirely within secure environments—mitigating geopolitical risks without sacrificing innovation. Read more. SAP and Google Cloud Launch BDC Connect for BigQueryFor years, enterprises have struggled with the cost, risk, and complexity of moving mission-critical SAP data into advanced analytics platforms. The general availability of SAP Business Data Cloud (BDC) Connect for BigQuery marks a turning point. By introducing revolutionary zero-copy, bi-directional data sharing, this new capability seamlessly bridges SAP systems with Google Cloud's powerful data and AI ecosystem. Instead of wrestling with manual data duplication and lost business context, organizations can now eliminate silos, dramatically lower their analytics costs, and rapidly deploy trustworthy, agentic AI solutions grounded in real-time operational reality. Read the full announcement to learn how to transform your data strategy. Google Cloud Cortex Framework version 7 is now generally available!This release helps you modernize your data architecture for AI agent readiness, enabling you to quickly deploy, customize, and extend robust data products while simplifying orchestration and reducing infrastructure overhead. It provides data product accelerators for SAP-sourced data to build trusted, high-quality data products ready for advanced analytics and agentic use cases. The Framework integrates with Google Cloud products including BigQuery, Dataform, Knowledge Catalog, and Gemini Enterprise Agent Platform. Learn more in our announcement blog, technical documentation, or try a demo deployment today. From API Management to AI Gateway with ApigeeMassive LLM adoption unlocked automation but exposed critical vulnerabilities, from unpredictable token costs to security risks like prompt injection. Without central management, organizations face accelerated technical debt. Learn how to transform Apigee into an enterprise AI Gateway to centralize governance. This architectural roadmap details how to utilize semantic cache to optimize token costs, implement prompt protection policies for security, and productize tools using the emerging MCP standard.Read the full architectural roadmap on the Apigee Community Hub Centrally govern enterprise AI traffic with Apigee AI GatewayManage, track, and secure model communication across your entire infrastructure from a single pane of glass. In a new video walkthrough, Principal Architect Tyler Ayers demonstrates how Apigee AI Gateway simplifies agentic governance. Learn how to transparently proxy model traffic, log real-time token counts, and apply runtime security quotas without impacting your developer workflow.Watch the Apigee AI Gateway demo Maximize Provisioned Throughput UtilizationSudden traffic micro-spikes can exceed per-second quotas, triggering 429 errors or forcing overflow into shared resource pools. A new architectural guide demonstrates how to build a serverless "shock absorber" using Cloud Run and Google Cloud Tasks. By decoupling request ingestion from execution, this queue-based pattern flattens volatile traffic bursts and smoothly drips requests to Gemini at your exact quota rate, maximizing Provisioned Throughput utilization while eliminating job failures during peak usage. Read the step-by-step setup guide. Eliminate security blindspots in agentic tool agentic tool calls via the Model Context Protocol (MCP) can introduce critical security risks to your enterprise architecture. Join our technical deep dive on Thursday, August 13, to discover how to position Apigee as a centralized security gateway. Featuring the new ParsePayload policy and payload operations groups in API Products, this session demonstrates how to enforce granular tool filtering, manage execution quotas, and scale secure agent ecosystems without impeding developer velocity. Register for the August 13 Community TechTalk Jul 27 - Jul 31 Data Cloud and Apigee CDMX: The AI Agent Evolution | August 12, 2026Enterprise AI demands evolution beyond basic conversational assistants. To generate real value, AI models must connect with the organization's core systems and live data sources. Join us this August 12 at Google CDMX for the exclusive event AI Evolution: Powering Tomorrow's Enterprise. Learn how to design an agile and secure ecosystem by unifying the power of Gemini, Apigee, and data agent technologies through practical demonstrations led by Google Cloud engineers.Secure your spot for the in-person session in Mexico City Register now! Vast Edge, built on GCP, launches the first live recovery interface for cloud backups, enabling IT teams to inspect backup contents in real time. This transforms backups from a blind, log-based process into an interactive platform where teams can instantly search, preview, and validate the exact data available for restore.This platform protects Google Workspace, NetSuite, Salesforce, Workday and many SaaS environments, providing complete visibility and enterprise-grade oversight.Visit Vast Edge Backup & Disaster Recovery and get a free trial of their backup solutions on the GCP Marketplace for Google Workspace Backup, NetSuite Backup, Salesforce Backup, and Workday Backup. Jul 20 - Jul 24 Claude Opus 5, Anthropic’s latest model, is now available on Agent Platform. It brings performance improvements over Opus 4.8 across coding, long-running agents, and knowledge work.The model is Zero Data Retention (ZDR) compatible. For safety, high-risk workflows — such as penetration testing or exploit generation — it will notify you and fall back to Opus 4.8.We’re excited to continue to offer enterprise customers options across frontier models to build, deploy, and scale AI securely. Try it here. Apigee Northam Roadshow 2026 | The AI Agent Evolution: Powering Tomorrow's EnterpriseAI is evolving. As your organization deploys autonomous agents, the integration between APIs and models becomes critical. Join Google Cloud specialists for an exclusive day of deep-dive sessions and live demos. Discover how the unified power of Apigee and the Google Cloud Agent Platform allows you to build, govern, and scale high-performance AI agents with complete control. Call to Action: Register for Sunnyvale | Register for NYC | Register for Chicago Deploy an Apigee Proxy for MCP Registry Discovery Learn how to deploy an Apigee X proxy to format Apigee API Hub data into the Model Context Protocol (MCP) Registry format. This tutorial by Tyler Ayers guides developers through cloning the sample repository, deploying using the Apigee Feature Templater (aft), and testing the endpoint to make API data easily discoverable by coding agents. Read the full community tutorial to get started. Simplify AI Infrastructure: Getting Started with Apigee AI GatewayManaging a complex AI landscape with multiple backend environments can present significant operational and governance challenges. A new tutorial walks you through how to build a unified API proxy using Apigee AI Gateway. By establishing a single, secure entry point for all model traffic, teams gain access to real-time analytics, comprehensive tracing, and financial operations auditing—completely seamlessly, and with absolutely no modifications required to client environments or user configurations. Read the step-by-step setup guide Your AI agents are ready. Is your data?The biggest bottleneck to scaling AI isn't the models—it's giving them access to business context. As enterprises move to proactive systems of action, legacy infrastructure often buckles under the nonlinear speed of AI agents. Google Cloud’s new Agentic Data Cloud, built on AI-native infrastructure, solves this by unifying data, AI models, and operational databases. Discover how a borderless Lakehouse and active Knowledge Catalog can empower your AI agents with trusted, real-time context without unnecessary engineering overhead. Read more. Secure and govern your AI at Apigee AI Horizon in LondonMoving AI from basic prompts to complex agentic workflows requires trust and control. Join us on Tuesday, 1st September 2026 at Google London for our 5th edition of Apigee AI Horizon. Discover how Google Cloud product leaders and architects are using Apigee and Model Armor to secure LLM APIs, implement policy controls, and manage token consumption. Do not miss this one—register soon!Secure your spot for AI Horizon London Jul 13 - Jul 17 Resource-Based CUD Sharing is Now Enabled by DefaultStarting June 16, 2026, the default setting for Google Cloud Resource-based Committed Use Discount (CUD) sharing will change from disabled to enabled for new billing accounts and eligible existing accounts without active CUDs. This update automatically maximizes your savings by pooling underutilized discounts across your resources.You retain full control and can adjust your CUD sharing preferences at any time by changing your CUD scope configuration. For instructions, see Enable CUD sharing or Disable CUD sharing. Webinar for India: Google Cloud for EdTech: Optimizing Traffic and Token Governance at ScaleAPI traffic surges and AI model integration are reshaping the EdTech landscape. Join Satyam Maloo for the webinar Google Cloud for EdTech: Optimizing Traffic and Token Governance at Scale on July 23, 2026. Learn to implement advanced rate limiting, gain granular token visibility, and leverage real-time analytics to govern your platform effectively. Whether you’re scaling for peak academic seasons or integrating complex AI workflows, this session provides the infrastructure blueprint you need.Register Now Scaling AI Agents: Treat prompts like software artifactsAs AI agents move into production, monolithic system prompts often result in configuration drift, merge conflicts, and silent runtime failures. The solution is adopting a Prompts-as-Code architecture. By breaking prompts into modular skill files and using a build-time transpiler, engineering teams can introduce dependency resolution, static validation, and CI/CD rigor to their agent's control plane. Stop manually editing massive text files and start building deterministic, reliable agent infrastructure.Read more here. Jul 6 - Jul 10 Webinar: Introducing Google Cloud NGFW Enterprise advanced malware protection - powered by Palo Alto NetworksDiscover the new Cloud NGFW advanced malware sandbox, arriving in preview later this year. Powered by Palo Alto Networks Advanced Wildfire, it leverages data from 70,000+ customers to help defeat advanced malware. Join us on July 16 at 11 AM EDT to learn how to build a resilient, zero-trust cloud infrastructure that protects your apps and data, wherever they reside.Register for the webinar now Safely run AI-generated code in Cloud Run sandboxesCloud Run sandboxes, now in public preview, are lightweight, isolated execution boundaries that you can spawn near-instantly within your existing Cloud Run service instances.Whether you need to let an LLM run a dynamically generated Python script to calculate business margins or spin up a headless browser to perform web research, Cloud Run sandboxes give you a secure, isolated sandbox to run these tasks without leaving your serverless environment.Read the blog to learn more and get started today. Australia API Horizon: Scaling Enterprise Governed AI AgentsThe transition from AI chatbots to autonomous agents is the most critical integration point for your business. Join Google Cloud at our upcoming events to explore exclusive deep-dive sessions on architecting for the agentic era.Discover how to use Apigee as an intelligent AI Gateway to govern, secure, and scale high-performance architectures. You will learn to seamlessly build AI tools from your existing APIs and maintain control over your entire ecosystem.Join us in your preferred city: Sydney: July 28, 2026, at Google Sydney, One Darling Island. Canberra: July 29, 2026, at Hotel Realm. Melbourne: August 4, 2026, at Google Melbourne. Build highly available, multi-region services on Cloud RunMaintaining uptime for business-critical applications just got a lot easier on Cloud Run. Service health, now Generally Available, automates cross-region failover by leveraging readiness probes for instance-level health checks with a simple, two-click setup. You can configure service health with global external Application Load Balancers for public-facing applications or cross-region internal Application Load Balancers for private networking traffic.Learn how to configure service health for Cloud Run. Report: 83% of organizations need infrastructure upgrades for agentic AIThe shift from conversational bots to autonomous agents is breaking legacy systems. Our new State of AI Infrastructure report details how engineering leaders are adapting to these massive new workloads. To eliminate inference bottlenecks, control hidden scaling costs, and manage agent sprawl, the industry is rapidly moving toward fluid compute, centralized governance, and unified, co-designed architectures.Explore our key infrastructure insights Stop tinkering, start scaling: the industrialized AI PlaybookDid you know that only 5% of custom AI investments actually return measurable business value? The problem isn’t the technology—it’s how organizations are wired to run it.In this compelling read, Google Cloud Consulting breaks down the operational blueprint that bridges the stark gap between "cool tech experiments" and real, P&L-impacting enterprise ROI.Read the full article on Medium AI Agent Clinic: Slashing App Latency by 80%Prototyping an AI agent is easy, but scaling for live traffic presents unique challenges. In the latest AI Agent Clinic, our technical experts partner with a developer to optimize PlaybackIQ, a live football analysis agent. This session demonstrates how to use OpenTelemetry to trace bottlenecks in the Gemini Enterprise Agent Platform and deploy to Cloud Run for high-concurrency scaling, achieving an 80% reduction in response time. Learn production-grade debugging strategies to optimize your own LLM applications.Watch the 60-minute teardown Jun 29 - Jul 3 Claude Sonnet 5, Anthropic’s latest model, is now available on Agent Platform. This addition serves as a drop-in replacement for Sonnet 4.6, giving organizations expanded choice for task completion across enterprise workflows. It features enhanced reasoning, cleaner code generation, and computer use capabilities for desktop and browser workflows.By continuing to rapidly bring frontier models to our platform, Google Cloud offers an uncompromised choice of the industry's best technology to build, test, and scale enterprise-grade AI.Get started today. Automate your AI governance with Apigee and YAMLManual API gateway configurations can quickly slow down your AI engineering velocity. Join the Apigee community on Thursday, July 16, to discover an automated, declarative blueprint for model garden management. Learn how a simple, repeatable YAML pattern lets your AI practitioners instantly spin up secure, policy-backed enterprise configurations without friction. Bring your questions and connect during our live Q&A session. Register for the July 16 Community TechTalk Build next-generation AI portals for autonomous agentsStandard developer portals were designed for human developers to subscribe to static APIs. Today, autonomous agents, LLM toolkits, and dynamic runtimes demand a central nervous system for governance. Join our technical deep dive on Thursday, July 23, to explore Apigee's new AI Portals solution. You will see exactly how to deploy full-service, MCP powered hubs to safely manage enterprise self-service for models, tools, and agents. Register for the July 23 Community TechTalk Protect your infrastructure from advanced cyberattacks at the API layer (Presented in Portuguese)In an era of increasingly sophisticated threats, relying solely on traditional firewalls leaves critical data gaps. Join our technical community TechTalk on Thursday, July 30—conducted in Portuguese—to learn how to proactively mitigate risks directly at the gateway layer. This session demonstrates how to configure and govern essential Apigee security policies to build a robust line of defense, ensuring maximum availability and complete integrity for your enterprise microservices. Register for the July 30 Portuguese Community TechTalk Jun 22 - Jun 26 Accelerate TPU model loading while saving RAM on GKE.Large model cold starts often stall scaling and leave high-value TPUs idle. The open-source Run:ai Model Streamer now natively supports TPUs with Google Cloud Storage in TPU vLLM 0.18.0. This integration accelerates inference pipelines on GKE by streaming tensors directly into CPU memory, bypassing local disk bottlenecks and the "double-buffering" trap. In benchmarks, loading a 480B parameter model was over 2x faster while cutting peak host memory usage by half. Read the full guide and get started today. Stop Training Blind: Scaling AI with the New OpenTelemetry-Based TPU AI Telemetry Collector AgentGoogle Cloud’s new AI Telemetry Collector agent standardizes TPU monitoring using OpenTelemetry. It optimizes enterprise ML workloads by identifying silent failures and providing zero-cost operational metrics without draining host CPU cycles. The agent seamlessly routes telemetry to Google Cloud Monitoring or Prometheus and custom Grafana setups. Pre-installed on Google-optimized Ubuntu images or available via Docker, it tracks memory, network latency, and core utilization to maximize multi-node training efficiency.You can read more of this capability by clicking this link. Jun 15 - Jun 19 Join us for a deep dive into agentic AI control with AppyThingsYour integrations aren’t failing—they are evolving. When users interact with AI agents, they no longer arrive directly at your site, resulting in experiences stripped of your context, expertise, and intended experience. Join us on Thursday, June 25, for a community tech talk in partnership with AppyThings to learn how to solve this new gateway challenge. We will explore how MTN laid an integration foundation with the Model Context Protocol (MCP) to deliver accurate, consistent experiences. Our technical experts will demonstrate how to leverage Apigee as a centralized tools management solution to govern agent access. Register for the session Optimize Spot VM Deployments with Capacity Advisor for Spot, Now in Public PreviewGoogle Compute Engine has launched Capacity Advisor for Spot to Public Preview, now open to all customers. This tool turns Spot capacity discovery into a data-driven process by providing real-time deployment recommendations to maximize obtainability and minimize preemption risks. Query the Capacity Advisor API for obtainability and minimum estimated uptimes, or use the new Console UI featuring a global availability map, spot price lookups, and historical preemption rate trends to visually find the most cost-efficient compute capacity.Get started today to start optimizing your Spot VM deployments! Build a multi-tenant agentic AI systemWhen scaling generative AI across different business units, your teams need specialized AI agents with unique operational rules and tools. Our new reference architecture helps you build a centralized multi-tenant platform to prevent fragmented silos, eliminate data exposure risks, and maintain unified compliance. Read the guide to design and deploy a multi-tenant agentic AI system in Google Cloud. How to Configure Gemini Enterprise to Connect to a Custom MCP ServerThe Gemini Enterprise MCP Connector was a big announcement at Google Cloud Next because it introduces the ability to connect Gemini Enterprise to MCP servers. This blog post provides a step-by-step guide on how to configure your first Custom MCP Server connector using the Google Maps Ground Lite MCP server as an example. Once you understand this flow, you can configure multiple MCP servers with Gemini Enterprise to bring all the context you need. Jun 8 - Jun 12 Simplify Multi-Cloud Planning with Cloud Location Finder, now Generally Available Cloud Location Finder provides up-to-date data on public regions, zones, and Google Distributed Cloud Connected locations across Google Cloud, AWS, Azure, and OCI. You can now programmatically discover locations based on provider, proximity, territory, and carbon footprint to optimize your global infrastructure strategy for performance, compliance, and sustainability. Get started for free today Jun 1 - Jun 5 Modeling the physical world with BigQuery GraphManaging complex supply chains requires more than just spreadsheets; it requires a digital replica of the physical world. In this post, Guru Rangavittal and Candice Chen explore how BigQuery Graph enables organizations to build a digital twin by turning physical assets into an interconnected map of nodes and edges. By moving beyond traditional relational databases, businesses gain real-time clarity into operations—from executing surgical ingredient recalls to analyzing weather-driven logistics risks. Discover how BigQuery Graph transforms reactive firefighting into proactive, precision modeling, allowing you to see critical connections in seconds and future-proof your supply chain. Apigee for AI: Govern LLMs and MCP Servers (Presented in Spanish)Learn how to securely transition your AI initiatives from experimental prototypes to enterprise-ready deployments. Join Luis Cuellar on June 18 for a technical deep dive (presented in Spanish) exploring Apigee’s latest AI gateway capabilities. Discover how to centralize governance over Model Context Protocol (MCP) servers, protect Large Language Models (LLMs) with robust API gateway security policies, and manage token-based quotas.Register for the June 18 Spanish Community TechTalk May 25 - May 29 Anthropic’s Claude Opus 4.8 is now available on Gemini Enterprise Agent Platform. As we continue to expand our platform's model offerings, this addition gives organizations more options for handling complex, multi-stage enterprise workflows. Claude Opus 4.8 brings strong capabilities in agentic coding, allowing developers to manage extensive refactors and tracking dependencies over extended sessions. API Horizon Munich July 6, 2026: Orchestrating the Next Era of AI and APIs Master the orchestration of next-gen AI and digital ecosystems. Join Google Cloud experts and DACH tech leaders on July 6 for an exclusive look at the Apigee roadmap, Agent Management, and Model Context Protocol (MCP). Gain real-world insights and connect with the regional integration community.Register now Securing AI Agents: The Extended Agent Gateway PatternLearn how to prevent autonomous AI agents from invoking unauthorized APIs. Join Apigee Specialist Joel Gauci on June 4 for a technical deep dive into the Extended Agent Gateway pattern. This session covers enforcing Fine-Grained Authorization (FGA), implementing secure token exchange, and establishing Model Context Protocol (MCP) governance at the API gateway layer to protect enterprise backend services.Register for the June 4 Community TechTalk API-to-Agent Security: Exposing REST APIs to Gemini Enterprise via MCPConnect Gemini Enterprise agents to core data without creating security hazards. Join Google Cloud Specialist Nigel Walters on June 11 to learn how to instantly transform legacy REST APIs into secure Model Context Protocol (MCP) servers. We’ll cover how to safely register tools with Gemini while enforcing gateway-level guardrails like rate limiting and access control policies.Register for the June 11 Community TechTalk May 18 - May 22 Chinese Webinar | June 4: AI Command and ControlAs AI agents move from experimental pilots to core enterprise functions, governance has become a critical next step. Join Google Cloud on June 4th at 10:00 AM (Beijing Time) to learn how to build a secure AI management layer architecture. We'll explore how to develop governed MCP (Model Context Protocol) endpoints, manage tool access to enterprise data, and leverage robust audit logs to operationalize AI. This session also includes a practical demonstration of these governance frameworks on Google Cloud.Register here GCP Announces New Features to Benchmark and Optimize LLMs for On-Device Use CasesDeploying fine-tuned LLMs from GCP to edge devices like smartphones is complex due to fragmented hardware. Google AI Edge Portal bridges this gap, giving GCP developers the ability to test AI performance on 120+ Android devices, representing the full diversity of high, medium, and low tier smartphones on the market today. This week at I/O, we announced brand new capabilities to benchmark and debug LLM performance across these devices. Sign-up to utilize these new features in private preview today. May 11 - May 15 Build Your AI & MCP Control Tower for Universal GovernanceMaster the future of agentic security with Apigee. Join our Community TechTalk on May 21 to discover how Apigee serves as a central "Control Tower" for the Model Context Protocol (MCP). We will explore how new JSON-RPC tool authorization enables fine-grained access policies across your organization, ensuring secure and scalable AI deployments. Whether managing internal tools or external users, learn to govern your agentic ecosystem with absolute precision. This session is designed for global coverage across EMEA and AMER regions.Register for the May 21 Community TechTalk Apr 27 - May 1 Master Your Launch: The Apigee Production Go-Live ChecklistEnsure a secure launch with the Apigee production guide. Join Nicola Cardace on May 28 to explore security guardrails, including IAM roles, mTLS configurations, and encrypted KVM migrations. Scheduled at 11 AM EDT / 5 PM CEST to support EMEA and AMER teams, this TechTalk provides the technical roadmap you need to flip the switch with absolute confidence.Register for the May 28 Community TechTalk Transforming APIs into Governed Agentic Tools on the Google Cloud Agentic PlatformTurn your APIs into secure, governed agentic tools on the Google Cloud Agentic Platform. Join Specialist Christophe Lalevée on May 7 for a technical deep dive into AI productization. Scheduled at 5 PM CEST / 11 AM EDT to maximize coverage for developers across EMEA and AMER, this session explores the integration and governance frameworks required to scale enterprise-ready AI with confidence. Register for the May 7 Community TechTalk Fractional G4 VMs are Generaly Available, providing a highly efficient and cost-effective entry point for AI and graphics workloads. These new configurations, using NVIDIA virtual GPU (vGPU) technology, allow you to leverage the power of the NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs in flexible, smaller increments, so you can right-size your infrastructure to match the specific demands of your applications. By providing more granular access to advanced hardware, fractional G4 VMs let you optimize resource allocation and reduce overhead without sacrificing performance. You can now select from additional GPU slice sizes for your specific needs: 1/2 GPU: Ideal for more intensive tasks such as LLM inference, robotics sensor simulation, and high-fidelity 3D rendering. 1/4 GPU: Optimized for mainstream workloads, including mid-range creative design, video transcoding, and real-time data visualization. 1/8 GPU: Great for lightweight applications such as remote desktops, productivity tools, and entry-level streaming services. Transitioning AI from a sandbox prototype to an enterprise-grade system is a major hurdle. A monolithic script won't suffice for widespread deployment. To achieve true scale and reliability with Gemini, organizations must adopt service-oriented micro-agent architectures, establish Zero-Trust security, and implement rigorous EvalOps. Master the "Agentic Maturity Ladder" to ensure your AI & Agentic solutions are robust, secure, and ready for the real world. Watch the deep dive and read the developer blog to learn more. ML Development in VS Code with Google Cloud Power: Workbench Extension Now AvailableData scientists and developers can now combine the local productivity of VS Code with the scalable infrastructure of Google Cloud. The new Google Cloud Workbench Notebooks extension allows you to connect to and run notebooks on managed cloud environments directly within your local IDE. This integration streamlines the ML lifecycle by eliminating context switching and providing high-performance compute for complex workloads in a familiar interface. As part of our commitment to the developer ecosystem, the extension is fully open-sourced to support community-driven innovation. Install from Marketplace: GoogleCloudTools.workbench-notebooks Contribute on GitHub: colab-enterprise-vscode Apr 20 - Apr 24 Announcing the 2026 Google Cloud Partners of the YearGoogle Cloud is honored to celebrate the winners of the 2026 Partner of the Year awards! These awards recognize an exceptional group of partners across AI, Security, Infrastructure, and more, who have demonstrated a commitment to customer success. From global system integrators to specialized startups, these winners are leveraging the power of Google Cloud to solve complex challenges and drive digital transformation worldwide. Join us in congratulating these organizations for their innovation, collaboration, and impactful results over the past year.See the 2026 Partner Award winners Apr 13 - Apr 17 We're excited to announce the Public Preview of Datastream’s metadata integration with Knowledge Catalog. This is the first step in our vision to provide a centralized, "single pane of glass" for all Datastream assets. The enhancement automatically synchronizes Streams, Connection Profiles, and Private Connections, eliminating data silos. It enhances discoverability, allowing you to search for Datastream assets using the same interface as BigQuery tables. Centralized governance is also provided, making your real-time data estate more transparent and easier to manage. Upgrading Apigee OPDK to 4.53 with OS your infrastructure using Google’s official, sequential upgrade path. Our Technical expert, Rakesh Talanki outlines how to upgrade Apigee OPDK to v4.53 while migrating to a supported OS (RHEL 8.x/9.x). This guide covers the "build-out" methodology, including multi-data center syncing, to ensure a stable, zero-downtime transitionRead the guide Cloud Run Worker Pools and CREMA: Powering Serverless AI at ScaleGoogle Cloud has announced the General Availability of Cloud Run worker pools, a new resource type designed specifically for pull-based, non-HTTP workloads. Unlike traditional Cloud Run services that scale based on request traffic, worker pools provide an "always-on" environment for background tasks like processing message queues or running large-scale AI inference. To support this, Google Cloud also open-sourced the Cloud Run External Metrics Autoscaler (CREMA). Built on KEDA, CREMA enables queue-aware autoscaling for worker pools, allowing them to dynamically scale based on external signals like Pub/Sub backlog or Kafka lag. Apigee Model Context Protocol (MCP) now Generally AvailableExpose enterprise APIs as MCP tools for agentic AI applications with the General Availability of MCP in Apigee. This update allows developers to transform APIs into AI-ready tools using OpenAPI Specifications, removing the need for local MCP servers or additional infrastructure. With managed endpoints and semantic search in API hub, you can now provide AI agents with secure, governed access to enterprise data at scale.Explore the MCP overview Apr 6 - Apr 10 Community TechTalk: Powering Retail Agents with ADK, UCP & Apigee XMove beyond basic chatbots to secure, transactional AI experiences. Join our Community TechTalk on April 16 to learn how Apigee X and Gemini build a "Trust Layer" for AI shopping assistants using UCP standards. We’ll demonstrate how to block prompt injections with Model Armor and implement cost governance via token limits to secure the path from discovery to purchase.Register for the TechTalk Implement multimodal capabilities in your AI agentsExplore three new reference architectures for building sophisticated multi-agent AI systems that can process and analyze multimodal data. To analyze disparate multimodal data and produce a high-confidence classification, see Classify multimodal data. To create a fluid conversational AI that processes audio and video streams in real time, see Enable live bidirectional multimodal streaming. To consolidate fragmented multimodal data into a searchable knowledge graph, see Multimodal GraphRAG resource orchestration. Automate SecOps workflows with an agentic AI systemTo accelerate incident response and reduce manual toil for your security team, you need a system that can automate remediation playbooks. Our new reference architecture helps you build an AI agent that orchestrates complex triage and investigation workflows across disparate security tools, such as SIEM, CSPM, and EDR, from a single interface. See the full guide to orchestrate security operations workflows. Mar 30 - Apr 3 ASEAN Webinar | April 30: Mastering Agentic Governance at Scale with GCPAs AI agents move from experimental pilots to core enterprise functions, governance is the critical next step. Join Google Cloud experts Shilpi Puri & Wely Lau for a webinar on April 30th at 11:00 AM SGT to learn how to architect a secure AI Management layer. We’ll explore developing governed MCP endpoints, managing tool access to enterprise data, and operationalizing AI with robust audit logs. The session includes a live demo of these frameworks in action on Google Cloud.RSVP here. Mar 23 - Mar 27 Turn your API sprawl into an agent-ready catalogAs organizations scale, APIs often become scattered across multiple gateways, creating "blind spots" that hinder AI adoption. To solve this, we’ve introduced two new capabilities for Apigee API hub: a new integration with API Gateway to automatically centralize API metadata into a single control plane, and a specification boost add-on (now in public preview). This add-on uses AI to enhance your API documentation with the precise examples and error codes that AI agents need to function reliably.Read the full blog post to get started. Webinar | April 16: AI Command & ControlAs AI agents move from experimental pilots to core enterprise functions, governance is the critical next step. Join Google Cloud expert Satyam Maloo for a webinar on April 16th at 11:00 AM IST to learn how to architect a secure AI Management layer. We’ll explore developing governed MCP endpoints, managing tool access to enterprise data, and operationalizing AI with robust audit logs. The session includes a live demo of these frameworks in action on Google Cloud.RSVP here. Modernizing and Decoupling Event Ingestion with ApigeeIn modern cloud-native architectures, decoupling producers from consumers is critical for building resilient systems. While Google Cloud Pub/Sub provides a scalable backbone, exposing it directly to external clients can introduce security and management overhead. This new guide explores how to leverage Apigee as an intelligent HTTP ingestion point. Learn how to handle security, mediation, and traffic control before messages reach your internal bus using the PublishMessage policy or Pub/Sub API.Read the full guide. Mar 16 - Mar 20 Gemini-powered Assistant in BigQuery Studio Gets Context-Aware UpgradesThe Gemini-powered assistant in BigQuery Studio has been transformed into a fully context-aware analytics partner, supporting your entire data lifecycle. The new capabilities include intelligent resource discovery, which uses Dataplex Universal Catalog search to find resources across projects and deep dive into metadata using natural language. You can now automate tasks, such as scheduling production-grade queries directly through the chat interface, and instantly troubleshoot long-running or failed jobs with root cause analysis and cost control auditing.Explore the full range of what the assistant can do. Mar 9 - Mar 13 Want to use Gemini to develop code and don't know where to start?This article includes a couple of examples of developing code with Gemini prompts; it identified changes that were needed to be made to get the code working. The article also refers to other examples that are available on github. Mar 2 - Mar 6 Introducing Gemini 3.1 Flash-Lite, our fastest and most cost-efficient Gemini 3 series model. Built for high-volume developer workloads at scale, 3.1 Flash-Lite delivers high quality for its price and model tier. Gemini 3.1 Flash-Lite can tackle tasks at scale, like high-volume translation and content moderation, where cost is a priority. And it can also handle more complex workloads where more in-depth reasoning is needed, like generating user interfaces and dashboards, creating simulations or following instructions. Starting today, 3.1 Flash-Lite is rolling out in preview to enterprises via Vertex AI and developers via the Gemini API in Google AI Studio. TechTalk: Implementing Device Authorization Grant (RFC 8628) for ApigeeLearn how to authorize "headless" devices like Smart TVs or AI agents that lack keyboards and browsers. Join our Community TechTalk on March 19 (5PM CET / 12PM EDT) to go under the hood of Apigee X/Hybrid. We’ll cover the real-world mechanics of state management, polling, and human-in-the-loop security patterns for devices and autonomous agents. Register for the TechTalk Feb 23 - Feb 27 Pro-level image generation gets faster and more accessible with Nano Banana 2Nano Banana 2 is our state-of-the-art image generation and editing model. It delivers Pro-level image generation and editing at the speed you expect from Flash — making the quality, reasoning, and world knowledge you loved about Nano Banana Pro more accessible. Learn more about the model here. The Intelligent Path to Compliance: Transforming Regulatory QC with Google CloudReducing "Refuse to File" (RTF) risks and submission cycle times is critical for life sciences leaders. Google Cloud’s Regulatory Submission Semantic QC Auditor leverages Gemini and RAG architecture to transform Quality Control from a manual burden into an active, intelligent workflow. By automating semantic cross-referencing, narrative coherence checks, and dynamic guidance-based auditing, this solution ensures rigorous accuracy and auditability. Operating within a secure GxP-ready environment, it empowers teams to detect subtle inconsistencies and generate remediation plans without sacrificing data privacy. Learn more. Stop typing, start interacting! The Gemini Live Agent Challenge is here. Build immersive agents that can help you see, hear, and speak using Gemini and Google Cloud. Compete for your share of $80,000+ in prizes and a trip to Google Cloud Next '26!Submissions are open from February 16, 2026 to March 16, 2026. Learn more and register at .devpost.com Feb 9 - Feb 13 Introducing Gemini 3.1 Pro on Google Cloud. 3.1 Pro is a noticeably smarter, more capable baseline for complex problem-solving. We’re shipping 3.1 Pro at scale, building upon our goal to help you transform your business for the agentic future. Learn more about the model’s capabilities here. Gemini 3.1 Pro is available starting today in preview in Vertex AI and Gemini Enterprise. Developers can access the model in preview via the Gemini API in Google AI Studio, Android Studio, Google Antigravity, and Gemini CLI. Automate Storage Compatibility with GKE Dynamic Default Storage ClassesManaging storage across mixed-generation VM clusters in GKE just got easier. With the new Dynamic Default Storage Class, Google Kubernetes Engine automatically selects between Persistent Disk (PD) and Hyperdisk based on a node's specific hardware compatibility. This abstraction eliminates the need for complex scheduling rules and manual pairing, ensuring your volumes "just work" regardless of the underlying infrastructure. By defining both variants in a single class, you reduce operational overhead while maintaining peak performance and cost-efficiency across your entire cluster.Explore automated disk type selection Community TechTalk: AI-Powered Apigee Development with strofa.ioJoin the Apigee community on February 26 for a deep dive into strofa.io. Guest speaker Denis Kalitviansky will demonstrate how this new AI-powered tool automates and orchestrates Apigee development, from local emulators to large-scale hybrid environments. Discover how to scale your API management and streamline team collaboration using the latest in AI-driven automation. Register now to reserve your spot. Jan 26 - Jan 30 Simplify API Governance with Native OpenAPI v3 SupportEliminate integration debt and accelerate deployment velocity with the General Availability of OpenAPI v3 (OASv3) support for API Gateway and Cloud Endpoints. You no longer need to downgrade modern specifications to OASv2. Instead, you can now define API contracts and enforce critical policies—including telemetry, quotas, and security—using native Google-specific extensions directly within your OASv3 files. This update ensures your APIs are secure by design while remaining fully compatible with the modern developer ecosystem and Google Cloud’s AI services.Get started with OpenAPI v3 on API Gateway and Cloud Endpoints. Accelerate API Testing with the New Open Source API TesterStart validating your APIs with API Tester, a simple, YAML-based Test Driven Development (TDD) framework. Designed for the Apigee community, this tool allows you to write human-readable tests, run them instantly via a web client or CLI, and perform deep unit testing on Apigee proxies. With native support for JSONPath assertions and Apigee shared flows, you can verify everything from payload data to internal variables like proxy.basepath without leaving your terminal.Explore the API Tester guide and start testing your proxies today. Secure Sensitive Data with Kubernetes Secrets in Apigee hybridEnhance security in Apigee hybrid by accessing Kubernetes Secrets directly within your API proxies. This hybrid-exclusive feature keeps sensitive credentials within your cluster boundary and prevents replication to the management plane. It supports strict separation of duties: operators manage secrets via kubectl, while developers reference them as secure flow variables—ideal for high-compliance and GitOps workflows.Implement Kubernetes Secrets in your hybrid proxies. See the Console in a Whole New Light: Dark Mode is Now Generally Available in Google CloudElevate your cloud management workflow with Dark Mode, now generally available in the Google Cloud console. We have delivered a modern, cohesive, and accessible experience reimagined for maximum comfort and productivity—especially during extended working hours and low-light environments. Dark Mode can be enabled automatically based on your operating system's preference, or manually through the Settings -> Appearance menu.Switch to Dark Mode today to enjoy a modern, comfortable, and productive environment! Apigee X Networking: PSC or VPC Peering?Deciding how to connect Apigee X? Watch this video to compare Private Service Connect and VPC Peering. We break down northbound and southbound routing, IP consumption, and how to reach targets on-prem or in the cloud. Learn to simplify your architecture and avoid common networking "gotchas" for a smoother deployment.Watch the video. Jan 19 - Jan 23 Bridge the Gap: Excel-to-API Conversion in Apigee PortalsGive your customers more ways to connect! This new article by Tyler Ayers explores how to extend the Apigee Integrated Portal to support direct Excel file uploads. By leveraging SheetJS and custom portal scripts, you can enable users to upload spreadsheets, preview data, and submit it directly to your APIs, all without writing a single line of integration code themselves. It’s a powerful way to simplify onboarding for those who aren't yet API-ready.Learn how to build it. Elevate your applications with Firestore’s new advanced query engineWe have fundamentally reimagined Firestore with pipeline operations for Enterprise edition. Experience a powerful new engine featuring over a hundred new query features, index-less queries, new index types, and observability tooling to improve query performance. Seamlessly migrate using built-in tools and leverage Firestore’s existing differentiated serverless foundation, virtually unlimited scale, and industry-leading SLA. Join a community of 600K developers to craft expressive applications that maximize the benefits of rich queryability, real-time listen queries, robust offline caching, and cutting-edge AI-assistive coding integrations.Learn more about Firestore pipeline operations.

How Google Cloud detects, contains, and protects against emerging threats

At Google Cloud, securing your data and business systems is our foundational commitment. We empower our customers with the tools, governance, and infrastructure needed to securely deploy workloads and maintain long-term trust. We approach security from a shared fate model, and we continuously work to proactively identify and mitigate potential threats before they can compromise your data and misuse your infrastructure. Understanding the risk: How bad actors attempt to exploit cloud workloads Hyperscale cloud platforms like Google Cloud offer massive compute capacity, high-speed networking, and cutting-edge AI engines, but these same core strengths also make us high-value targets for malicious actors seeking service disruption, financial gain, or exploit cloud resources. By tracking active adversary techniques, Google’s specialist security teams actively monitor and defend across several areas. AI workload exploitation: As organizations rapidly adopt AI tools and systems, including Gemini Enterprise Agent Platform, threat actors target unsecured API keys and leaked access tokens. Common attack patterns include using stolen credentials for unauthorized distillation attacks and reselling access tokens on third-party marketplaces. We track consumption rates, account standing, and access context to catch these anomalies early. Cryptocurrency mining: Malicious actors often use stolen credentials to spin up virtual machines (VM) for illicit cryptomining. While Google Cloud respects customer privacy and does not inspect internal VM processes, we can accurately infer mining activity by analyzing infrastructure telemetry — such as distinctive CPU and memory utilization spikes and rapid VM creation rates. Exfiltrated credentials and supply chain attacks: Developers occasionally commit secrets and API keys to public source repositories where automated scrapers harvest them in seconds. Exposed credentials also stem from supply chain attacks against local development environments or managed cloud workloads. Account takeover (ATO): Adversary-in-the-middle (AITM) techniques — such as sophisticated phishing and session cookie theft — can grant unauthorized users administrative control. Once inside, adversaries establish persistence, move laterally, and execute downstream abuse like resource hijacking or data exfiltration. Without proper containment, these attacks can lead to operational disruptions, compromised system integrity, and unchecked resource misuse — such as runaway costs — creating substantial friction for impacted users. Mitigating risks: Tailored containment in action Detecting a threat is only half the battle; maintaining business continuity by containing it without interrupting your legitimate operations is critical. Google Cloud deploys tailored mitigation strategies based on the nature of the threat. Granular containment and throttling: When anomalous traffic indicates AI abuse or cryptomining, we apply targeted throttling measures. This isolates malicious activity while preserving legitimate corporate traffic. Collaborative triage for complex workloads: In AI environments, malicious API calls are often interlaced with critical business operations. In these scenarios, our Cloud Abuse and Cloud Support teams collaborate directly to isolate and inspect specific traffic vectors. Localized identity isolation: To prevent lateral movement, localized containment protocols can be systematically applied across compromised user identities and Google Workspace domains. Targeted suspensions as a last resort: Our primary objective is to enforce containment at the most granular resource level possible. However, if platform integrity or customer financial exposure is severely threatened, we may temporarily suspend specific projects, backed by a clear appeal process. Additionally, to stop attacks at the root, Google actively partners with public repository hosters through initiatives like GitHub Secret Scanning to catch exposed credentials immediately and trigger proactive warnings before exploitation occurs. Communicating risk: Proactive transparency and log visibility During a security event, time-to-awareness is everything. We provide a robust suite of tools and channels to ensure your key security stakeholders receives actionable visibility: Cloud Abuse Event Logging: Provides a 30-day window into security and abuse notifications with resource-level granularity. These logs can be ingested directly into your SIEM product for automated orchestration and response. Proactive support cases and abuse notifications: When critical abuse is detected, automated email notifications and high-touch support cases are generated to open an immediate channel for resolution and best-practice sharing. Cloud Audit Logging and anomaly spending alerts: Audit logs monitor unexpected resource changes that point to an ATO, while automated billing alerts notify key stakeholders of sudden spend spikes driven by compromised workloads. Essential Contacts: To ensure notifications reach the right people instantly, Google Cloud allows you to maintain a dedicated directory of designated contacts across security, billing, and operations. Customer action plan: Hardening your environment We handle the security of the underlying infrastructure, yet your organization remains resilient only through proactive hygiene on your side of our shared fate partnership. To minimize risk exposure across your user accounts, service accounts, and API keys, we recommend implementing these foundational defenses. Mandatory identity protection: Enforce multi-factor authentication (MFA) and 2-Step Verification (2SV) across all user accounts and Google Workspace domains without exception to prevent AITM cookie theft and phishing attacks. Ensure that you use Device Bound Session Credentials (DBSC) for your Google Workspace accounts to bind a user's session to their specific device. Secure service accounts and API keys: Treat keys and tokens as top-tier secrets. Never embed API keys in source code or public repositories. Use keyless authentication where possible, rotate keys regularly, and follow strict governance for service account management. Enforce least privilege and perimeter defense: Use Identity and Access Management (IAM), VPC Service Controls (VPC-SC), and Context-Aware Access (CAA) to restrict access so identities only have the exact permissions required for their specific function. Configure Essential Contacts and billing alerts: Set up detailed billing alerts to identify unauthorized spending before costs rise, and conduct quarterly reviews to keep your Essential Contacts directory current. Regular resource hygiene: Conduct periodic audits of your organization to identify and decommission unused resources, legacy billing accounts, and dormant user accounts. Pay special attention to groups or service accounts with elevated permissions to ensure your attack surface remains as small as possible. Continuous vigilance together Google continuously monitors platform health to detect anomalous usage patterns before they impact your workloads. Ultimately, maintaining a secure environment is a partnership built on shared fate. While we take every precaution to prevent bad actors from gaining a foothold, protecting your organization requires equal dedication on your end. By applying robust access controls, staying vigilant, and adopting security best practices, together we can keep your workloads secure and resilient. Explore key resources to harden your environment: Google Cloud Security Best Practices Center Google Cloud Well-Architected Framework: Security, Privacy, and Compliance Google Workspace and Cloud Identity Security Checklists Best Practices for Managing Service Accounts & Keys Best Practices for Managing API Keys Google Cloud Trust Center

Unifying Structured and Unstructured Data Insights with BQ Search Innovations

Modern enterprises possess a vast amount of unstructured data, yet they frequently encounter significant challenges in managing and extracting value from it. Historically, unlocking the insights hidden within PDFs, audio files, images, and unstructured text required a fragmented architecture: moving data out of your warehouse, stitching together complex LLM pipelines, and managing disparate search indexes. BigQuery has worked with many enterprises to make sense of their unstructured data sources. For example, consider an advanced healthcare company managing thousands of clinical trial documents in PDF form. BigQuery helps unlock insights from these documents through a simple, five-step lifecycle: Access, Process, Ground, Relate, and Activate. In this post, we are highlighting three major milestones focused heavily on the "Ground" phase of this framework: General Availability (GA) of Autonomous Embedding Generation General Availability (GA) of AI.SEARCH with massive single-query performance gains Public Preview of Hybrid Search Let’s dive into how these features work together to simplify your AI architecture, using a real-world clinical trial research platform as an example. Simplify Pipelines with Autonomous Embedding Generation (GA) Building a retrieval-augmented generation (RAG) pipeline or search application usually requires managing complex, asynchronous embedding infrastructure. You have to handle retries, error logging, and pipeline orchestration every time a new record arrives. With the General Availability of Autonomous Embedding Generation, BigQuery manages this entirely for you. By simply defining a column in your schema, BigQuery asynchronously and continuously generates embeddings as new data is ingested. You have the flexibility to choose external models (like Vertex AI text-embeddings) or natively utilize Gemma embedding models directly within BigQuery. How it works in practice: Imagine you are building a research platform analyzing clinical trial PDFs stored in Google Cloud Storage. After extracting the study titles and disease areas into a table, you can automatically embed those titles: code_block <ListValue: [StructValue([('code', "CREATE OR REPLACE TABLE mydataset.clinical_trials\r\n(\r\n trial_id STRING,\r\n study_title STRING,\r\n study_embedding STRUCT<result ARRAY<FLOAT64>, status STRING>\r\n GENERATED ALWAYS AS (AI.EMBED(\r\n study_title,\r\n connection_id => 'myconnection',\r\n endpoint => 'text-embedding-005'\r\n ))\r\n STORED\r\n OPTIONS( asynchronous = TRUE )\r\n);"), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7fab13937550>)])]> BigQuery eliminates the need for complex third-party vector databases by managing enterprise-scale processing with one configuration. This autonomous embedding generation keeps data synchronized automatically as source text changes, removing the need for manual machine learning pipelines. This integrated approach streamlines workflows for dynamic datasets and reduces the operational burden of maintaining custom data scripts. Finally, with this GA launch, Autonomous Embedding Generation now also supports generating embeddings natively over images using ObjectRefs, unlocking true multimodal search and analytics. Natural Language Search at Scale with AI.SEARCH (GA) To truly enable conversational analytics agents and snappier user experiences, your underlying search infrastructure needs to be intuitive and performant. Once your data is seamlessly embedded, you need an efficient way to query it. Today, we are announcing the General Availability of AI.SEARCH(). This function provides a streamlined, natural-language-focused search experience, allowing you to easily find semantically related records without generating embeddings in your search path. In pairing this with the Autonomous Embedding Generation, we leverage the same embedding model used in your dataset for easier use. Furthermore, as part of efficiency investments in the last year, we have heavily optimized AI.SEARCH for single-query execution. For online applications and single-query searches (those most common in agentic searches), we have observed up to a 133x gain in slot efficiency.. This means you can serve highly concurrent, user-facing natural language searches directly out of BigQuery faster and more cost-effectively than ever before. code_block <ListValue: [StructValue([('code', 'SELECT base.trial_id, base.study_title, distance\r\nFROM\r\n AI.SEARCH(\r\n TABLE mydataset.clinical_trials,\r\n \'study_title\',\r\n "What treatments are available for advanced tumors?"\r\n );'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7fab13937220>)])]> Unifying Keyword and Vector with Hybrid Search (Public Preview) Semantic (vector) search is incredibly powerful; for example, the query above will successfully return conceptually related terms like "chemotherapy." However, semantic search isn't always enough. What if a researcher is searching for a specific, highly technical immunotherapy drug designation like "MK3475"? Because this alphanumeric string lacks broad semantic meaning, pure vector search might struggle to rank it correctly. By merging lexical search with existing semantic capabilities, BigQuery's hybrid search allows for data retrieval based on both keyword similarity and underlying meaning. This approach unites the conceptual depth of semantic vector search with the pinpoint accuracy of lexical matching, utilizing algorithms such as Reciprocal Rank Fusion and BM25. The result is a significant boost in search precision and a reduction in LLM hallucination costs through the reranking of results based on keyword frequency and semantic relevance. Users can implement this via the AI.SEARCH and VECTOR_SEARCH functions by employing the hybrid mode or lexical_search_columns parameters. Furthermore, performance can be optimized by extending vector indexes to include keyword data, which accelerates the lexical search process. You can now perform hybrid searches effortlessly using the AI.SEARCH() function by simply setting the mode to HYBRID: code_block <ListValue: [StructValue([('code', 'SELECT base.trial_id, base.study_title, distance\r\nFROM\r\n AI.SEARCH(\r\n TABLE mydataset.clinical_trials,\r\n \'study_title\',\r\n "Cancer treated by MK-3475",\r\n mode => \'HYBRID\'\r\n );'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7fab13937a90>)])]> To speed up these hybrid queries at scale, you can easily extend your CREATE VECTOR INDEX DDL to include the keyword columns you want to use for the lexical portion of the search, natively combining your indexes. Building an End-to-End Unstructured Data Analytics Platform The search and embedding features launching today are part of a much broader vision. We are building an end-to-end unstructured data analytics platform. One common type of unstructured data is documents, and BigQuery now provides the complete toolset to manage this workflow from end to end: Access: Enable zero-ETL workflows by querying unstructured PDFs and documents directly where they live in Google Cloud Storage using Object Tables. Process: Utilize embedded AI capabilities like AI.PARSE and AI.CHUNK_DOC (coming soon) for layout-aware chunking (perfect for RAG), AI.GENERATE for entity extraction/summarization, and AI.CLASSIFY to instantly categorize records using foundational models directly in your SQL pipelines. Ground: Build highly accurate context using Autonomous Embeddings and Hybrid Search. Hybrid search combines the conceptual understanding of semantic vector search with the exact precision of lexical keyword matching By reranking results based on both semantic relevance and keyword frequency, you drastically increase search precision and drive down LLM hallucinations. Relate: Uncover hidden multi-hop insights by mapping extracted entities (like Sponsors, Trials, and Drugs) into a BigQuery Graph—no specialized graph database required. Activate: Bring it all together with BigQuery's Conversational Analytics agents. Using functions like AI.AGG, you can chat directly with your complex data, generate visualizations, and perform trend analysis at massive scale. With unstructured data as a first-class citizen in BigQuery, you can finally bridge the gap between your raw documents and conversational AI. Ready to get started? Explore the Code: Check out the complete end-to-end clinical trials demonstration in our Document Analytics on BigQuery GitHub Repository. Read the Docs: Dive into the official documentation for Autonomous Embeddings and Hybrid Search to start building your own unified data pipelines today.

GOL! How Televisa Univision streamed the FIFA World Cup to millions with Google Cloud

Live sports broadcasting represents the ultimate stress test for digital media infrastructure, where operational success or failure is measured in milliseconds and observed live by millions of viewers simultaneously. During the 2026 FIFA World Cup, the stakes reached a high for TelevisaUnivision, the leading Spanish-language media conglomerate. With Mexico serving as both a primary host nation and a core contender on home soil, fan engagement created unprecedented demand across Latin America and TelevisaUnivision's ViX streaming platform. For a marquee broadcaster like TelevisaUnivision, high-stakes events carry direct, long-term brand and reputational implications. Audiences demand uninterrupted, pristine access to every critical moment of play. Playback interruptions during a key goal, login latency at kickoff, or degraded stream resolutions immediately impact customer satisfaction, risking subscriber churn and brand dilution. When streaming tier-1 global sports events, technical execution directly impacts consumer trust, requiring an absolute commitment to zero-downtime availability and flawless performance on the part of the provider. Why TelevisaUnivision selected Google Cloud Navigating Latin America's complex networking ecosystem, which is marked by heavy ISP fragmentation and cross-border transit bottlenecks, required more than a standard vendor relationship. TelevisaUnivision needed a strategic partner willing to make joint investments in network capacity, infrastructure resiliency, and custom feature development. TelevisaUnivision selected Google Cloud's Media CDN based on two foundational differentiators: its architecture, and Google Cloud’s customer focus. Platform architecture Media CDN provided a resilient, globally distributed infrastructure built specifically to absorb massive live-stream traffic spikes while protecting origin infrastructure. Core architectural advantages included: In-ISP deep edge caching: Media CDN embedded cache nodes deep within local ISP networks across Mexico and Central and South America, placing video segments within a single network hop of viewers. Direct ISP peering: By establishing direct peering connections with major regional telecommunications operators such as América Móvil and Telefônica, the architecture completely bypassed congested international transit routes. Dedicated capacity reservations: TelevisaUnivision reserved live event capacity with dedicated allocated headroom in-region. This isolated livestream traffic from "noisy neighbor" risks and comfortably absorbed peak traffic surges. Sub-millisecond sessions with Valkey 9.0: To handle massive traffic spikes during the World Cup, TelevisaUnivision migrated its session store to Memorystore for Valkey 9.0, operating as serverless microservices on edge compute. This architecture delivered sub-millisecond response times for critical authentication and entitlement checks, while providing automatic scaling to process peak API traffic without the need for manual capacity reservations. Obsession for customer success Beyond technical capabilities, TelevisaUnivision chose Google Cloud for its joint co-engineering model and deep operational alignment. Joint 24/7 war rooms: For all 104 matches, TelevisaUnivision engineers and Google Cloud specialists operated side-by-side in unified command centers. Proactive monitoring as a service: Google Cloud’s Customer Reliability Engineering teams provided round-the-clock proactive monitoring and automated alerting. Joint operational authority: Combined teams performed extensive pre-tournament stress tests and simulated failovers. During live matches, unified telemetry empowered joint leads to dynamically route traffic and adjust CDN configurations instantly as regional ISP congestion emerged. Summary The strategic partnership between TelevisaUnivision and Google Cloud during the 2026 FIFA World Cup established a new benchmark for global sports broadcasting. Across 39 consecutive days of tournament execution, TelevisaUnivision reported that the joint infrastructure delivered: Total matches broadcast 104 live matches Platform availability 100% platform availability (0 downtime) Cumulative viewership 675 million views across TelevisaUnivision & ViX By uniting localized edge delivery, sub-millisecond serverless compute, and dedicated operational co-engineering, TelevisaUnivision and Google Cloud solidified a battle-tested blueprint for executing marquee live streaming events at record global scale.

Responding to the next frontier of critical cyber capabilities

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Introducing FILE type: a native column type for multimodal data

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Doximity Stock Soars 55% After Earnings Miss. Its AI Can Beat Anthropic. - Barron's

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Jill Lepore on the ‘Artificial State’ and why Silicon Valley’s leaders are bad sci-fi readers

Historian Jill Lepore has a theory about why tech companies often use soaring language to describe their products — almost as if they’re forming a new government. And whether you’re thinking of Twitter’s old “town hall in your pocket” or Anthropic’s Claude constitution, it’s a theory that doesn’t paint Silicon Valley in a very flattering light. In Lepore’s upcoming book, The Rise and Fall of the Artificial State, the Pulitzer […]

Open AI’s Security Breach Was More Alarming Than We Knew

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Byte Dance trains massive AI model in bid to rival Anthropic

TikTok owner training a model with 10 trillion parameters.

Not Micron, Not Nvidia. This Artificial Intelligence (AI) Giant Could Be the Ultimate Winner of the AI Arms Race. - Yahoo Finance

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Why Google Deep Mind broke up the Alpha Fold team - Scientific American

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Open AI's hockey-puck-sized smart speaker with moving parts is set to ship in 2027

OpenAI's planned smart speaker is taking shape. The company's first hardware device will be donut-shaped, roughly the size of a hockey puck, and priced above $300, according to Bloomberg. The article OpenAI's hockey-puck-sized smart speaker with moving parts is set to ship in 2027 appeared first on The Decoder.

Managing AI Coding Costs at Scale

AI coding tools deliver immense value: at Databricks, agentic coding has measurably...

What is an AI Assistant?

AI assistants use language models, data retrieval, and reasoning to understand requests...

New Mexico court orders Meta to pay additional $567 M in child safety case

Meta's total fine has raked up to $942 million in this case.

The Hottest New AI Chatbot Is Just a Guy Answering Your Questions

WIRED spoke with Tucker Bryant, an artist and former Google employee who created ChatTJB to get people to reflect on the “strange moment” we’re in.

Open AI reportedly slows research after its own models secretly coordinated hacks for weeks undetected

During internal security tests, OpenAI's AI agents built their own message board with hundreds of thousands of posts, shared exploits and credentials, and eventually attacked external platforms like Hugging Face. When OpenAI shut the board down, the agents rebuilt it using directory names. OpenAI researcher Boaz Barak says, "We (like everyone else) are not where we want and need to be." The article OpenAI reportedly slows research after its own models secretly coordinated hacks for weeks undetected appeared first on The Decoder.

How HSP GRUPPE builds AI capabilities for tax advisory

Discover how HSP GRUPPE uses ChatGPT Enterprise to boost productivity, improve work quality, and create more capacity for tax advisory and client service.

Amazon, Cursor, Microsoft, Open AI, and Vercel unite on a shared standard for AI agent plugins

Amazon, Cursor, Microsoft, OpenAI, and Vercel have jointly created Agent Plugins, an open standard that defines a single package format for AI agent extensions. Version 1.0.0 uses a plugin.json manifest file and supports both agent skills and MCP servers. The article Amazon, Cursor, Microsoft, OpenAI, and Vercel unite on a shared standard for AI agent plugins appeared first on The Decoder.

Open AI improves GPT-5.6 Sol in Chat GPT and restricts free users to its weakest model

OpenAI has updated GPT-5.6 Sol with more focused responses and a reasoning slider that lets users adjust how deeply the model thinks. Free users will get unlimited text chats with the smaller GPT-5.6 Luna starting next week, plus a button that lets Luna reason longer. But the smaller model still falls well short of its bigger siblings. The article OpenAI improves GPT-5.6 Sol in ChatGPT and restricts free users to its weakest model appeared first on The Decoder.

Microsoft Open Sources code-testing-generator: a Polyglot Unit-Test Agent That Hits 92.1% Task Completion Versus 78.9% for Stock Copilot

Microsoft has open sourced code-testing-generator, a polyglot unit-test agent shipping in the MIT-licensed dotnet/skills repository. It reads a repository before writing anything — detecting the language, test framework, existing conventions, and the real build and test commands — then plans, writes, runs and validates the tests it produces. On Microsoft's internal 152-task benchmark it completed 140 tasks against 120 for stock GitHub Copilot on the same model, with the gain concentrated almost entirely in vague prompts and diff-targeted requests. The post Microsoft Open Sources code-testing-generator: a Polyglot Unit-Test Agent That Hits 92.1% Task Completion Versus 78.9% for Stock Copilot appeared first on MarkTechPost.

Byte Dance targets mega AI model nearing Anthropic’s Mythos - Financial Times

ByteDance targets mega AI model nearing Anthropic’s Mythos Financial Times

Nvidia Is a Massive Investor in the Genius Artificial Intelligence (AI) Stock Up 170% This Year - Yahoo Finance

Nvidia Is a Massive Investor in the Genius Artificial Intelligence (AI) Stock Up 170% This Year Yahoo Finance

Is Personalized Modality Weighting Actually Personalized? A Controlled Audit of Per-User Weighting Claims in Multimodal Recommenders

arXiv:2608.05655v1 Announce Type: new Abstract: Per-user modality weighting is deployed at billion-user scale in multimodal recommenders, through user modality-strength vectors, attention gates, meta-weight hypernetworks, and low-rank guided weights, each claiming a ranking gain from user-specific modality preference. Yet, to our knowledge, prior evaluations do not isolate a genuinely user-specific signal from a global modality weight plus model capacity. We audit this family with a two-contrast audit principle, reducing six implementations onto one shared collaborative backbone and measuring a utility gap (real-GM) against a single global modality weight and an identifiability gap (real-shuf) against an eval-time permutation of the user-weight binding. Across three independent short-video corpora, a single global weight already delivers nearly all of the content gain (+1.9/+3.6/+3.5pp over a no-modality baseline, p < .001). Making the weight per-user adds no consistent utility: no implementation wins on all corpora and metrics, and the few positive gaps are small (<=0.9pp) and flip. The shuffle control is necessary but not sufficient, since real-shuf reaches +128% of the content gain for heads that simultaneously lose to the global weight. We trace this dissociation to gates reading the shared collaborative embedding: decoupling the gate input collapses the inflated real-shuf to near zero while the utility conclusion stands. A monotone signal-implant dose-response (capture AUROC rising from 0.57 to 0.89 and from 0.64 to 1.00) verifies the harness would detect user-specific structure if present, and every finding replicates on a fourth, cross-domain e-commerce corpus. We propose reporting real-GM alongside real-shuf as a minimum evidentiary standard for personalization claims.

Orchestra Bench: Evaluating Multi-Agent Orchestration Failure Modes, Recovery, and Decomposition Quality

arXiv:2608.05263v1 Announce Type: new Abstract: Multi-agent orchestration frameworks are moving from demos to production, yet benchmarks typically report task accuracy without diagnosing why a pipeline failed, where a cascade began, or which routing decision caused the breakdown. OrchestraBench evaluates failure, recovery, and decomposition through a controlled, seed-reproducible failure-injection harness over templated enterprise workflows. It introduces cascade radius and per-failure-mode recovery as primary metrics and compares routing policies with bootstrap confidence intervals and paired tests. On a 26-case gold-labelled diagnostic, a keyword/flag router scored 0% on adversarial cases with misleading or missing surface flags, whereas an intent-reasoning model router scored 100%, matching the oracle. Controlled mechanism probes with a real Claude agent over a verifiable arithmetic dependency chain revealed three failure-handling tiers across five MAST modes: tool faults recovered fully (1.0), ambiguous delegation recovered partially (0.30), and three latent or semantic modes never recovered (0.0). This ordering persisted when the computation was reframed as a loan-approval workflow and across Sonnet, Opus, and Haiku, although absolute rates shifted with context. Blind retry reproduced latent faults and increased time to detection, indicating that detection and attribution are necessary for containment. Cascade radius increased with pipeline depth (mean 0.9 to 4.7 across depths 3-7). A trusted-state repair ablation showed that apparent containment gains primarily came from the trusted-state signal rather than autonomous detection. These results are controlled-chain mechanism probes, not domain-workload claims.

Meta Artificial Intelligence Is the Latest AI Technology to Hack Another Company During Testing - People.com

Meta Artificial Intelligence Is the Latest AI Technology to Hack Another Company During Testing People.com

'Significant risk': AI expert calls for legislation to address security breaches - National Desk

'Significant risk': AI expert calls for legislation to address security breaches National Desk

Open AI’s new AI smart speaker will reportedly sell for between $300 and $400

Additional details about OpenAI's mysterious new AI device make it sound like a pricey smart speaker.

What are Agentic Workflows?

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What is Tool Calling?

Tool calling is the ability of an AI model to interact with external tools, APIs,...