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Google named a Leader in the 2026 IDC MarketScape for Worldwide Foundation Model Software

For years, we’ve built with a clear priority: putting the practical needs of the enterprise first. Long before generative AI dominated the headlines, we were focused on building the global infrastructure, security frameworks, and data platforms that power the world’s largest organizations. We’ve always believed that technology is only as good as its reliability, security, and predictability in production. By anchoring our frontier research to this enterprise foundation, we can deliver models built specifically for business impact. We believe that approach is why Google has been named a Leader in the IDC MarketScape: Worldwide Foundation Model Software 2026 Vendor Assessment1 and highlights our history of turning cutting-edge frontier research into secure, production-grade systems that developers can deploy at scale. We believe Google’s position as a Leader highlights the exact momentum we are seeing in the market and validates the unique strength of our integrated, first-party AI stack. By translating Google DeepMind’s continuous pipeline of fundamental research into production-grade business products, we unite our robust infrastructure and foundation model software to work together seamlessly as a single, unified solution. Gemini Enterprise: A unified system for the agentic era A great foundation model is only as valuable as an organization’s ability to safely put it to work. In the enterprise, that value is realized when models are given the tools, memory, and agency to act as autonomous partners – moving from simple prompt-and-response text to dynamic agents that can execute complex business workflows. Gemini Enterprise serves as this end-to-end system. It brings our most powerful developer capabilities and user-facing tools together into a single architecture, featuring the Gemini Enterprise app as the front door for everyday business teams to interact with AI, and the Gemini Enterprise Agent Platform for developers to orchestrate them behind the scenes. Agent Platform abstracts away the underlying complexity of how technical teams build, scale, govern, and optimize these agents – whether they are handling customer-facing workflows or managing internal operations. Any agent engineered on the platform can be instantly surfaced in the Gemini Enterprise app, giving your workforce immediate access to secure, custom-built tools. Because rigorous governance, enterprise security, and cryptographic identity are baked into the foundation by default, organizations can stop worrying about managing technological risk and start focusing entirely on driving agent-led business outcomes. Powered by Gemini Our latest models are built specifically to orchestrate and execute complex, multi-step actions. At I/O this year, we kicked off the Gemini 3.5 series with the release of Gemini 3.5 Flash. It delivers intelligence on multiple dimensions at speeds you have come to expect from the Flash series. It’s ideal for tackling long-horizon agentic tasks. Google DeepMind engineered these models from the ground up using our purpose-built AI infrastructure. This unique co-design of the model and hardware allows us to train deeper reasoning capabilities faster and more efficiently with every new generation. Developers can build agents using Gemini 3.5 Flash on the Gemini Enterprise Agent Platform, or use it in your projects in Google AI Studio and Antigravity. Business users can use Gemini 3.5 Flash in the Gemini Enterprise app to help discover, create, and use the best of Google AI in their workflows starting today. Get started Download the IDC MarketScape: Worldwide Foundation Model Software 2026 Vendor Assessment excerpt to learn why organizations are choosing Google Cloud. Explore Gemini Enterprise today, or speak to your Google Cloud account representative to schedule a hands-on technical workshop. 1. IDC MarketScape: Worldwide Foundation Model Software 2026 Vendor Assessment, Doc #US54427726, July 2026 IDC MarketScape vendor analysis model is designed to provide an overview of the competitive fitness of technology and suppliers in a given market. The research methodology utilizes a rigorous scoring methodology based on both qualitative and quantitative criteria that results in a single graphical illustration of each supplier’s position within a given market. The Capabilities score measures supplier product, go-to-market and business execution in the short-term. The Strategy score measures alignment of supplier strategies with customer requirements in a 3-5-year timeframe. Supplier market share is represented by the size of the icons.

BY Michael Gerstenhaber
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Google named a Leader in the 2026 IDC MarketScape for Worldwide Foundation Model Software
Claude at scale on Google Cloud: Frontier AI, built for enterprise production
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Claude at scale on Google Cloud: Frontier AI, built for enterprise production

Running frontier AI in production is demanding — accelerators to manage, latency to hold steady across continents, regulated data to keep in-region, and long-context requests to serve reliably. Claude on Google Cloud is built for exactly this. Like Monet and water lilies, frontier models and the enterprise platforms are often better together. In our case, Claude brings the reasoning, and Google Cloud brings the managed infrastructure, global reach, and compliance posture that enterprises already run on. Calling Claude becomes operationally identical to calling any other Google Cloud service — same Identity and Access Management (IAM), same VPC Service controls, same observability — so teams are able to spend their time building features instead of running inference infrastructure. This post walks through what Claude on Google Cloud delivers in production across four areas: Managed infrastructure that gives engineers their time back Global endpoints that hold latency low, and uptime high for a worldwide user base Security and data-sovereignty controls inherited straight from Google Cloud Serving-layer features that keep cost and performance optimized at scale. Managed infrastructure that frees engineering time Claude on Google Cloud runs on fully managed infrastructure, so enterprise teams ship features instead of building clusters. Compute provisioning, auto-scaling logic, load balancing, and failover at frontier-model scale are handled by the platform — work that would otherwise occupy multiple teams full-time. Claude is available through Agent Platform’s Model Garden as a Model-as-a-Service offering, ready to use over standard REST / JSON over HTTP/1.1 or HTTP/2 endpoints. Invoking Claude is operationally identical to invoking any other Google Cloud service: the same IAM policies, the same VPC controls, and the same observability stack via Cloud Logging and Cloud Monitoring. Serving Claude takes a few lines of Python using the AnthropicVertex client: code_block <ListValue: [StructValue([(‘code’, ‘from anthropic import AnthropicVertex\r\n\r\nclient = AnthropicVertex(\r\n project_id=“your-project-id”,\r\n region=“us”\r\n)\r\n\r\nmessage = client.messages.create(\r\n model=“claude-opus-4-8”,\r\n max_tokens=1024,\r\n messages=[{“role”: “user”, “content”: “Analyze this system architecture.”}]\r\n)’), (’language’, ‘’), (‘caption’, <wagtail.rich_text.RichText object at 0x7fbfc31777c0>)])]> The same AnthropicVertex client handles prompt caching, tool use, structured outputs, streaming, and adaptive thinking; for batch inference, use Vertex AI Batch Prediction. Authentication uses Application Default Credentials; requests automatically inherit your project’s IAM and VPC configuration. Global reach with consistent latency and built-in failover Serving a worldwide user base from a single endpoint produces high tail latency and a single point of failure. Most enterprises can’t replicate inference infrastructure across continents while keeping performance consistent. Agent Platform exposes three endpoint types for Claude, each solving a different production requirement: Global endpoints route requests to a region with available AI compute capacity. For example, if us-central1 is capacity-constrained, traffic redirects to europe-west1 or another region with available capacity. That’s automatic failover and geographic load balancing without application-side routing logic. Global endpoints are ideal for maximum availability and lowest cost. Regional endpoints like us-east5 or europe-west1 keep prompts, completions, and intermediate state inside a specific geographical boundary, making it ideal for low latency and data-residency requirements. Multi-region endpoints give U.S. or EU data residency without single-region dependency. They dynamically route across regional endpoints providing built-in resilience against regional outages and capacity constraints. The diagram below shows how applications reach Claude through these endpoint types, and how the Agent Platform serving layer routes traffic to the Compute AI clusters across regions: Serving Claude Models From Regional & Global Endpoints Serving Claude Models From Multi-Region Endpoints Serving Claude Models From Regional Endpoints Enterprise security and data sovereignty built in Regulated workloads — financial services, healthcare, and government — get enterprise-grade security and data sovereignty without trading compliance for convenience, and without re-engineering the hardest layer to control: inference, where prompts, completions, and intermediate state all flow through the serving stack. Claude on Agent Platform inherits Google Cloud’s full security posture. FedRAMP High and HIPAA compliance enable deployment in government, healthcare, and financial services environments. VPC Service Controls let organizations define a perimeter around Agent Platform resources, preventing data exfiltration. IAM-native access control governs Claude endpoints with the same roles and policies that protect every other Google Cloud resource — no separate API keys to manage or rotate. Cloud Logging and Cloud Monitoring provide near real-time visibility into token usage, error rates, latency, and quota consumption. Combined with the regional and multi-region endpoints above, this gives regulated customers a path to running frontier AI in production without re-auditing their compliance posture. Optimized for cost and performance at scale In production, cost and performance drive every architectural decision. Getting both right requires capabilities from two layers: Claude’s native model features, and Google Cloud’s serving infrastructure. Agent Platform supports both, so teams can optimize across the stack without managing them separately. Claude-native capabilities, fully supported on Agent Platform These features are built into Claude and available on Agent Platform without any additional configuration: Prompt caching stores and reuses shared prefixes — long system prompts, legal documents, codebases — reducing request latency by up to 80% and cost by up to 90%. Streaming responses over server-sent events deliver tokens as they are generated, critical for chat interfaces and coding assistants where perceived latency matters. Extended and adaptive thinking lets Claude dynamically determine when and how much to reason through complex, multi-step problems — and allows users to dial the thinking effort directly, for example to control cost. Optimized for use cases like advanced code generation, mathematical reasoning, and multi-document analysis. Extended context windows up to 1M tokens (for Claude Opus 4.6,Sonnet 4.6 and newer models) enable long-document analysis, large codebase reasoning, and multi-turn conversations at depth. Google Cloud serving infrastructure Agent Platform adds its own serving-layer capabilities on top of Claude’s native features: Batch prediction handles large-scale offline workloads — document classification, content moderation, bulk summarization — asynchronously at lower priority and reduced cost. Provisioned throughput reserves dedicated inference capacity for mission-critical workloads, isolating them from public traffic and ensuring predictable performance during peak demand. Memory management and scheduling for long-context requests is handled at the infrastructure layer,. Together, these two layers give teams the full range of optimization levers — from model-level efficiency to infrastructure-level capacity control — on a single, unified platform. From inference to agents The same infrastructure that serves Claude inference powers the agent layer of Agent Platform on Google Cloud. The build-and-register flow has three steps: Build with Claude. Claude is well-suited as an orchestration backbone — its extended context window, native tool use, and adaptive thinking make it effective at planning multi-step tasks and delegating to sub-agents. Pick Claude Opus, Sonnet, or Haiku from the Model Garden, then build with the Agent Development Kit (ADK) — code-first in Python, Go, Java, or TypeScript — deploy to Agent Runtime, Cloud Run or Google Kubernetes Engine. Deploy the Agent to a Runtime. Depending on your use case, select Agent Runtime, Google Kubernetes Engine or GKE Agent Sandbox to run your deployed agents. Interoperate over A2A. The Agent2Agent protocol runs at 150+ organizations, letting a registered Claude-powered agent delegate tasks to agents from SaaS and other service providers. The result: a planning agent built on Claude can orchestrate sub-tasks across the broader agent ecosystem, under unified IAM, fully auditable, on the same infrastructure that serves the underlying inference. Start building Open the Agent Platform console, enable Claude in the Model Garden, and make your first API call with the AnthropicVertex SDK. Add prompt caching, provisioned throughput, and other features as your workload demands. When you’re ready to go agentic, learn more about Claude on Agent Platform. Reach out to your Google Cloud sales representative to discuss bringing Claude into your production environment at scale.

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Google and Industry Partners Announce Agentic Resource Discovery Specification for AI Agents
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Google and Industry Partners Announce Agentic Resource Discovery Specification for AI Agents

Google and industry partners announced Agentic Resource Discovery (ARD) Specification, an open standard for publishing, discovering, and verifying AI tools, APIs, and agents. ARD introduces a discovery layer built on catalogs and registries, enabling dynamic capability discovery while leveraging existing protocols such as MCP and OpenAPI for execution and emphasizing trust and interoperability. By Leela Kumili

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Is a Pod the right deployment unit for an AI agent?
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Is a Pod the right deployment unit for an AI agent?

When we first started building kagent, we didn’t run every agent in its own Kubernetes Pod, Service, and ServiceAccount. Instead, agents were simply executed inside the kagent runtime. It was the simplest architecture possible: one runtime…

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Google's Genkit Ships Agents API with Detached Turns and Human-in-the-Loop for TypeScript and Go
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Google's Genkit Ships Agents API with Detached Turns and Human-in-the-Loop for TypeScript and Go

Google released the Genkit Agents API in preview for TypeScript and Go. The open-source framework packages message history, tool loops, streaming, and state persistence behind a single chat() interface. Detached turns let agents work after clients disconnect. Interruptible tools provide human-in-the-loop control with anti-forgery validation on resume. By Steef-Jan Wiggers

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Accessing private Git repositories from Amazon EKS capability for Argo CD
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Accessing private Git repositories from Amazon EKS capability for Argo CD

In this post, we walk you through three main steps: First, you create an AWS CodeConnections host in your VPC with connectivity to your private Git server. Second, you establish a connection that Argo CD can use. Finally, you deploy a sample application to verify the integration. By the end, you have a secure way to deploy applications from private repositories.

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Building the AI-defined vehicle with Android, Google Cloud, and Nexus SDV
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Building the AI-defined vehicle with Android, Google Cloud, and Nexus SDV

The automotive industry is moving from building hardware-centric platforms toward building their own sophisticated Software-Defined Vehicle (SDV) architectures. For OEMs, a vehicle is no longer just a way to go from point A to point B, but an intelligent, connected node within an AI-native ecosystem! With its partners, Google’s Android and Google Cloud are at the forefront of this transition. Android’s open source Automotive OS (AAOS) SDV implements the AI-defined vehicle while Google Cloud provides scalable infrastructure including a full suite of AI integration tools, leveraging services like Bigtable for automotive and manufacturing telematics at scale. Valtech, a Google Cloud partner, uses Google technologies as part of its Nexus SDV platform, establishing a full end-to-end connected vehicle system that enables truly agentic mobility, offering automotive OEMs a ready-to-use, end-to-end foundation for the next generation of connected vehicles. Let’s take a look at how this all comes together. The vehicle side: AAOS SDV As the foundational in-vehicle platform, Google’s open source AAOS SDV platform abstracts core functions into reusable services independent of physical hardware, establishing a modular Service-Oriented Architecture (SOA). By decoupling non-safety domains like climate control, lighting, and diagnostics from Electronic Control Units (ECUs), the AAOS SDV platform introduces dynamic runtime service discovery. With this, the SDV can easily discover what services are running (e.g., the odometer, HVAC, sunroof, motorized seats, electric windows, etc.) and their status. To accelerate development, engineering teams leverage the Android Cuttlefish emulator to build digital twins in the cloud, simulating high-frequency sensor streams to validate these decoupled services bit-for-bit before physical silicon is ready. Valtech Nexus SDV utilizes this AAOS SDV middleware layer to discover, map, and manage vehicle resources, structuring and streaming high-frequency telemetry data straight into Bigtable. Compare this to the prior state of affairs, where OEMs outsourced system software to a variety of suppliers, each with their own pipelines, protocols, and data stored in separate silos. Crucially, this model decouples services from the heavy main infotainment stack, so they can run independently, even when the vehicle is off and parked. This allows functions like remote vehicle monitoring to remain active even when the primary infotainment system is powered down, ensuring continuous telemetry access without draining the vehicle’s 12V battery or main EV battery pack. This tight integration between the AAOS SDV platform and Nexus SDV enables a number of agentic AI and innovative first-party solutions. Unlike traditional sandboxed infotainment tools, multimodal AI agents can utilize the service discovery layer to safely interact with the physical car and process complex, intent-based requests. For example, an AI agent could automatically adjust climate zones, window actuators, or interior lighting based on a conversation with the driver, or in response to climate sensors, as in this clip: By linking this on-vehicle service layer managed by Nexus SDV with historical fleet telemetry stored in Bigtable, you deliver deeply integrated experiences that unlock new mobility solutions. Now let’s take a quick look at the Cloud side. The Google Cloud side: AI-native mobility Beyond SDV, we are rapidly moving toward AI-defined vehicles, or AIDV, where AI is core to a vehicle’s operational logic. To be AI-native means being autonomous by design, with AI embedded at every architectural level. With this level of AI, the system can perceive environments, reason through complex scenarios using engines like Google Gemini, and proactively execute actions. For example, a Gemini-powered vehicle doesn’t just warn you that you’re low on power; it analyzes your schedule, traffic, and charger availability to suggest an optimized charging stop that pre-conditions the battery for maximum efficiency. This is the level of contextual understanding and proactive automation that characterizes AIDV. Compare this to legacy architectures, which weren’t designed to capture the volume and variety of data coming from different systems across the vehicle. This can lead to data silos of isolated maintenance and safety information telematics. Moreover, because this data is fragmented, it can be very difficult to get cohesive value from the data across systems. An AI-native approach can help collapse these silos, providing a unified contextual understanding. This solves a primary OEM pain point: the massive complexity of managing high-bandwidth telemetry from multiple sources like SDV telematics. Bigtable: The data backbone for Automotive Telemetry Bigtable was purpose-built for the massive ingestion rates and sub-millisecond latency requirements, and serves as the data backbone for petabyte-scale automotive and manufacturing telemetry datasets. In fact, Bigtable is already being used to support business critical automotive telemetry solutions. Its flexible, sparse-row schema allows OEMs to evolve their data models without downtime, accommodating diverse sensor arrays — from high-frequency engine metrics to LiDAR point clouds — within a single, unified table structure. Then, by versioning time-series events in a way that is natively optimized for both massive writes and complex, multi-dimensional analytical lookups, Bigtable helps avoid the data overload typical of legacy systems. Meanwhile, features like Continuous Materialized Views (CMV) allow for pre-calculating key metrics, such as average battery temperature or fleet-wide torque distributions, directly within the storage layer, minimizing computational overhead. Bigtable’s integration with Agent Development Kit (ADK) further bridges the gap between data and action by giving AI agents access to data. This kit combined with Bigtable’s integrations with frameworks like Apache Spark help monitor the “firehose” of live telemetry data and trigger automated workflows in real time, e.g., logging mission-critical alerts, initiating proactive over-the-air (OTA) software adjustments, or pre-ordering replacement parts, the moment specific degradation patterns are detected. Bring it all together: Nexus-SDV platform The Nexus SDV platform is built on Google Cloud and integrated with AAOS SDV, supporting the future of connected vehicles. By providing a standardized data foundation, Nexus empowers automotive OEMs to go beyond building infrastructure from scratch and start focusing on unique brand experiences. Nexus SDV uses Google components like Gemini Enterprise Agent Platform, Bigtable, and BigQuery. Setting up Nexus SDV is quick, automated and transparent. OEMs can create brand-specific customer experiences in the vehicle, as well as in other customer touch points such as the UI screen, mobile app, or service centers. The connection to the vehicle is accomplished by leveraging the open source Synadia NATS interface. This integration with the vehicle is facilitated through simple Cloud and vehicle SDKs, for service discovery on both sides. Nexus SDV is optimized for AAOS SDV, but can integrate with any vehicle framework. Security is woven into the Nexus architecture via a “Defense-in-Depth” model. Mutual TLS (mTLS) and Google Cloud Certificate Authority Service (CAS) provide vehicles with a cryptographically secure identity. Network isolation is maintained through Private GKE clusters, while the Secure AI Framework (SAIF) helps ensure data privacy throughout the machine learning lifecycle, protecting sensitive user data and OEM intellectual property. Together, the quick setup and integration time coupled with a standardized data foundation and built-in state-of-the-art security leads to an immediate and measurable business impact for the car manufacturer. Let’s put it all together and look at a use case in more detail… Predictive maintenance By moving from reactive to predictive maintenance, OEMs can reduce warranty costs, improve customer loyalty, and ensure higher vehicle uptime. The challenge: Traditional scheduled maintenance is often inefficient, leading to unnecessary service visits or unexpected vehicle breakdowns that incur significant costs for both OEMs and owners. By moving to a proactive, AI-driven approach, Nexus SDV, Bigtable, and ADK transform this experience. The process begins by taking the firehose of vehicle telemetry data —monitoring engine RPM, vibration, fluid levels, brake pressure, and more — ingesting it and storing it directly into Bigtable. To enable real-time anomaly detection, agentic AI can monitor telemetry streams as they arrive. Bigtable CMVs pre-calculate rolling aggregations such as average engine vibration or sudden fluctuations in battery temperature profiles. AI models consuming these live aggregates can then detect subtle deviations from normal parameters, identifying early signs of engine wear or accelerated battery degradation long before a warning light appears on the dashboard. Once an anomaly is detected by specialized AI models, the system shifts into the agentic reasoning and action phase. A Gemini-powered engine assesses the severity and context of the data, considering factors like mileage, model, make, service history, and upcoming trips. Based on this intelligent assessment, the system can proactively notify the driver via the AAOS infotainment system, suggests an optimized service appointment at a nearby dealership, or can even trigger an automated parts order to ensure everything is ready upon arrival. The AI model works against false negatives to protect customer sentiment or erosion of confidence, while the solution as a whole ensures higher vehicle uptime, transforming maintenance from a reactive burden into a brand-defining service experience. Get started The AI-native Nexus SDV platform with AAOS SDV is available today, providing a sophisticated, end-to-end connected vehicle ecosystem designed to meet the extreme scale and analytical rigors of modern mobility. By adopting this unified, open-source architecture, OEMs can transcend the limitations of legacy infrastructure and redirect their resources toward the development of high-impact, brand-defining features. Nexus SDV takes the connected vehicle service into the agentic era, where vehicles are no longer merely connected, but serve as intelligent, proactive partners in the driving experience. Give it a try today. Learn more If you’d like to learn more about Nexus SDV platform, AAOS SDV and Bigtable contact us today at nexus-sdv@google.com. AAOS SDV is available in the Android Automotive 26Q2 release. Nexus SDV documentation can be found here. Go here to learn more about Bigtable as the time-series database for automotive telemetry. Thinking about your connected vehicle security, check this out, Shift into high gear with agents: Securing the software-defined vehicle.

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Key findings from the 2026 Public Sector M-Trends report and beyond
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Key findings from the 2026 Public Sector M-Trends report and beyond

In 2026, the public sector is no longer defending a traditional perimeter. Instead, they are defending a complex web of interconnected trust relationships against adversaries that now operate at machine speed. We recently published the 2026 Public Sector Threat Landscape: M-Trends and Beyond report, which distills more than 500,000 hours of frontline incident investigations conducted by Mandiant in 2025, specifically tailored to the mission-critical needs of public sector leaders. Key findings from the report and what they mean for the public sector The most alarming trend in this year’s M-Trends data is the 22-second hand-off: the median time between an initial access broker establishing a foothold and the hand-off to a ransomware operator. This extreme compression of the attack cycle renders traditional, human-speed triage obsolete. When an infection on a municipal workstation can move to an encrypted network before a human analyst can even open a ticket, the strategic mandate for resilience must pivot toward machine-speed defense. Additionally, the report uncovered several emerging “boundaries of trust” that adversaries are systematically exploiting: The persistence paradox: State-sponsored espionage actors are pursuing multi-year persistence, with some remaining undetected for over five years. This “persistence paradox” directly challenges standard 90-day telemetry retention policies, often leaving agencies unable to quantify the full impact of a breach. The virtualization stack: Attackers are moving “down the stack” to target the virtualization management plane. Techniques like “snapshot mounting” allow attackers to bypass guest-level security tools, creating snapshots of domain controllers to steal databases offline. The SaaS domino effect: At the state and local levels, the reliance on third-party cloud tools has turned integrations into threat vectors. Exploiting non-human identities (NHIs) like service accounts and OAuth tokens allows a single compromise to trigger a chain reaction across an entire agency network. The vishing surge: Voice phishing (vishing) has surged to 11% of global infections. These highly effective social engineering attacks target government help desks to reset passwords or enroll unauthorized devices. This proves that the ‘human element’—the administrative trust placed in help desk staff and IT administrators—is now a primary vector for establishing initial access. A mandate for continuous verification Looking ahead, resilience in the public sector will require more than a compliance checklist; it demands a cultural pivot to continuous verification—a security doctrine where trust is never assumed and must be constantly re-validated. Success is no longer just defined by the absence of a breach, but also by an agency’s ability to remain operational while under active attack. At Google, we provide the technical architecture to make continuous verification a reality through three core capabilities. Identity as the new perimeter: Through Chrome Enterprise Premium, we replace traditional VPNs with context-aware access. We verify the user’s identity and the security posture of their device for every single application request, ensuring that access is only granted under the right conditions. Agentic defense: We enable agencies to ingest and analyze massive telemetry datasets in real-time using Google Security Operations, which includes threat-centric case management, interactive, context-rich alert graphing, and automatic stitching together of entities. This allows for the “Machine-Speed” detection required to spot an adversary within the 22-second hand-off window, turning manual triage into automated, continuous monitoring. To stay ahead of these rapid shifts, this operational stack is directly infused with Google Threat Intelligence, exposing global actor infrastructure and matching internal telemetry with Mandiant’s frontline incident insights in real time. At Google Cloud Next ‘26, we announced three new AI-powered autonomous agents within Google Security Operations: a Threat Hunting agent to proactively unearth hidden attack patterns, a Detection Engineering agent to automatically close telemetry coverage gaps, and a Third-Party Context agent to seamlessly enrich analyst workflows. Hardened infrastructure: By moving “down the stack” with Security Command Center and leveraging our strategic partnership with Wiz, we offer deep visibility into the virtualization and cloud layers. This allows agencies to continuously verify the integrity of their hypervisors and cloud configurations, automatically detecting unauthorized “Snapshot Mounting” or configuration drifts that adversaries exploit for persistence. By hardening the administrative fabric—including identity and virtualization—and modernizing log retention to close the visibility gap, government leaders can move from a state of reactive triage to a future of context-aware resilience. Google security in action Google’s security technology comes to life across the public sector, where agencies are successfully shifting from manual triage to agentic defense, and accelerating their security transformation. The Pasco Sheriff’s Office transformed its security and operations, unifying siloed tools with Google Security Operations to boost efficiency, improve community safety, and champion secure AI for law enforcement. Meanwhile, the State of Connecticut moved from a fragmented operating model to a unified, proactive security posture using Google Security Operations to reduce forensic investigation times from months to mere hours and create a secure-by-design digital infrastructure for the future of public service. Secure your future Download the 2026 Public Sector Threat Landscape: M-Trends and Beyond report to explore the data and strategic recommendations for the latest insights and trends and what they mean for the public sector. Catch the replay of our Gemini for Government webinar to dive deeper into securing and governing an agent.

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