GCP News - 2026-09-02
2026-09-02
最終更新: 2026-09-03 04:42:34 JST
AI による概要
この日は BigQuery の予測分析と、us-central1 で起きた障害が目を引きました。BigQuery に Google Research が開発した表形式データ向けの事前学習済み基盤モデル TabFM がプレビューで追加されました。解約予測や購買意向、不正スコアリングといった予測分析は、これまで XGBoost やランダムフォレスト、DNN でカスタムモデルを組み、学習・チューニング・デプロイ・再学習のサイクルを回す必要がありましたが、TabFM はコンテキスト内学習 (ICL) により、ラベル付きの履歴データと予測対象テーブルを 1 つの SQL 関数へ渡すだけでゼロショットの予測を返します。学習とデプロイの工程が消えるため、エージェント型アプリケーションへ予測能力を組み込む用途にも向きます。障害としては、太平洋時間 2026 年 9 月 1 日 07:44 から 11:52 にかけて us-central1-b の一部でネットワークの品質低下が発生し、BigQuery、Dataflow、Cloud SQL、Cloud Run、AlloyDB、Spanner、GKE など広範なプロダクトでパケットロスとエラーの増加が観測されました。予備的な分析では、定期的なネットワークファブリックの経路メンテナンスが us-central1-b の 1 クラスタで想定外の問題を引き起こしたことが原因とされ、影響を受けた容量の復旧後、同リージョンのメンテナンスは事前監査のため停止されています。セキュリティ速報も 1 件出ており、GKE Multi-Cloud の CreateAttachedCluster / CreateAwsCluster / CreateAzureCluster API にプロジェクトの権限チェック漏れがあり、任意の対象プロジェクトの Workload Identity Federation for GKE へアタッチ済みクラスタを登録できてしまう問題 (GCP-2026-058) が Critical として公開されました。悪用には対象のプロジェクト番号 (プロジェクト ID ではない) と、roles/iam.workloadIdentityUser がバインドされた名前空間とサービスアカウントの組み合わせを知っている必要があり、実際に悪用された形跡はないと確認されています。サーバー側に不足していた認可チェックを追加する形で全 GKE クラスタにパッチ適用済みで、利用者側の対応は不要です。このほか VPC Service Controls に違反アナライザーと違反ダッシュボードというポリシーインテリジェンス機能が追加され、データ持ち出しや侵害されたアカウント、内部脅威に備える境界の管理とトラブルシュートが簡素化されました。Fortune 500 の半数超に採用される財務オペレーション管理の BlackLine が、上位環境と下位環境を明確に分離する境界の維持に活用しています。リリースノートでは Spanner が PostgreSQL 方言のデータベースで TABLESAMPLE 演算子を使い、データセットのランダムなサンプルを取得できるようになりました。月次まとめでは、エージェントワークロード向けの柔軟な課金とコスト管理、金融サービス向けと法務向けの Gemini Enterprise、企業向けに広がった Antigravity が取り上げられました。日本語ブログは Maps Platform と学習コンテンツが中心で、Navigation SDK にレイアウトカスタマイズフレームワークが Android で試験運用版として導入され、Gemini Enterprise Agent Platform の Google マップによるグラウンディングには最大 13 か所の経由地を扱えるルート検索が一般提供されました。後者は Realtor.com の RealAssist AI でセッション時間 4 倍、一意の見込み顧客 15 倍という成果につながっています。このほかインド最大の EV 充電ネットワークである Statiq の Places UI キット活用事例、5 週間の無料ライブ学習シリーズ Agent Valley の案内、Google Cloud Partner Top Engineer 2027 の申し込み開始が公開されました。
主要トピック
脆弱性: GKE Multi-Cloud のクラスタ作成 API に権限チェック漏れ (GCP-2026-058、Critical)。パッチ適用済みで利用者の対応は不要、悪用の形跡なし
障害: us-central1-b で 2026-09-01 07:44〜11:52 (PT) にネットワーク品質低下、BigQuery や GKE など広範に影響し復旧済み
予測分析: BigQuery に表形式データ向け基盤モデル TabFM がプレビュー、1 つの SQL でゼロショット予測
セキュリティ: VPC Service Controls に違反アナライザーと違反ダッシュボードを追加 (BlackLine の事例)
データベース: Spanner の PostgreSQL 方言で TABLESAMPLE 演算子によるランダムサンプリングに対応
位置情報: Google マップによるグラウンディングのルート検索が GA、最大 13 経由地に対応し Realtor.com でセッション 4 倍・見込み顧客 15 倍
モバイル: Navigation SDK のレイアウトカスタマイズフレームワークが Android で試験運用版として提供開始
AI 月次まとめ: エージェント向けの柔軟な課金とコスト管理、金融サービス版・法務版の Gemini Enterprise、企業向け Antigravity
学習: 5 週間の無料ライブシリーズ Agent Valley が 9 月 1 日から毎週火曜に開講
国内: Google Cloud Partner Top Engineer 2027 の申し込みを開始 (2026-10-09 まで、結果発表は 2026-11-25)
Google Cloud Release Notes
September 02, 2026
- Link: https://docs.cloud.google.com/release-notes#September_02_2026
- Published: 2026-09-02 16:00:00
- Fetched: 2026-09-03 04:42:27
Spanner
Feature
Spanner supports using the TABLESAMPLE operator in PostgreSQL-dialect
databases to select a random sample of a dataset.
For more information, see
TABLESAMPLE operator.
Google Cloud Blog
How Blackline simplifies perimeter policy intelligence with VPC Service Controls
- Link: https://cloud.google.com/blog/topics/customers/how-blackline-prevents-data-exfiltration-with-vpc-service-controls/
- Published: 2026-09-02 01:00:00
- Fetched: 2026-09-02 02:09:31
詳細を表示
Establishing network-level perimeters with VPC Service Controls (VPC-SC) is a critical step that can help you protect your cloud environment against data exfiltration, compromised accounts, and insider threats.
Today, Google Cloud is excited to share new policy intelligence capabilities in VPC-SC that can help drive even greater operational simplicity. With our latest release of the VPC-SC violation analyzer and violation dashboard, we have simplified policy management and troubleshooting, to make managing and optimizing your security perimeter more efficient and straightforward than ever.
How BlackLine streamlines incident response
BlackLine, a leader in financial operations management, adopted the VPC-SC policy intelligence solution to maintain strict security perimeters. Chosen by over half of Fortune 500 companies, BlackLine uses Google Cloud's full suite of managed services and built-in security capabilities to protect sensitive customer financial data.
VPC Service Controls are the foundation of BlackLine's preventative compliance and security controls in our Google Cloud environment, helping us to mitigate data exfiltration risks and ensure clear separation between our higher and lower environments by establishing strong security perimeters.
Managing these complex perimeters is a continuous process. VPC Service Controls violation analyzer helps BlackLine cloud infrastructure administrators adapt to changing API connection requirements of the business by adjusting security perimeters through approved access levels, ingress policies, and egress policies.
With only the troubleshooting token or unique ID from any VPC-SC violation error message, we can produce a detailed report identifying the principals and target resources involved in a failed API request, and explaining why and how that API request violated BlackLine's service perimeters. We don’t need to write a Cloud Logging SQL query to extract the data.
The clear access context and actionable insights in the violation details report are an invaluable starting point as we collaborate to resolve violations, significantly reducing our mean-time-to-resolution (MTTR) for service perimeter issues, and helping BlackLine maintain our focus on our customers and continue to innovate on their behalf.
Streamlining the perimeter operations lifecycle
Our new policy intelligence tools — the VPC-SC Violation analyzer and Violation dashboard — simplify real-time monitoring and active incident response. These tools provide clear, actionable insights in the Google Cloud Console, offering greater speed and automation to help you confidently enforce least-privilege perimeters, and quickly resolve access denials.
Violation Dashboard aggregates and visualizes all service perimeter violations across your entire Google Cloud organization in a single pane of glass, helping your team identify trends, spot spikes in access denials, and shareable filters on violations by specific perimeters, projects, or identities.
Violation Analyzer streamlines investigating violations, eliminating the need to query Cloud Logging and manually piece together the details. When you click a troubleshooting token from the dashboard (or input a unique denial ID), the analyzer maps out the identity, source, target, and VPC-SC rule triggered, creating a report telling you why that specific request was blocked. This helps your team more quickly take action to determine whether to modify existing policy rules or create a new one, and resolve incidents more quickly.
Together, the new VPC Service Controls policy intelligence tools go beyond automated log analysis to provide unified visibility of violations and actionable insights to investigate them, making your perimeter deployment and management simpler and lower-risk.
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<figcaption class="article-image__caption "><p>Streamlining the VPC Service Controls lifecycle, from deployment to policy refinement.</p></figcaption>
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With the new VPC-SC troubleshooting tools you can more easily:
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Test new perimeters (deployment): Use the violation dashboard to visualize the impact of a service perimeter during your initial dry run phase, helping to verify that enforcement is accurate and predictable before it affects production traffic. Filter violations to track and resolve with prebuilt contextual filters for principals, service perimeters, enforcement type, and more.
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Track perimeter denials (monitor): The violation dashboard offers a unified view of your perimeter health, allowing your security operations team to monitor status in real time, including dynamic agentic access denials.
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Triage an event (investigate): Violation analyzer provides the identity, source, target, and operations for any violation. It cross-references identity and access management (IAM) permissions, resource ancestry, and context evaluation to identify which rule was triggered, reducing manual effort.
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Fix the rule (refine policy): Instead of searching through configuration files, violation analyzer maps violations directly to the relevant line in your VPC-SC policy, allowing you to make updates more quickly and with less manual overhead.
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<figcaption class="article-image__caption "><p>The VPC Service Controls violation dashboard produces detailed reports to jump-start perimeter access investigations that are simplified using the violation analyzer.</p></figcaption>
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Core VPC-SC operations: Simple perimeter enforcement
Our new troubleshooting capabilities build on VPC Service Controls’ foundational simplicity for designing, enforcing, and managing strong perimeters.
By using dry run mode, your teams can build precise, contextual ingress and egress rules based on observed traffic — without disrupting vital business workflows. Once you validate these access patterns, moving to full enforcement becomes a more confident, data-driven process. To keep perimeter maintenance more efficient and straightforward, scoped policies allow you to delegate management directly to project-level administrators, empowering the teams closest to the workload.
Getting started
Simplify data security with VPC Service Controls. With the new Violation Analyzer and Violation dashboard, you can spend less time investigating incidents and more time safely scaling your cloud initiatives. Your data is your most valuable asset — protect it with a perimeter that’s as simple to manage as it is effective in enforcing controls.
Learn more and get started with the VPC-SC violation analyzer and violation dashboard in our documentation.
Introducing TabFM in BigQuery: Predictive analytics reimagined
- Link: https://cloud.google.com/blog/products/data-analytics/tabfm-adds-predictive-ml-to-bigquery/
- Published: 2026-09-02 01:00:00
- Fetched: 2026-09-02 02:09:31
詳細を表示
Historically, enterprise predictive analytics tasks such as predicting churn, purchase intent, or fraud scoring have meant building custom models using libraries like XGBoost, Random Forest, or Deep Neural Networks (DNNs). While effective, the traditional train-tune-deploy-retrain cycle can be complex and time-consuming. Additionally, the overhead of manual feature engineering, hyperparameter tuning, lengthy and expensive training, and the need for specialized data science skills can lead businesses to underutilize predictive models in their decision-making.
Today, we are announcing the TabFM model in BigQuery. Developed by Google Research, TabFM is a state-of-the-art, pre-trained foundation model for regression and classification on tabular data. It leverages in-context learning (ICL) to deliver highly accurate predictions on your tabular datasets instantly via a single SQL statement, removing the separate training and deployment steps. TabFM on BigQuery is currently in preview.
Here is what TabFM brings to your BigQuery analytics:
- Zero-shot predictions: Skip model training, tuning, and artifact deployment. Simply pass your labeled historical data and new prediction tables into a single SQL function to get instant, high-quality predictions.
- Predictive ML for your agentic applications: Building an agent for your business use? Add predictive powers to it with TabFM plus BigQuery MCP server. No runtimes or infrastructure to manage, just data in and predictions out.
- State-of-the-art accuracy: Outperforms custom-trained, out-of-the-box traditional models on complex datasets, achieving superior accuracy scores on industry benchmarks.
- Simple developer experience: Runs natively in BigQuery and is accessible via simple SQL syntax. Automatically handles featurization tasks such as missing values, categorical encoding, etc., with no complex feature engineering pipelines to manage.
- Scalability: Processes massive inference tables (up to millions of rows) in minutes using BigQuery’s distributed inference architecture.
The leading model for tabular predictions
Google’s TabFM delivers industry-leading accuracy across a wide range of tabular data. In evaluations on the TabArena benchmark, TabFM consistently outperforms both classic machine learning models and other tabular foundation models.
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<figcaption class="article-image__caption "><p>ELO ratings (↑) for the top 10 models across TabArena classification (upper) and regression (lower). (D) = default; (T+E) = tuned + ensemble. Higher scores denote superior performance.</p></figcaption>
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Learn more about the TabFM model here.
Getting started with TabFM in BigQuery
Using TabFM is straightforward. It is exposed directly through new, built-in SQL functions: AI.PREDICT and AI.EVALUATE.
1. Get instant predictions with AI.PREDICT
To make predictions, you write a single query that passes your training data and prediction data. The model automatically infers whether the task is a classification or regression problem based on the data type of your target label.
- code_block
- <ListValue: [StructValue([('code', "-- Classifying transactions as fraudulent or not\r\nSELECT *\r\nFROM AI.PREDICT(\r\n TABLE `my_project.my_dataset.historical_transactions`, -- Training data (in-context examples)\r\n TABLE `my_project.my_dataset.new_transactions`, -- Prediction data\r\n label_col => 'is_fraud'-- Target column to predict\r\n);"), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7f97d6f75d00>)])]>
In this example, the output contains all original columns from your prediction table plus predicted label and probability columns (e.g. predicted_is_fraud). No manual feature engineering or model creation was required.
2. Evaluate models with AI.EVALUATE
You can quickly check prediction performance against a test set using the AI.EVALUATE function. This allows you to generate standard evaluation metrics in a single step.
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- <ListValue: [StructValue([('code', "-- Regression Evaluation for Customer Lifetime Value (LTV)\r\nSELECT *\r\nFROM AI.EVALUATE(\r\n TABLE `my_project.my_dataset.historical_customer_ltv`,\r\n TABLE `my_project.my_dataset.test_customer_ltv`,\r\n label_col => 'ltv'\r\n);"), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7f97d6f75b80>)])]>
AI.EVALUATE returns a robust set of metrics such as r2_score, mean_absolute_error etc. for regression problems and metrics such as precision, recall, and f1 for classification problems.
TabFM in BigQuery under the hood
Traditional machine learning requires fitting model parameters to a training dataset. TabFM, in contrast, uses in-context learning. Similar to how large language models (LLMs) learn a task from few-shot examples in a prompt, TabFM reads your training table as in-context examples and generates predictions for your target table in a single forward pass.
To handle the computational complexity and memory footprint of tabular foundation models, BigQuery performs distributed, parallelized inference on your data. Further, to optimize performance and resource utilization, it uses intelligent training-data sampling as well as distributed execution. This allows BigQuery to handle large input rows for training data while executing predictions quickly and efficiently across millions of rows of inference data.
Choosing the right tool for the job
TabFM introduces groundbreaking zero-shot capabilities to BigQuery, and complements existing offerings such as XGBoost models. Here’s how to choose between TabFM and other models:
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Use TabFM when you need rapid, high-quality predictive insights without machine learning expertise, when historical datasets are small-to-medium sized, when data changes frequently, and when you need to retrain your models frequently to maintain accuracy. It is also a great fit for conversational or agentic workflows where you need predictive analysis on demand.
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Use traditional models like XGBoost when you have very large historical datasets, require complete control over custom hyperparameter tuning, have a high number of features that exceed current limits of TabFM, or need feature-importance explainability, i.e., which of the input features contributed most to the prediction.
Predictive machine learning made easy
With TabFM natively integrated into BigQuery, predictive ML is now as easy as running a standard SELECT query. By eliminating the manual overhead of model training, tuning, and management, TabFM lets developers, data scientists and analysts go from raw data to rich predictive insights in seconds.
To get started today, check out the public documentation. For questions or feedback reach out to our team at bqml_feedback@google.com. We look forward to seeing what you build!
What Google Cloud announced in AI this month
- Link: https://cloud.google.com/blog/products/ai-machine-learning/what-google-cloud-announced-in-ai-this-month/
- Published: 2026-09-02 01:00:00
- Fetched: 2026-09-02 08:01:35
詳細を表示
Editor’s note: Want to keep up with the latest from Google Cloud? Check back here for a monthly recap of our latest updates, announcements, resources, events, learning opportunities, and more.
This month, we focused on making AI highly practical for your business, including its associated costs. This meant tailoring our models for specialized industries – starting with Financial Services and Legal – and helping you keep your budgets under control. Let’s dive in!
Top announcements
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FinOps for the AI era: New flexible billing and cost controls for agents: To help you get better return on AI, we announced expanded billing flexibility and new cost management tools for agent workloads across Gemini Enterprise and developer tools like Google Antigravity in Gemini Enterprise and Android Studio.
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Gemini Enterprise for Financial Services: We’re bringing Google’s agentic AI directly into the workflows of capital markets and corporate banking.
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Gemini Enterprise for Legal: Gemini Enterprise for Legal provides an integrated, fully governed environment configured for rapid deployment across firms and corporate legal departments.
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Expanding Google Antigravity for enterprise customers: Antigravity is available now as part of eligible Gemini Enterprise app subscriptions, including out-of-the-box administrative and spend controls.
Thought leadership (editor’s pick):
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Tokenomics: Why smart teams spend more on AI, on purpose: Hear from Eric Lam, Head of Value, Delta, Google Cloud Consulting about how disciplined organizations are moving past reactive sticker shock over AI bills and embracing tokenomics.
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Meet the researcher fighting AI hallucinations at Google Cloud: Cyrus is a senior research scientist at Google. Lately, his focus has shifted to large language models, specifically a persistent issue known as hallucination, which is when an artificial intelligence model lacks the correct facts but confidently invents an answer anyway.
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What sports cars can teach us about optimizing AI spend: More tokens doesn't always mean better AI. Read our conversation with Mike Clark, Director of Product Management for Gemini Enterprise Agent Platform, on how to balance horsepower with efficiency and get the highest return out of every dollar you spend on AI.
News you can use:
Looking for a steer on your foundation or inspiration for your next project? Take a look at some of our favorite how-to guides from August:
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10 questions every startup should answer before moving to production with their AI prototype: These ten are scoped to the prototype-to-production transition itself. Each question ends with a short, runnable snippet you can copy into your own project today. Adjacent decisions that matter just as much but aren't specific to that move, your data layer and RAG architecture, CI/CD, network design, are deliberately out of frame here.
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Your chance to start building AI agents from the absolute basics: Agent Valley is a free, 5-week live learning series designed to take you from scratch to building your very own hands-on agent systems. And instead of staring at boring terminal lines, you’ll be building and playing inside a tiny, low-poly virtual world!
Stay tuned for monthly updates on Google Cloud’s AI announcements, news, and best practices. For a deeper dive into the latest from Google Cloud customers, read our monthly recap, Cool stuff customers built.
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July
Since launching the Gemini Enterprise Agent Platform a few months ago, we’ve watched businesses move from basic experiments to serious, production-grade builds. We want to make it even easier — and more secure — for you to scale those systems.
Along with a batch of new platform updates, this month we’ve put together 13 practical demos and 20 diagnostic questions to help your engineering teams align on a strong architectural blueprint. Let’s dive in!
Top announcements
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What’s new in Gemini Enterprise Agent Platform: In this helpful recap, we announced some of our most popular capabilities are available for everyone, from Agent Runtime to Agent Identity.
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Now in preview: Find and fix software vulnerabilities with CodeMender: As adversarial AI threats accelerate attacks on code, security teams must counter them with machine-speed defenses that can automate code remediation and fight AI with AI. You can learn more about CodeMender and review the documentation here.
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Solve harder problems with AlphaEvolve, now available to everyone on Google Cloud: AlphaEvolve is a code optimization and discovery agent built on top of Gemini that helps solve the hardest algorithmic problems and achieve breakthroughs for your business and research.
Thought leadership (editor’s pick):
If automation requires delegation, then delegation requires trust. But letting an AI agent run on its own is a big leap for any business. While the productivity gains are clear, the fear of losing control is very real. This month, we sat down with our experts to discuss how leaders can navigate this shift by focusing on transparency, predictability, and setting clear boundaries for how agents handle weird data exceptions.
Here’s our picks for the month to learn more.
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How leaders can scale AI by trading control for trust (Q&A): We sat down with Michael Gerstenhaber, VP of Product Management for Gemini Enterprise, to discuss why the future of AI is about defining safe boundaries.
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What makes an AI agent trustworthy: Context is fast becoming one of the most valuable assets a company owns. Prajakta Damle, Senior Director, Product Management, shares what it takes to get trustworthy AI right.
News you can use:
What if you’re looking for a steer on your basic foundation, inspiration for recipes, or some inspiration? Take a look at some of our favorite how-to guides from July:
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Automate your agent development lifecycle using any coding agent: Stuck prototyping? With Agents CLI skills, you can go through the different phases of the entire agent lifecycle without ever leaving your coding agent.
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Why AI apps fail in production (And how Google solved it): Only 5% of AI prototypes make it to production, and the other 95% fall into the validation abyss. How can you move confidently into production?
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13 hands-on demos to build on Gemini Enterprise Agent Platform: Not sure where to start with Agent Platform? Here’s 13 ways you can stir up some creativity.
Stay tuned for monthly updates on Google Cloud’s AI announcements, news, and best practices. For a deeper dive into the latest from Google Cloud customers, read our monthly recap, Cool stuff customers built.
June
Our main focus in June was helping your teams build, scale, and secure AI. Today, we’re sharing a fresh roundup of updates designed to help you run smarter, more secure applications while keeping everything under your control.
We even shared a cool virtual shopping demo at Cannes to show how retailers can make product discovery more exciting. Let’s dive in!
Top announcements
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Introducing the Open Knowledge Format: We introduced the Open Knowledge Format (OKF), an open specification that formalizes the LLM-wiki pattern into a portable, interoperable format. This is a vendor-neutral, agent- and human-friendly standard for representing the metadata, context, and curated knowledge that modern AI systems need.
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Collaboration with Apple on its expanded Private Cloud Compute (PCC) systems: Our collaboration with Apple is built on a foundation of deep commitment to privacy that leverages Google Cloud's security and privacy technologies. At the heart of this collaboration is our Confidential Computing portfolio and our Titanium security architecture.
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Claude Fable 5: Available on Google Cloud: Claude Fable 5, Anthropic’s latest frontier model, is now generally available on Google Cloud. This launch is the latest proof point of our ongoing commitment to bring the industry's latest models straight to our Agent Platform.
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Cloud Atelier: How Gemini Enterprise is helping restyle the retail playbook: This year at Cannes, we showcased Cloud Atelier — a destination-based, virtual shopping experience that highlights how retail brands can turn this classic dilemma into an exciting moment of product discovery.
Thought leadership (editor’s pick):
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How Google Cloud Security uses AI internally: To counter machine-speed, AI-driven threats, we’ve worked hard to transition Google Cloud’s security posture to an autonomous, proactive model. By embedding specialized AI agents directly into our software development lifecycle (SDLC), we’ve created automated guardrails that protect code at a scale and speed unreachable by human teams — and we’re taking steps to make those same guardrails widely available.
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The 4 lessons that guided AI Threat Defense: We introduced Chris Betz as the new CISO of Google Cloud. For his first Cloud CISO Perspectives, Chris shares four key lessons we learned about using AI to the defender’s advantage while building AI Threat Defense.
News you can use:
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5 lessons from red teaming AI applications: To help you build AI securely, Mandiant has developed a proactive, risk-based approach centered on the Good AI Assessment (GAIA) Top 10, outlined in our new report, Secure Development of Generative AI Applications: A Proactive Approach.
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How to unlock true ROI in software development – a deep dive into the latest DORA research: To help you evaluate the costs and business benefits of AI, we recently shared the DORA: ROI of AI-assisted software development report. This research offers a practical approach to help your team work through early adoption challenges, align engineering plans, and drive business growth.
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Agent Factory Recap: 100X engineering with AI agents in Google Antigravity 2.0: In this episode of the Agent Factory, Shir Meir Lador, Head of AI Engineering, Google Cloud Developer Relations, sat down with Rody Davis, one of Google’s top agentic engineers. They dive into the massive shift from traditional IDEs to agent-first platforms, the reality of code reviews in an AI-driven world, and how to use "skills" to perform at a 100X level.
Stay tuned for monthly updates on Google Cloud’s AI announcements, news, and best practices. For a deeper dive into the latest from Google Cloud customers, read our monthly recap, Cool stuff customers built.
May
We’ve had a busy month! Between announcing Gemini Spark and Gemini 3.5 at Google I/O – and unveiling Google AI Threat Defense, our latest AI-powered cybersecurity solution, we had a lot to share with Google Cloud customers. Keeping up with the latest news takes time, so we gathered the most important announcements, thought leadership, and technical guides in one place to help you quickly catch up.
To learn more about our I/O announcements, here’s everything you need to know for Google Cloud customers, and top news for startups.
Top announcements
Introducing Google AI Threat Defense to help you outpace the adversary: Google Cloud is introducing a comprehensive AI-powered cybersecurity solution — Google AI Threat Defense — an always-on autonomous security platform. Learn more here.
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Gemini 3.5: Our latest family of models combines frontier intelligence with action – starting with Gemini 3.5 Flash.
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Gemini Omni: Our new model is a leap forward in world understanding, multimodality, and editing, letting you generate any output from any input, starting with video.
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Google Antigravity: Google Antigravity’s expanded capabilities and new integration with Agent Platform bring agentic development to your entire organization.
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Gemini Spark: For Gemini Enterprise and Workspace customers, Gemini Spark is your 24/7 personal AI agent that helps you work more efficiently by autonomously taking action on your behalf, under your direction.
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Google Workspace: Google Pics, our new image generation and editing tool, and new voice features in Gmail, Docs and Keep, help reimagine how you work.
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Managed Agents API on Agent Platform: Allows developers to build and run custom agents inside secure, Google-hosted environments that seamlessly integrate with Agent Platform.
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CodeMender: A powerful AI security agent provided through Agent Platform, CodeMender can help find and fix vulnerabilities in your code.
Nano Banana 2 and Nano Banana Pro are generally available: Available today via Gemini Enterprise Agent Platform, organizations are already putting the models to work. Learn more here.
Thought leadership (editor’s pick):
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Cloud CISO Perspectives: How Google + Wiz changes multicloud strategy for CISOs: Vinod D’Souza, director, Office of the CISO, shares highlights from his RSA Conference fireside chat with Anthony Belfiore, chief strategy officer, Wiz. While threat actors have seen gains from the adversarial misuse of AI, Google and Wiz are tackling these challenges head-on by combining Wiz's deep cloud telemetry with Google's world-class AI and quantum research to help CISOs and their organizations meet the needs of the agentic enterprise era. Read more here.
News you can use:
What Google I/O '26 means for developing agents on Google Cloud: Dig deep into how Gemini Enterprise Agent Platform and the new developer tools shared at I/O fit together, unpack the spectrum of choice for building, and share what we’d actually try first. Learn more here.
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Five must-have guides to move agents into production with Gemini Enterprise Agent Platform: Here is a look back at our five-part series covering the architecture patterns and best practices you need to move your agents into production. Learn more here.
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How to build an AI-ready security program for the public sector: From industrial control systems to decades-old municipal databases, here’s our CISO guidance to prep AI-ready security programs for the public sector. Learn more here.
Stay tuned for monthly updates on Google Cloud’s AI announcements, news, and best practices. For a deeper dive into the latest from Google Cloud customers, read our monthly recap, Cool stuff customers built.
April
We hosted Google Cloud Next in Las Vegas on April 22, announcing incredible innovations from Gemini Enterprise Agent Platform to our eight-generation TPUs. We also expanded the Gemini Enterprise app in collaborative ways – now, with new features like Projects, you can work side-by-side with your agents and colleagues.
If you missed the livestream, take a look at our Day 1 recap. It’s been incredible to see how customers have been applying AI in thousands of ways — so far, we’ve counted more than 1,300 examples.
Top announcements
1. Gemini Enterprise Agent Platform: Our new, comprehensive platform to build, scale, govern, and optimize agents. Moving forward, all Vertex AI services and roadmap evolutions will be delivered exclusively through the Agent Platform, rather than as a standalone service, to power the next generation of agent development.
The platform is designed around four core pillars — build, scale, govern, and optimize — that allow teams to collaborate seamlessly. Learn more about Agent Platform here.
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2. Gemini Enterprise app has all the key components to let teams discover, create, share, and run AI agents in a single environment. At Next ‘26, we introduced several new capabilities in the Gemini Enterprise app:
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Agent Designer uses the same no-code agent designer experience of Agent Platform and lets employees build sophisticated schedule- and trigger-based agents using any enterprise connector. It gives you a virtual flowchart of your agent, allowing you to inspect, test, and approve workflows, ensuring total transparency for executing critical business processes.
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Long-running agents are designed to execute complex business processes. They can work autonomously in secure cloud sandboxes, giving agents the ability to orchestrate business logic, write code to build custom tools, and complete multi-step work like reconciliation activities or sales prospect sequencing — without needing constant prompting.
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Inbox in Gemini Enterprise provides a central location to monitor, guide, and help manage all of your agent activity, including your long-running agents. Notifications are intuitively categorized into actionable groups like "Needs your input," "Errors," and "Completed.”
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Projects create a dedicated space where the agent’s memory is confined to the files and conversations your team adds. By connecting it to data sources including Google Drive, NotebookLM, and Google Group Chats, the agent becomes an expert on a specific topic and can provide team members daily briefings or status updates without digging through months of documents.
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Skills create simple shortcuts using an “@” mention for repetitive tasks such as applying brand guidelines, formatting a report, and accessing specific data.
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Canvas gives our customers an interactive editor directly within Gemini Enterprise. It allows teams to easily create and edit Docs and Slides, and even export to Microsoft 365 files, within the same experience.
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Agent Gallery provides access to third-party agents from partners like Adobe, Atlassian, Lovable, and ServiceNow, and is adding more third-party connectors for Asana, Mailchimp, Workday, and more. These integrations enable your agents to retrieve data and execute tasks with your systems-of-record.
3. AI Hypercomputer: Designed specifically for demanding AI workloads, our AI Hypercomputer is an advanced, purpose-built architecture that unites performance-optimized hardware for compute, storage, networking, open software and machine learning frameworks — as well as flexible consumption models — into a single, integrated system. We are announcing innovations at every layer of the AI Hypercomputer:
- TPU 8t, optimized for training, uses breakthrough Inter-Chip Interconnect (ICI) technology to scale up to 9,600 TPUs and 2 PB of shared, high-bandwidth memory in a single superpod. It achieves 3x the processing power of Ironwood and delivers up to 2x more performance/Watt.
- TPU 8i, optimized for inference, uses our new Boardfly topology to directly connect 1,152 TPUs in a single pod. It features 3x more on-chip SRAM compared to previous versions to host larger KV caches entirely on-silicon and integrates a specialized Collectives Acceleration Engine. Taken together, TPU 8i delivers 80% better performance per dollar for inference than the prior generation, enabling millions of concurrent agents to run cost-effectively.
4. The Agentic Data Cloud: A new data architecture built for the speed and scale of agentic AI. The Agentic Data Cloud delivers an AI-native architecture, allowing agents to perceive, reason, and act on your behalf in real-time, including:
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Cross-Cloud Lakehouse, standardized on Apache Iceberg, is our Lakehouse that enables you to leave your data in AWS or Azure (coming later this year) while querying it instantly — without the friction of vendor lock-in or the cost of data movement
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Knowledge Catalog constructs a unified, dynamic context graph of your entire business enabling you to ground agents in all of your business data and semantics. With Smart Storage and the Object Context API, files in Google Cloud Storage are instantly tagged and enriched with metadata before an agent touches them. Then our Knowledge Engine uses Gemini to autonomously tag, define logic and instantly map complex relationships across your entire enterprise, providing the semantic definition your agents have been missing.
5. Protecting the agentic enterprise: Security built for the AI era. Our full-stack AI approach, from the chips to the models, gives you a competitive advantage with better integration and velocity to help protect customers. Not only can Google action insights from the world’s largest threat observatory and Mandiant frontline experts, but we also bring cutting-edge insights and breakthroughs from Google DeepMind, to help make your platforms more secure.
- Agentic defense: Three new agents in Google Security Operations can help hunt threats, engineer detections, and provide context on third parties. You can build your own security agents with remote Google Cloud model context protocol (MCP) server support for Google Security Operations, now generally available. You can also access the MCP server client directly from the Google Security Operations chat interface, available in preview.
- Protecting AI and cloud apps across any infrastructure with Wiz: Newly expanded AI coverage helps build secure agents across clouds and AI studios. New AI-Bill of Materials in development tools can help secure AI-generated code and mitigate the risk of shadow AI. Learn more.
- Securing agents and the agentic web: Model Armor can integrate with Agent Gateway, and new Agent Identities provide more layers of defense against shadow AI. Google Cloud Fraud Defense, the next evolution of reCAPTCHA, offers agent-specific capabilities that can help secure the agentic web as well as the entire user and customer journey.
- Trusted Cloud: We’re simplifying permissions with modern IAM, and advancing Google Cloud security with new capabilities in Security Command Center plus new innovations in data and network security.
- New partner-supported workflows for Google Security Operations: This new robust cohort of partner integrations includes partners developing their own agentic security operations centers (SOCs).
You can catch up on all our security announcements from Next ‘26 here.
News you can use
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Guide to prompting Gemini 3.1 Flash TTS (text-to-speech): The new TTS model introduces a high level of controllability by allowing you to steer the delivery using more than 200 audio tags. We'll share how to get strong results from the model, whether you are building accessible gaming soundtracks, banking systems, or audiobooks. Learn more about the model here.
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Ultimate prompting guide for Lyria 3 models: Lyria 3, Google's family of music-generation models, is designed to give you granular control over vocals, instrumentation, and arrangement. So we spent weeks testing against every musical genre and use case we could imagine. We put together this guide to share exactly what we learned and how you can get the best results.
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How to find the sweet spot between cost and performance: This guide will walk you through Google Cloud's flexible gen AI infrastructure options, showing you how to find that sweet spot on the efficient frontier between cost and performance. We'll start with the foundational pay-as-you-go (PayGo) models and then explore how to layer on more specialized options to build a robust and cost-effective gen AI strategy.
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Essential AI and cloud security now on by default: To support the next generation of AI innovators, we are offering on by default essential AI security and cloud security in Security Command Center Standard.
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Securing AI inference on GKE with Model Armor: Here’s how to secure AI inference on Google Kubernetes Engine with Model Armor and high-performance storage.
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Cloud CISO Perspectives: AI, security, and the workforce of the future: You can’t bring traditional security to an AI fight, so how do we defend against AI-powered attacks, boost defenders with AI, and secure AI use? Drop in on this RSA Conference fireside chat between Francis deSouza, Google Cloud COO and President, Security Products, and Nick Godfrey, senior director, Office of the CISO.
Stay tuned for monthly updates on Google Cloud’s AI announcements, news, and best practices. For a deeper dive into the latest from Google Cloud customers, read our monthly recap, Cool stuff customers built.
March
March was a busy month for our AI teams. We launched Gemini Embedding 2, rolled out a highly cost-effective Veo 3.1 Lite model, and officially welcomed the Wiz team to Google Cloud to help redefine security in the AI era.
Alongside these launches, we created comprehensive guides to help you get the most out of these models, from prompting formulas for Nano Banana 2, to practical advice for optimizing your TPU training. Here’s a quick look at the latest news and resources to help your team build what’s next.
Top hits:
- Gemini Embedding 2: Our first natively multimodal embedding model: Gemini Embedding 2 is our first natively multimodal embedding model that maps text, images, video, audio and documents into a single embedding space, enabling multimodal retrieval and classification across different types of media — and it’s available now in public preview.
- Build with Veo 3.1 Lite, our most cost-effective video generation model: This model empowers developers to build high-volume video applications, at less than 50% of the cost of Veo 3.1 Fast, but with the same speed. This rounds out the Veo 3.1 model family, giving developers flexibility based on needs. For Cloud customers, it’s now available on Vertex AI.
Here’s a fun bonus: Check out our ultimate prompting guide for Veo 3.1 to get started.
- Welcoming Wiz to Google Cloud: Redefining security for the AI era: Google has completed its acquisition of Wiz, a leading cloud and AI security platform. The Wiz team will join Google Cloud, and we will retain the Wiz brand. With the addition of Wiz, we will provide customers with a comprehensive platform to secure their cloud and hybrid environments, as well as accelerate threat prevention, detection, and response.
- Gemini 3.1 Flash Live: Making audio AI more natural and reliable: We’ve improved 3.1 Flash Live’s overall quality, making it more reliable for developers and enterprises to build voice-first agents that can complete complex tasks at scale. On ComplexFuncBench Audio, a benchmark that captures multi-step function calling with various constraints, it leads with a score of 90.8% compared to our previous model.
News you can use:
- The ultimate Nano Banana prompting guide: This is a must-read for anyone working with Nano Banana. We spent weeks testing Nano Banana 2 and Nano Banana Pro against every use case we could imagine to test its limits. We put together this guide to share exactly what we learned and how you can get the best results. Here’s an example formula: [Reference images] + [Relationship instruction] + [New scenario]
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- A developer’s guide to training with Ironwood TPUs: In this guide, we hear from Lillian Yu, CPA, CA , Product Strategy and Operation, and Liat Berry, Product Manager, on five strategies within the JAX and MaxText ecosystems designed to help developers refine training efficiency and hit peak performance on Ironwood hardware.
- How to build production-ready AI agents with Google-managed MCP servers: In this guide, we anchor on a specific example. Cityscape is a demo agent built with Google's Application Development Kit (ADK) that turns a simple text prompt — like "Generate a cityscape for Kyoto" — into a unique, AI-generated city image. Check out the guide to learn more.
Stay tuned for monthly updates on Google Cloud’s AI announcements, news, and best practices. For a deeper dive into the latest from Google Cloud customers, read our monthly recap, Cool stuff customers built.
February
In February, we’re giving developers more reasoning power with Gemini 3.1 Pro and Claude 4.6, and faster creative scaling with Nano Banana 2. We’re also opening up new training programs and step-by-step guides to help you tackle the hardest parts of the AI lifecycle, from capacity planning to mounting defenses against AI-powered attacks.
Here’s a rundown of our latest news, tools, and resources to help you build what’s next.
Top hits
- Pro-level image generation gets faster and more accessible with Nano Banana 2: To build creative that stands out, you need models that naturally integrate into your workflows and scale with ease. Check out our blog to see how this comes to life (and how customers are putting the model to work).
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- Introducing Gemini 3.1 Pro on Google Cloud: Gemini 3.1 Pro is a clear step forward in reasoning, designed to solve tougher problems, giving you the reasoning depth your business needs. 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.
- Announcing Claude Opus 4.6 and Claude Sonnet 4.6 on Vertex AI: Now generally available on Vertex AI, explore our sample notebook to get started and visit our documentation for comprehensive pricing and regional availability details.
- New AI threats report: Distillation, experimentation, and integration: John Hultquist, chief analyst, Google Threat Intelligence Group, details what security leaders should know from our newest AI threat report on experimentation, integration, and distillation attacks.
News you can use
- A developer's guide to production-ready AI agents: To help developers work through these challenges, we've published a collection of guides covering the full agent lifecycle. These resources first appeared during Kaggle’s 5 days of AI Agents Intensive, and they’ve proven so popular and useful, we wanted to make sure a wider audience had access, as well.
- Gemini Enterprise Agent Ready (GEAR) program now available: We opened the Gemini Enterprise Agent Ready (GEAR) learning program to everyone. As a new specialized pathway within the Google Developer Program, GEAR empowers developers and pros to build and deploy enterprise-grade agents with Google AI.
- Your guide to Provisioned Throughput (PT) on Vertex AI: Check out this deep-dive blog designed to show you the resources available to you today on Vertex AI, and how you can get started capacity planning.
- How AI can boost defenders, from defense in depth to the cyber kill chain (Q&A): We know that defenders are also developing powerful AI tools, but what’s still unknown is what it could mean for enterprise software ownership if companies have to constantly mount AI-directed defenses at AI-powered attacks?
Stay tuned for monthly updates on Google Cloud’s AI announcements, news, and best practices. For a deeper dive into the latest from Google Cloud customers, read our monthly recap, Cool stuff customers built.
Janurary
We used to have to learn the language of computers. In 2026, they’re learning ours.
We kicked off the year by exploring the future of agentic commerce, where AI agents navigate the web to find and buy products for us. Our leaders call this the "invisible shelf" — a world where commerce isn't tied to a specific website. To make this reality scalable, we announced the Universal Commerce Protocol (UCP), a shared language that allows agents and retailers to understand each other.
We brought that same fluency to our creative and technical tools:
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Updates to Veo 3.1 allow creators to use simple inputs — like reference images — to generate precise, mobile-ready video.
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Natural language queries: With Comments to SQL in BigQuery, we’re removing the language barrier to data. Engineers can now write queries by describing their intent in natural language, prioritizing the question over the code.
Let’s dive in.
Top hits
1. Gemini Enterprise for Customer Experience (CX): Specifically built for agentic retail, this platform transforms fragmented search, commerce and service touch points into one seamless journey — whether you need a shopping assistant, a support bot, agentic search or help with merchandising.
2. We announced Universal Commerce Protocol (UCP): A new open standard for agentic commerce that works across the entire shopping journey — from discovery and buying to post-purchase support. UCP establishes a common language for agents and systems to operate together across consumer surfaces, businesses and payment providers. So instead of requiring unique connections for every individual agent, UCP enables all agents to interact easily. UCP is built to work across verticals and is compatible with existing industry protocols like Agent2Agent (A2A), Agent Payments Protocol (AP2) and Model Context Protocol (MCP).
3. We updated Veo 3.1, including improvements to Ingredients to Video and Portrait mode: Veo is getting more expressive, with improvements that help you create more fun, creative, high-quality videos based on ingredient images, built directly for the mobile format. This includes:
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Improvements to Veo 3.1 Ingredients to Video, our capability that lets you create videos based on reference images.
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Native vertical outputs for Ingredients to Video (portrait mode) to power mobile-first, short-form video creation.
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State-of-the-art upscaling to 1080p and 4K resolution 1 for high-fidelity production workflows.
These updates are launching in the Gemini app, YouTube, Flow, Google Vids, the Gemini API and Vertex AI.
4. Vibe querying with comments-to-SQL: Crafting complex SQL queries can be challenging. Often, engineers simply want to express their data needs in plain English directly within their SQL workflow. That’s why we’re introducing Comments to SQL in BigQuery. This feature makes writing queries using natural language – ‘vibe querying’ – a reality. Learn more in the blog.
News you can use
- Mastering Gemini CLI: Your complete guide from installation to advanced use-cases: We’ve teamed up with DeepLearning.ai and are excited to announce a free course – Gemini CLI: Code & Create with an Open-Source Agent. This course isn’t just for developers; we dive into practical use cases for various tasks such as data analysis, content creation, and personalized learning.
- How Google SREs use Gemini CLI to solve real-world outages: In this article, we’ll delve into real scenarios that Google SREs are solving today using Gemini 3 (our latest foundation model) and Gemini CLI—the go-to tool for bringing agentic capabilities to the terminal.
- Getting started with Gemini 3: Deploy your first Gemini 3 app to Google Cloud Run: In this blog, we will show you how to vibe code your first app—which leverages the Gemini 3 Flash Preview model and deploy it as a publicly accessible URL on Google Cloud Run. Google AI Studio lets you go from idea to app quickly by using natural language to generate fully functional apps using the power of Gemini 3.
- Practical guidance: Building with the Secure AI Framework (SAIF) on Google Cloud: We know that security and data privacy are the top concern for executives when evaluating AI providers, and security is the top use case for AI agents in a majority of industries. To help you build AI boldly and responsibly, here’s our guide to developing AI with the Secure AI Framework (SAIF) on Google Cloud.
- The truths about AI hacking that every CISO needs to know (Q&A): How will AI boost threat actors? And what can chief information security officers do about it? Google’s Heather Adkins, vice-president, Security Engineering, explores how securing the enterprise is about to change.
Stay tuned for monthly updates on Google Cloud’s AI announcements, news, and best practices. For a deeper dive into the latest from Google Cloud customers, read our monthly recap, Cool stuff customers built.