Cloud

At Google Cloud, we believe that being at the forefront of driving secure innovation and meeting the evolving needs of customers includes working with partners. The reality is that the security landscape should be interoperable, and your security tools should be able to integrate with each other. 

Google Unified Security, our AI-powered, converged security solution, has been designed to support greater customer choice. To further this vision, today we’re announcing Google Unified Security Recommended, a new program that expands strategic partnerships with market-leading security solutions trusted by our customers.

We welcome CrowdStrike, Fortinet, and Wiz as inaugural Google Unified Security Recommended partners. These integrations are designed to meet our customers where they are today and ensure their end-to-end deployments are built to scale with Google in the future.

Building confidence through validated integrations

As part of the Google Unified Security Recommended program, partners agree to adhere to comprehensive technical integration across Google’s security product portfolio; a collaborative, customer-first support model that reflects our intent to collectively protect our customers; and invest jointly in AI innovation. This program offers our customers:

• Enhanced confidence: Select partner products that have undergone evaluation and validation to ensure optimal integration with Google Unified Security.

• Accelerated discovery: Streamline your evaluation process with a carefully curated selection of market-leading solutions addressing specific enterprise challenges.

• Prioritize outcomes: Minimize integration overhead, allowing your team to allocate resources towards building security solutions that deliver business outcomes.

We’re working to ensure that customers can use solutions that are powerful today — and designed for future advancements. Learn more about the product-level requirements that define the Google Unified Security Recommended designation here. 

Our inaugural partners: Unifying your defenses

Our collaborations with CrowdStrike, Fortinet and Wiz exemplify our “better together” philosophy by addressing tangible security challenges.

CrowdStrike Falcon (endpoint protection): Integrations between the AI-native CrowdStrike Falcon® platform, Google Security Operations, Google Threat Intelligence, and Mandiant Threat Defense can enable customers to detect, investigate, and respond to threats faster across hybrid and multicloud environments. 

Customers can use Falcon Endpoint risk signals to define Context-Aware access policies enforced by Google Chrome Enterprise. The collaboration also supports integrations that secure the AI lifecycle — and extends through the model context protocol (MCP) to advance AI for security operations. Together, CrowdStrike and Google Cloud deliver unified protection across endpoint, identity, cloud, and data.

“CrowdStrike and Google Cloud share a vision for an open, AI-powered future of security. Together, we’re uniting our leading AI-native platforms – Google Security Operations and the CrowdStrike Falcon® platform – to help customers harness the power of generative AI and stay ahead of modern threats,” said Daniel Bernard, chief business officer, CrowdStrike.

Fortinet cloud-delivered SASE and Next-Generation Firewall (network protection): Integrating Fortinet’s Security Fabric with Google Security Operations combines AI-driven FortiGuard Threat Intelligence with rich network and web telemetry to deliver unified visibility and control across users, applications, and network edges. 

Customers can integrate FortiSASE and FortiGate solutions into Google Security Operations to correlate activity across their environments, apply advanced detections, and automate coordinated response actions that contain threats in near real-time. This collaboration can help reduce complexity, streamline operations, and strengthen protection across hybrid infrastructures.

“Customers are demanding simplified security architectures that reduce complexity and strengthen protection,” said Nirav Shah, senior vice president, Product and Solutions, Fortinet. “As an inaugural partner in the Google Cloud Unified Security Recommended program, we are combining the power of FortiSASE and the Fortinet Security Fabric with Google Cloud’s security capabilities to converge networking and security across environments. This approach gives SecOps and NetOps shared visibility and coordinated controls, helping teams eliminate tool sprawl, streamline operations, and accelerate secure digital transformation.”

Wiz (multicloud CNAPP): Customers can integrate Wiz’s cloud security findings with Google Security Operations to help teams identify, prioritize, and address their most critical cloud risks in a unified platform. 

In addition, Wiz and Security Command Center integrate to provide complete visibility and security for Google Cloud environments, including threat detection, AI security, and in-console security for application owners. Wiz is actively developing a new Google Threat Intelligence (GTI) integration that allows existing GTI customers to access threat intelligence seamlessly in the Wiz console, enabling threat intelligence-driven detection and response processes.

“Achieving secure innovation in the cloud requires unified visibility and radical risk prioritization. Our inclusion in the Google Unified Security Recommended program recognizes the power of Wiz to deliver code-to-cloud security for Google Cloud customers. By integrating our platform with Google Security Operations and Security Command Center, we enable customers to see their multicloud attack surface, prioritize the most critical risks, and automatically accelerate remediation. Together, we are simplifying the most complex cloud security challenges and making it easier for you to innovate securely,” said Anthony Belfiore, chief strategy officer, Wiz.

Powering the agentic SOC with MCP

A critical aspect of Google Unified Security Recommended is our shared dedication to strategic AI initiatives, including MCP support. Because it enables AI models to interact with and use security tools, MCP can enhance security workflows by ensuring Gemini models possess contextual awareness across multiple downstream services.

MCP can help facilitate an enhanced, cross-platform agentic experience. With MCP, our new AI agents — such as the alert triage agent in Google Security Operations that autonomously investigates alerts — can query partner tools for telemetry, enrich investigations with third-party data, and orchestrate response actions across your entire security stack. 

We are proud to confirm that all of our inaugural launch partners support MCP and have developed recommended approaches for how to activate MCP-supported agentic workflows across our products, a crucial step towards realizing our vision of an agentic SOC where AI functions as a virtual security assistant, proactively identifying threats and guiding you to faster, more effective responses.

Our open future on Google Cloud Marketplace

The introduction of the Google Unified Security Recommended program is only the beginning. We are dedicated to expanding this program to include a wider array of most trusted partner solutions with substantial investment across the Google Unified Security product suite, helping our customers build a more scalable, effective, and interoperable security architecture.

For simplified procurement and deployment, all qualified Google Unified Security Recommended solutions are available in the Google Cloud Marketplace. We offer Google Unified Security and Google Cloud customers streamlined purchasing of third-party offerings, all consolidated into one Google Cloud bill.

To learn more about the program and explore Google-validated solutions from our partners, visit the Google Unified Security Recommended page. Tech partners interested in program consideration are encouraged to reach out for guidance.

AI agents are transforming the nature of work by automating complex workflows with speed, scale, and accuracy.  At the same time, startups are constantly moving, growing, and evolving – which means they need clear ways to implement agentic workflows, not piles of documentation that send precious resources into a tailspin.

Today, we’ll share a simple four-step framework to help startups build multi-agent systems.  Multi-agentic workflows can be complicated, but there are easy ways to get started and see real gains without spending weeks in production. 

In this post, we’ll show you a systematic, operations-driven roadmap for navigating this new landscape, using one of our projects to provide concrete examples for the concepts laid out in the official startups technical guide: AI agents.

Step #1: Build your foundation 

The startups technical guide outlines three primary paths for leveraging agents:

1. Pre-built Google agents

2. Partner agents

3. Custom-built agents (agents you build on your own).

To build our Sales Intelligence Agent, we needed to automate a highly specific, multi-step workflow that involved our own proprietary logic and would eventually connect to our own data sources. This required comprehensive orchestration control and tool definition that only a “code-first” approach could provide.

That’s why we chose Google’s Agent Development Kit (ADK) as our framework. It offered the balance of power and flexibility necessary to build a truly custom, defensible system, combined with high-level abstractions for agent composition and orchestration that accelerated our development.

Step #2: Build out the engine 

We took a hybrid approach when building our agent architecture, which is managed by a top-level root_agent in orchestrator.py. Its primary role is to act as an intelligent controller using an LLM Agent for flexible user interaction, while delegating the core processing loop to [more deterministic ADK components like LoopAgent and custom BaseAgent classes.

1. Conversational onboarding: The LLM Agent starts by acting as a conversational “front-door,” interacting with the user to collect their name and email.

2. Workflow delegation: Once it has the user’s information, it delegates the main workflow to a powerful LoopAgent defined in its sub_agents list.

3. Data loading: The first step inside the LoopAgent is a custom agent called the CompanyLoopController. On the very first iteration of the loop, its job is to call our crm_tool to fetch the list of companies from the Google Sheet and load them into the session state.

4. Tool-based execution in a loop:  The loop processes each company by calling two key tools: the research_pipeline tool that encapsulates our complex company_researcher_agent and the sales_briefing_agent tool  that encapsulates the sales_briefing_agent. This “Agent-as-a-Tool” pattern is crucial for state isolation (more in Step 3).

This hybrid pattern gives us the best of both worlds: the flexibility of an LLM for user interaction and the structured, reliable control of a workflow agent with isolated, tool-based execution. 

Step #3: Tools, state, and reliability

An agent is only as powerful as the tools it can wield. To be truly useful, our system needed to connect to live data, not just a static local file. To achieve this, we built a custom tool, crm_tool.py, to allow our agent to read its list of target companies directly from a Google Sheet.

To build our read_companies_from_sheet function, we focused on two key areas:

1. Secure authentication: We used a Google Cloud Service Account for authentication, a best practice for production systems. Our code includes a helper function, get_sheets_service(), that centralizes all the logic for securely loading the service account credentials and initializing the API client.

2. Configuration management: All configuration, including the SPREADSHEET_ID, is managed via our .env file. This decouples the tool’s logic from its configuration, making it portable and secure.

This approach transformed our agent from one that could only work with local data to one that could securely interact with a live, cloud-based source of truth.

Managing state in loops: The “Agent-as-a-Tool” Pattern A critical challenge in looping workflows is ensuring state isolation between iterations. ADK’s session.state persists, which can cause ‘context rot’ if not managed. Our solution was the “Agent-as-a-Tool” pattern. Instead of running the complex company_researcher_agent directly in the loop, we encapsulated its entire SequentialAgent pipeline into a single, isolated AgentTool (company_researcher_agent_tool).

Every time the loop calls this tool, the ADK provides a clean, temporary context for its execution. All internal steps (planning, QA loop, compiling) happen within this isolated context. When the tool returns the final compiled_report, the temporary context is discarded, guaranteeing a fresh start for the next company. This pattern provides perfect state isolation by design, making the loop robust without manual cleanup logic.

Step 4: Go from Localhost to a scalable deployed product 

Here is our recommended three-step blueprint for moving from a local prototype to a production-ready agent on Google Cloud.

1. Adopt a production-grade project template
Our most critical lesson was that a simple, local-first project structure is not built for the rigors of the cloud. The turning point for our team was adopting Google’s official Agent Starter Pack. This professional template is not just a suggestion; for any serious project, we now consider it a requirement. It provides three non-negotiable foundations for success out of the box:

• Robust dependency management: It replaces the simplicity of local tools like Poetry with the production-grade power of PDM and uv, ensuring that every dependency is locked and every deployment is built from a fast, deterministic, and repeatable environment.

• A pre-configured CI/CD pipeline: It comes with a ready-to-use continuous integration and deployment pipeline for Google Cloud Build, which automates the entire process of testing, building, and deploying your agent.

• Multi-environment support: The template is pre-configured for separate staging and production environments, a best practice that allows you to safely test changes in an isolated staging environment before promoting them to your live users.

The process begins by using the official command-line tool to generate your project’s local file structure. This prompts you to choose a base template; we used the “ADK Base Template” and then moved our agent logic into the newly created source code files ( App) .

The final professional project structure:

With the local files created, the next step is to provision the cloud infrastructure. From inside the new project directory, you run the setup-cicd command. This interactive wizard connects to your Google Cloud and GitHub accounts, then uses Terraform under the hood to automatically build your entire cloud environment, including the CI/CD pipeline.

2. Cloud Build 
Once the setup is complete with the starter pack, your development workflow becomes incredibly simple. Every time a developer pushes a new commit to the main branch of your GitHub repository:

• Google Cloud Build fetches your latest code.

• It builds your agent into a secure, portable container image. This process includes installing all the dependencies from your uv.lock file, guaranteeing a perfect, repeatable build every single time.

• It deploys this new version to your staging environment. Within minutes, your latest code is live and ready for testing in a real cloud environment.

• It waits for your approval. The pipeline is configured to require a manual “Approve” click in the Cloud Build console before it will deploy that exact same, tested version to your production environment. This gives you the perfect balance of automation and control.

3. Deploy on Agent Engine and Cloud Run 
The final piece of the puzzle is where the agent actually runs. Cloud Build deploys your agent to Vertex AI Agent Engine, which provides the secure, public endpoint and management layer for your agent.

Crucially, Agent Engine is built on top of Google Cloud Run, a powerful serverless platform. This means you don’t have to manage any servers yourself. Your agent automatically scales up to handle thousands of users, and scales down to zero when not in use, meaning you only pay for the compute you actually consume. 

Get started 

Ready to build your own?

• Explore the code for our Sales Intelligence Agent on GitHub.

• Dive deeper with the Startups Technical Guide: AI agents.

• Get started with production deployments using the Agent Starter Pack.

• No matter where you are with AI adoption, we are here to help. Contact our Startup team today and you could get up to $350,000 USD in cloud credits with the Google for Startups Cloud Program,

^{The technical journey and insights detailed in this blog post were the result of a true team effort. I want to extend my sincere appreciation to the core collaborators whose work provided the foundation for this article: Luis Sala, Isaac Attuah, Ishana Shinde, Andrew Thankson, and Kristin Kim. Their hands-on contributions to architecting and building the agent were essential to the lessons shared here.}

For those building with AI, most are in it to change the world — not twiddle their thumbs. So when inspiration strikes, the last thing anyone wants is to spend hours waiting for the latest AI models to download to their development environment.

That’s why today we’re announcing a deeper partnership between Hugging Face and Google Cloud that:

• reduces Hugging Face model download times through Vertex AI and Google Kubernetes Engine

• offers native support for TPUs on all open models sourced through Hugging Face

• provides a safer experience through Google Cloud’s built-in security capabilities.

We’ll enable faster download times through a new gateway for Hugging Face repositories that will cache Hugging Face models and datasets directly on Google Cloud. Moving forward, developers working with Hugging Face’s open models on Google Cloud should expect download times to take minutes, not hours.

We’re also working with Hugging Face to add native support for TPUs for all open models on the Hugging Face platform. This means that whether developers choose to deploy training and inference workloads on NVIDIA GPUs or on TPUs, they’ll experience the same ease of deployment and support. 

Open models are gaining traction with enterprise developers, who typically work with specific security requirements. To support enterprise developers, we’re working with Hugging Face to bring Google Cloud’s extensive security protocols to all Hugging Face models deployed through Vertex AI. This means that any Hugging Face model on Vertex AI Model Garden will now be scanned and validated with Google Cloud’s leading cybersecurity capabilities powered by our Threat Intelligence platform and Mandiant.

A more open AI

Ultimately, we’re committed — through our robust and diverse AI ecosystem — to supporting developers with class-leading AI tools, a choice of AI-optimized infrastructure, and a selection of models in the hundreds; this includes a broad set of open models optimized to run Google Cloud through Hugging Face.

This expanded partnership with Hugging Face furthers that commitment and will ensure that developers have an optimal experience when serving AI models on Google Cloud, whether they choose a model from Google, from our many partners, or one of the thousands of open models available on Hugging Face.

You can read more on Hugging Face’s blog.

Written by: Josh Stroschein, Jae Young Kim

The prevalence of obfuscation and multi-stage layering in today’s malware often forces analysts into tedious and manual debugging sessions. For instance, the primary challenge of analyzing pervasive commodity stealers like AgentTesla isn’t identifying the malware, but quickly cutting through the obfuscated delivery chain to get to the final payload.

Unlike traditional live debugging, Time Travel Debugging (TTD) captures a deterministic, shareable record of a program’s execution. Leveraging TTD’s powerful data model and time travel capabilities allow us to efficiently pivot to the key execution events that lead to the final payload.

This post introduces all of the basics of WinDbg and TTD necessary to start incorporating TTD into your analysis. We demonstrate why it deserves to be a part of your toolkit by walking through an obfuscated multi-stage .NET dropper that performs process hollowing.

What is Time Travel Debugging?

Time Travel Debugging (TTD), a technology offered by Microsoft as part of WinDbg, records a process’s execution into a trace file that can be replayed forwards and backwards. The ability to quickly rewind and replay execution reduces analysis time by eliminating the need to constantly restart debugging sessions or restore virtual machine snapshots. TTD also enables users to query the recorded execution data and filter it with Language Integrated Query (LINQ) to find specific events of interest like module loads or calls to APIs that implement malware functionalities like shellcode execution or process injection.

During recording, TTD acts as a transparent layer that allows full interaction with the operating system. A trace file preserves a complete execution record that can be shared with colleagues to facilitate collaboration, circumventing environmental differences that can affect the results of live debugging.

While TTD offers significant advantages, users should be aware of certain limitations. Currently, TTD is restricted to user-mode processes and cannot be used for kernel-mode debugging. The trace files generated by TTD have a proprietary format, meaning their analysis is largely tied to WinDbg. Finally, TTD does not offer “true” time travel in the sense of altering the program’s past execution flow; if you wish to change a condition or variable and see a different outcome, you must capture an entirely new trace as the existing trace is a fixed recording of what occurred.

A Multi-Stage .NET Dropper with Signs of Process Hollowing

The Microsoft .NET framework has long been popular among threat actors for developing highly obfuscated malware. These programs often use code flattening, encryption, and multi-stage assemblies to complicate the analysis process. This complexity is amplified by Platform Invoke (P/Invoke), which gives managed .NET code direct access to the unmanaged Windows API, allowing authors to port tried-and-true evasion techniques like process hollowing into their code.

Process hollowing is a pervasive and effective form of code injection where malicious code runs under the guise of another process. It is common at the end of downloader chains because the technique allows injected code to assume the legitimacy of a benign process, making it difficult to spot the malware with basic monitoring tools.

In this case study, we’ll use TTD to analyze a .NET dropper that executes its final stage via process hollowing. The case study demonstrates how TTD facilitates highly efficient analysis by quickly surfacing the relevant Windows API functions, enabling us to bypass the numerous layers of .NET obfuscation and pinpoint the payload.

Basic analysis is a vital first step that can often identify potential process hollowing activity. For instance, using a sandbox may reveal suspicious process launches. Malware authors frequently target legitimate .NET binaries for hollowing as these blend seamlessly with normal system operations. In this case, reviewing process activity on VirusTotal shows that the sample launches InstallUtil.exe (found in %windir%Microsoft.NETFramework<version>). While InstallUtil.exe is a legitimate utility, its execution as a child process of a suspected malicious sample is an indicator that helps focus our initial investigation on potential process injection.

Despite newer, more stealthy techniques, such as Process Doppelgänging, when an attacker employs process injection, it’s still often the classic version of process hollowing due to its reliability, relative simplicity, and the fact that it still effectively evades less sophisticated security solutions. The classic process hollowing steps are as follows:

1. CreateProcess (with the CREATE_SUSPENDED flag): Launches the victim process (InstallUtil.exe) but suspends its primary thread before execution.

2. ZwUnmapViewOfSection or NtUnmapViewOfSection: “Hollows out” the process by removing the original, legitimate code from memory.

3. VirtualAllocEx and WriteProcessMemory: Allocates new memory in the remote process and injects the malicious payload.

4. GetThreadContext: Retrieves the context (the state and register values) of the suspended primary thread.

5. SetThreadContext: Redirects the execution flow by modifying the entry point register within the retrieved context to point to the address of the newly injected malicious code.

6. ResumeThread: Resumes the thread, causing the malicious code to execute as if it were the legitimate process.

To confirm this activity in our sample using TTD, we focus our search on the process creation and the subsequent writes to the child process’s address space. The approach demonstrated in this search can be adapted to triage other techniques by adjusting the TTD queries to search for the APIs relevant to that technique.

Recording a Time Travel Trace of the Malware

To begin using TTD, you must first record a trace of a program’s execution. There are two primary ways to record a trace: using the WinDbg UI or the command-line utilities provided by Microsoft. The command-line utilities offer the quickest and most customizable way to record a trace, and that is what we’ll explore in this post.

Warning: Take all usual precautions for performing dynamic analysis of malware when recording a TTD trace of malware executables. TTD recording is not a sandbox technology and allows the malware to interface with the host and the environment without obstruction.

TTD.exe is the preferred command-line tool for recording traces. While Windows includes a built-in utility (tttracer.exe), that version has reduced features and is primarily intended for system diagnostics, not general use or automation. Not all WinDbg installations provide the TTD.exe utility or add it to the system path. The quickest way to get TTD.exe is to use the stand-alone installer provided by Microsoft. This installer automatically adds TTD.exe to the system’s PATH environment variable, ensuring it’s available from a command prompt. To see its usage information, run TTD.exe -help.

The quickest way to record a trace is to simply provide the command line invoking the target executable with the appropriate arguments. We use the following command to record a trace of our sample:

C:UsersFLAREDesktop> ttd.exe 0b631f91f02ca9cffd66e7c64ee11a4b.bin
Microsoft (R) TTD 1.01.11 x64
Release: 1.11.532.0
Copyright (C) Microsoft Corporation. All rights reserved.

Launching '0b631f91f02ca9cffd66e7c64ee11a4b.bin'
    Initializing the recording of process (PID:2448) on trace file: C:UsersFLAREDesktopb631f91f02ca9cffd66e7c64ee11a4b02.run
    Recording has started of process (PID:2448) on trace file: C:UsersFLAREDesktopb631f91f02ca9cffd66e7c64ee11a4b02.run

Once TTD begins recording, the trace concludes in one of two ways. First, the tracing automatically stops upon the malware’s termination (e.g., process exit, unhandled exception, etc.). Second, the user can manually intervene. While recording, TTD.exe displays a small dialog (shown in figure 2) with two control options:

• Tracing Off: Stops the trace and detaches from the process, allowing the program to continue execution.

• Exit App: Stops the trace and also terminates the process.

Recording a TTD trace produces the following files:

• <trace>.run: The trace file is a proprietary format that contains compressed execution data. The size of a trace file is influenced by the size of the program, the length of execution, and other external factors such as the number of additional resources that are loaded.

• <trace>.idx: The index file allows the debugger to quickly locate specific points in time during the trace, bypassing sequential scans of the entire trace. The index file is created automatically the first time a trace file is opened in WinDbg. In general, Microsoft suggests that index files are typically twice the size of the trace file.

• <trace>.out: The trace log file containing logs produced during trace recording.

Once a trace is complete, the .run file can be opened with WinDbg.

Triaging the TTD Trace: Shifting Focus to Data

The fundamental advantage of TTD is the ability to shift focus from manual code stepping to execution data analysis. Performing rapid, effective triage with this data-driven approach requires proficiency in both basic TTD navigation and querying the Debugger Data Model. Let’s begin by exploring the basics of navigation and the Debugger Data Model.

Navigating a Trace

Basic navigation commands are available under the Home tab in the WinDbg UI.

The standard WinDbg commands and shortcuts for controlling execution are:

• g: Go (F5) – Resume execution

• gu: Go Up / Step Out (Shift+F11) – Execute until current function is complete

• t: Trace / Step Into (F11 or F8) – Single step into

• p: Step / Step Over (F10) – Single step over

Replaying a TTD trace enables the reverse flow control commands that complement the regular flow control commands. Each reverse flow control complement is formed by appending a dash (–) to the regular flow control command:

• g-: Go Back – Execute the trace backwards

• g-u: Step Out Back – Execute the trace backwards up to the last call instruction

• t-: Step Into Back – Single step into backwards

• p-: Step Over Back – Single step over backwards

Time Travel (!tt) Command

While basic navigation commands let you move step-by-step through a trace, the time travel command (!tt) enables precise navigation to a specific trace position. These positions are often provided in the output of various TTD commands. A position in a TTD trace is represented by two hexadecimal numbers in the format #:# (e.g., E:7D5) where:

• The first part is a sequencing number typically corresponding to a major execution event, such as a module load or an exception.

• The second part is a step count, indicating the number of events or instructions executed since that major execution event.

We’ll use the time travel command later in this post to jump directly to the critical events in our process hollowing example, bypassing manual instruction tracing entirely.

The TTD Debugger Data Model

The WinDbg debugger data model is an extensible object model that exposes debugger information as a navigable tree of objects. The debugger data model brings a fundamental shift in how users access debugger information in WinDbg, from wrangling raw text-based output to interacting with structured object information. The data model supports LINQ for querying and filtering, allowing users to efficiently sort through large volumes of execution information. The debugger data model also simplifies automation through JavaScript, with APIs that mirror how you access the debugger data model through commands.

The Display Debugger Object Model Expression (dx) command is the primary way to interact with the debugger data model from the command window in WinDbg. The model lends itself to discoverability – you can begin traversing through it by starting at the root Debugger object:

0:000> dx Debugger
Debugger
    Sessions
    Settings
    State
    Utility
    LastEvent

The command output lists the five objects that are properties of the Debugger object. Note that the names in the output, which look like links, are marked up using the Debugger Markup Language (DML). DML enriches the output with links that execute related commands. Clicking on the Sessions object in the output executes the following dx command to expand on that object:

0:000> dx -r1 Debugger.Sessions
Debugger.Sessions                
    [0x0]            : Time Travel Debugging: 0b631f91f02ca9cffd66e7c64ee11a4b.run

The -r# argument specifies recursion up to # levels, with a default depth of one if not specified. For example, increasing the recursion to two levels in the previous command produces the following output:

0:000> dx -r2 Debugger.Sessions
Debugger.Sessions                
    [0x0]            : Time Travel Debugging: 0b631f91f02ca9cffd66e7c64ee11a4b.run
        Processes       
        Id               : 0
        Diagnostics     
        TTD             
        OS              
        Devices         
        Attributes

The -g argument displays any iterable object into a data grid in which each element is a grid row and the child properties of each element are grid columns.

0:000> dx -g Debugger.Sessions

Debugger and User Variables

WinDbg provides some predefined debugger variables for convenience which can be listed through the DebuggerVariables property.

0:000> dx Debugger.State.DebuggerVariables
Debugger.State.DebuggerVariables                
   cursession       : Time Travel Debugging: 0b631f91f02ca9cffd66e7c64ee11a4b.run
    curprocess       : 0b631f91f02ca9cffd66e7c64ee11a4b.exe [Switch To]
    curthread        [Switch To]
    scripts         
    scriptContents   : [object Object]
    vars            
    curstack        
    curframe         : ntdll!LdrInitializeThunk [Switch To]
    curregisters    
    debuggerRootNamespace

Frequently used variables include:

• @$cursession: The current debugger session. Equivalent to Debugger.Sessions[<session>]. Commonly used items include:

• @$cursession.Processes: List of processes in the session.

• @$cursession.TTD.Calls: Method to query calls that occurred during the trace.

• @$cursession.TTD.Memory: Method to query memory operations that occurred during the trace.

• @$curprocess.Modules: List of currently loaded modules.

• @$curprocess.TTD.Events: List of events that occurred during the trace.

Investigating the Debugger Data Model to Identify Process Hollowing

With a basic understanding of TTD concepts and a trace ready for investigation, we can now look for evidence of process hollowing. To begin, the Calls method can be used to search for specific Windows API calls. This search is effective even with a .NET sample because the managed code must interface with the unmanaged Windows API through P/Invoke to perform a technique like process hollowing.

Process hollowing begins with the creation of a process in a suspended state via a call to CreateProcess with a creation flag value of 0x4. The following query uses the Calls method to return a table of each call to the kernel32 module’s CreateProcess* in the trace; the wildcard (*) ensures the query matches calls to either CreateProcessA or CreateProcessW.

0:000> dx -g @$cursession.TTD.Calls("kernel32!CreateProcess*")

This query returns a number of fields, not all of which are helpful for our investigation. To address this, we can apply the Select LINQ query to the original query, which allows us to specify which columns to display and rename them.

0:000> dx -g @$cursession.TTD.Calls("kernel32!CreateProcess*").Select(c => new { TimeStart = c.TimeStart, Function = c.Function, Parameters = c.Parameters, ReturnAddress = c.ReturnAddress})

The result shows one call to CreateProcessA starting at position 58243:104D. Note the return address: since this is a .NET binary, the native code executed by the Just-In-Time (JIT) compiler won’t be located in the application’s main image address space (as it would be in a non-.NET image). Normally, an effective triage step is to filter results with a Where LINQ query, limiting the return address to the primary module to filter out API calls that do not originate from the malware. This Where filter, however, is less reliable when analyzing JIT-compiled code due to the dynamic nature of its execution space.

The next point of interest is the Parameters field. Clicking on the DML link on the collapsed value {..} displays Parameters via a corresponding dx command.

0:000> dx -r1 @$cursession.TTD.Calls("kernel32!CreateProcess*").Select( c => new { TimeStart = c.TimeStart, Parameters = c.Parameters, ReturnAddress = c.ReturnAddress})[0].Parameters

@$cursession.TTD.Calls("kernel32!CreateProcess*").Select( c => new { TimeStart = c.TimeStart, Parameters = c.Parameters, ReturnAddress = c.ReturnAddress})[0].Parameters
    [0x0]            : 0x55de700055de74
    [0x1]            : 0x55e0780055e0ac
    [0x2]            : 0x808000400000000
    [0x3]            : 0x55de4000000000

Function arguments are available under a specific Calls object as an array of values. However, before we investigate the parameters, there are some assumptions made by TTD that are worth exploring. Overall, these assumptions are affected by whether the process is 32-bit or 64-bit. An easy way to check the bitness of the process is by inspecting the DebuggerInformation object.

0:00> dx Debugger.State.DebuggerInformation
Debugger.State.DebuggerInformation                
    ProcessorTarget  : X86 <--- Process Bitness
    Bitness          : 32
    EngineFilePath   : C:Program FilesWindowsApps<SNIPPED>x86dbgeng.dll
    EngineVersion    : 10.0.27871.1001

The key identifier in the output is ProcessorTarget: this value indicates the architecture of the guest process that was traced, regardless of whether the host operating system running the debugger is 64-bit.

TTD uses symbol information provided in a program database (PDB) file to determine the number of parameters, their types and the return type of a function. However, this information is only available if the PDB file contains private symbols. While Microsoft provides PDB files for many of its libraries, these are often public symbols and therefore lack the necessary function information to interpret the parameters correctly. This is where TTD makes another assumption that can lead to incorrect results. Primarily, it assumes a maximum of four QWORD parameters and that the return value is also a QWORD. This assumption creates a mismatch in a 32-bit process (x86), where arguments are typically 32-bit (4-byte) values passed on the stack. Although TTD correctly finds the arguments on the stack, it misinterprets two adjacent 32-bit arguments as a single, 64-bit value.

One way to resolve this is to manually investigate the arguments on the stack. First we use the !tt command to navigate to the beginning of the relevant call to CreateProcessA.

0:000> !tt 58243:104D

(b48.12a4): Break instruction exception - code 80000003 (first/second chance not available)
Time Travel Position: 58243:104D
eax=00bed5c0 ebx=039599a8 ecx=00000000 edx=75d25160 esi=00000000 edi=03331228
eip=75d25160 esp=0055de14 ebp=0055df30 iopl=0         nv up ei pl zr na pe nc
cs=0023  ss=002b  ds=002b  es=002b  fs=0053  gs=002b             efl=00000246
KERNEL32!CreateProcessA:
75d25160 8bff            mov     edi,edi

The return address is at the top of the stack at the start of a function call, so the following dd command skips over this value by adding an offset of 4 to the ESP register to properly align the function arguments.

0:000> dd /c 1 esp+4 L0A
0055de18  0055de74  <-- Application Name
0055de1c  0055de70
0055de20  0055e0ac
0055de24  0055e078
0055de28  00000000
0055de2c  08080004  <-- Creation Flags - 0x4 (CREATE_SUSPENDED)
0055de30  00000000
0055de34  0055de40
0055de38  0055e0c0
0055de3c  0055e068

The value of 0x4 (CREATE_SUSPENDED) set in the bitmask for the dwCreationFlags argument (6th argument) indicates that the process will be created in a suspended state.

The following command dereferences esp+4 via the poi operator to retrieve the application name string pointer then uses the da command to display the ASCII string.

0:000> da poi(esp+4)
0055de74  "C:WindowsMicrosoft.NETFramewo"
0055de94  "rkv4.0.30319InstallUtil.exe"

The command reveals that the target application is InstallUtil.exe, which aligns with the findings from basic analysis.

It is also useful to retrieve the handle to the newly created process in order to identify subsequent operations performed on it. The handle value is returned through a pointer (0x55e068 in the earlier referenced output) to a PROCESS_INFORMATION structure passed as the last argument. This structure has the following definition:

typedef struct _PROCESS_INFORMATION {
  HANDLE hProcess;
  HANDLE hThread;
  DWORD  dwProcessId;
  DWORD  dwThreadId;
}

After the call to CreateProcessA, the first member of this structure should be populated with the handle to the process. Step out of the call using the gu (Go Up) command to examine the populated structure.

0:000> gu
Time Travel Position: 58296:60D

0:000> dd /c 1 0x55e068 L4
0055e068  00000104 <-- handle to process
0055e06c  00000970
0055e070  00000d2c
0055e074  00001c30

In this trace, CreateProcess returned 0x104 as the handle for the suspended process.

The most interesting operation in process hollowing for the purpose of triage is the allocation of memory and subsequent writes to that memory, commonly performed via calls to WriteProcessMemory. The previous Calls query can be updated to identify calls to WriteProcessMemory.

0:000> dx -g @$cursession.TTD.Calls("kernel32!WriteProcessMemory*").Select( c => new { TimeStart = c.TimeStart, ReturnAddress = c.ReturnAddress, Params = c.Parameters})
=============================================================
=          = (+) TimeStart = (+) ReturnAddress = (+) Params =
=============================================================
= [0x0]    - 6A02A:4B4     - 0x15032e2         - {...}      =
= [0x1]    - 6E516:A91     - 0x15032e2         - {...}      =
= [0x2]    - 729A2:511     - 0x15032e2         - {...}      =
= [0x3]    - 76E2D:750     - 0x15032e2         - {...}      =
= [0x4]    - 7B2DF:C1C     - 0x15032e2         - {...}      =
=============================================================

The query returns four results. The following queries expand the arguments for each call to WriteProcessMemory.

0:000> dx -r1 @$cursession.TTD.Calls("kernel32!WriteProcessMemory*").Select( c => new { TimeStart = c.TimeStart, ReturnAddress = c.ReturnAddress, Params = c.Parameters})[0].Params
@$cursession.TTD.Calls("kernel32!WriteProcessMemory*").Select( c => new { TimeStart = c.TimeStart, ReturnAddress = c.ReturnAddress, Params = c.Parameters})[0].Params                
    [0x0]            : 0x104        <-- Target process handle
    [0x1]            : 0x400000     <-- Target Address
    [0x2]            : 0x9810af0    <-- Source buffer
    [0x3]            : 0x200        <-- Write size

0:000> dx -r1 @$cursession.TTD.Calls("kernel32!WriteProcessMemory*").Select( c => new { TimeStart = c.TimeStart, ReturnAddress = c.ReturnAddress, Params = c.Parameters})[1].Params
@$cursession.TTD.Calls("kernel32!WriteProcessMemory*").Select( c => new { TimeStart = c.TimeStart, ReturnAddress = c.ReturnAddress, Params = c.Parameters})[1].Params                
    [0x0]            : 0x104
    [0x1]            : 0x402000
    [0x2]            : 0x984cb10
    [0x3]            : 0x3b600

0:000> dx -r1 @$cursession.TTD.Calls("kernel32!WriteProcessMemory*").Select( c => new { TimeStart = c.TimeStart, ReturnAddress = c.ReturnAddress, Params = c.Parameters})[2].Params
@$cursession.TTD.Calls("kernel32!WriteProcessMemory*").Select( c => new { TimeStart = c.TimeStart, ReturnAddress = c.ReturnAddress, Params = c.Parameters})[2].Params                
    [0x0]            : 0x104
    [0x1]            : 0x43e000
    [0x2]            : 0x387d9d0
    [0x3]            : 0x600

0:000> dx -r1 @$cursession.TTD.Calls("kernel32!WriteProcessMemory*").Select( c => new { TimeStart = c.TimeStart, ReturnAddress = c.ReturnAddress, Params = c.Parameters})[3].Params
@$cursession.TTD.Calls("kernel32!WriteProcessMemory*").Select( c => new { TimeStart = c.TimeStart, ReturnAddress = c.ReturnAddress, Params = c.Parameters})[3].Params                
    [0x0]            : 0x104
    [0x1]            : 0x440000
    [0x2]            : 0x3927a78
    [0x3]            : 0x200

WriteProcessMemory has the following function signature:

BOOL WriteProcessMemory(
  [in]  HANDLE  hProcess,
  [in]  LPVOID  lpBaseAddress,
  [in]  LPCVOID lpBuffer,
  [in]  SIZE_T  nSize,
  [out] SIZE_T  *lpNumberOfBytesWritten
);

Investigating these calls to WriteProcessMemory shows that the target process handle is 0x104, which represents the suspended process. The second argument defines the address in the target process. The arguments to these calls reveal a pattern common to PE loading: the malware writes the PE header followed by the relevant sections at their virtual offsets.

It is worth noting that the memory of the target process cannot be analyzed from this trace. To record the execution of a child process, pass the -children flag to the TTD.exe utility. This will generate a trace file for each process, including all child processes, spawned during execution.

The first memory write to what is likely the target process’s base address (0x400000) is 0x200 bytes. This size is consistent with a PE header, and examining the source buffer (0x9810af0) confirms its contents.

0:000> db 0x9810af0
09810af0  4d 5a 90 00 03 00 00 00-04 00 00 00 ff ff 00 00  MZ..............
09810b00  b8 00 00 00 00 00 00 00-40 00 00 00 00 00 00 00  ........@.......
09810b10  00 00 00 00 00 00 00 00-00 00 00 00 00 00 00 00  ................
09810b20  00 00 00 00 00 00 00 00-00 00 00 00 80 00 00 00  ................
09810b30  0e 1f ba 0e 00 b4 09 cd-21 b8 01 4c cd 21 54 68  ........!..L.!Th
09810b40  69 73 20 70 72 6f 67 72-61 6d 20 63 61 6e 6e 6f  is program canno
09810b50  74 20 62 65 20 72 75 6e-20 69 6e 20 44 4f 53 20  t be run in DOS 
09810b60  6d 6f 64 65 2e 0d 0d 0a-24 00 00 00 00 00 00 00  mode....$.......

The !dh extension can be used to parse this header information.

0:000> !dh 0x9810af0

File Type: EXECUTABLE IMAGE
FILE HEADER VALUES
     14C machine (i386)
       3 number of sections
66220A8D time date stamp Fri Apr 19 06:09:17 2024

----- SNIPPED -----

OPTIONAL HEADER VALUES
     10B magic #
   11.00 linker version
         ----- SNIPPED -----
       0 [       0] address [size] of Export Directory
   3D3D4 [      57] address [size] of Import Directory
   ----- SNIPPED -----
       0 [       0] address [size] of Delay Import Directory
    2008 [      48] address [size] of COR20 Header Directory

SECTION HEADER #1
   .text name
   3B434 virtual size
    2000 virtual address
   3B600 size of raw data
     200 file pointer to raw data
----- SNIPPED -----

SECTION HEADER #2
   .rsrc name
     546 virtual size
   3E000 virtual address
     600 size of raw data
   3B800 file pointer to raw data
----- SNIPPED -----

SECTION HEADER #3
  .reloc name
       C virtual size
   40000 virtual address
     200 size of raw data
   3BE00 file pointer to raw data
----- SNIPPED -----

The presence of a COR20 header directory (a pointer to the .NET header) indicates that this is a .NET executable. The relative virtual addresses for the .text (0x2000), .rsrc (0x3E000), and .reloc (0x40000) also align with the target addresses of the WriteProcessMemory calls.

The newly discovered PE file can now be extracted from memory using the writemem command.

0:000> .writemem c:usersflareDesktopheaders.bin 0x9810af0 L0x200
Writing 200 bytes.

0:000> .writemem c:usersflareDesktoptext.bin 0x984cb10 L0x3b600
Writing 3b600 bytes.......................................................................................................................

0:000> .writemem c:usersflareDesktoprsrc.bin 0x387d9d0 L0x600
Writing 600 bytes.

0:000> .writemem c:usersflareDesktopreloc.bin 0x3927178 L0x200
Writing 200 bytes.

Using a hex editor, the file can be reconstructed by placing each section at its raw offset. A quick analysis of the resulting .NET executable (SHA256: 4dfe67a8f1751ce0c29f7f44295e6028ad83bb8b3a7e85f84d6e251a0d7e3076) in dnSpy reveals its configuration data.

----- SNIPPED -----

// Token: 0x0400000E RID: 14
public static bool EnableKeylogger = Convert.ToBoolean("false");
// Token: 0x0400000F RID: 15
public static bool EnableScreenLogger = Convert.ToBoolean("false");
// Token: 0x04000010 RID: 16
public static bool EnableClipboardLogger = Convert.ToBoolean("false");
// Token: 0x0400001C RID: 28
public static string SmtpServer = "<REDACTED";
// Token: 0x0400001D RID: 29
public static string SmtpSender = "<REDACTED>";
// Token: 0x04000025 RID: 37
public static string StartupDirectoryName = "eXCXES";
// Token: 0x04000026 RID: 38
public static string StartupInstallationName = "eXCXES.exe";
// Token: 0x04000027 RID: 39
public static string StartupRegName = "eXCXES";
----- SNIPPED -----

Conclusion: TTD as an Analysis Accelerator

This case study demonstrates the benefit of treating TTD execution traces as a searchable database. By capturing the payload delivery and directly querying the Debugger Data Model for specific API calls, we quickly bypassed the multi-layered obfuscation of the .NET dropper. The combination of targeted data model queries and LINQ filters (for CreateProcess* and WriteProcessMemory*) and low-level commands (!dh, .writemem) allowed us to isolate and extract the hidden AgentTesla payload, yielding critical configuration details in a matter of minutes.

The tools and environment used in this analysis—including the latest version of WinDbg and TTD—are readily available via the FLARE-VM installation script. We encourage you to streamline your analysis workflow with  this pre-configured environment.

The TTD trace can be downloaded from VirusTotal along with the original sample.

Identifying patterns and sequences within your data is crucial for gaining deeper insights. Whether you’re tracking user behavior, analyzing financial transactions, or monitoring sensor data, the ability to recognize specific sequences of events can unlock a wealth of information and actionable insights. 

Imagine you’re a marketer at an e-commerce company trying to identify your most valuable customers by their purchasing trajectory. You know that customers who start with small orders and progress to mid-range purchases will usually end up becoming high-value purchasers and your most loyal segment. Having to figure out the complex SQL to aggregate and join this data could be quite the challenging task.

That’s why we’re excited to introduce MATCH_RECOGNIZE, a new feature in BigQuery that allows you to perform complex pattern matching on your data directly within your SQL queries!

What is MATCH_RECOGNIZE?

At its core, MATCH_RECOGNIZE is a tool built directly into GoogleSQL for identifying sequences of rows that match a specified pattern. It’s similar to using regular expressions, but instead of matching patterns in a string of text, you’re matching patterns in a sequence of rows within your tables. This capability is especially powerful for analyzing time-series data or any dataset where the order of rows is important.

With MATCH_RECOGNIZE, you can express complex patterns and define custom logic to analyze them, all within a single SQL clause. This reduces the need for cumbersome self-joins or complex procedural logic. It also lessens your reliance on Python to process data and will look familiar to users who have experience with Teradata’s nPath or other external MATCH_RECOGNIZE workloads (like Snowflake, Azure, Flink, etc.).

How it works

The MATCH_RECOGNIZE clause is highly structured and consists of several key components that work together to define your pattern-matching logic:

• PARTITION BY: This clause divides your data into independent partitions, allowing you to perform pattern matching within each partition separately.

• ORDER BY: Within each partition, ORDER BY sorts the rows to establish the sequence in which the pattern will be evaluated.

• MEASURES: Here, you can define the columns that will be included in the output, often using aggregate functions to summarize the matched data.

• PATTERN: This is the heart of the MATCH_RECOGNIZE clause, where you define the sequence of symbols that constitutes a match. You can use quantifiers like *, +, ?, and more to specify the number of occurrences for each symbol.

• DEFINE: In this clause, you define the conditions that a row must meet to be classified as a particular symbol in your pattern.

Let’s look at a simple example. From our fictional scenario above, imagine you have a table of sales data, and as a marketing analyst, you want to identify customer purchase patterns where their spending starts low, increases to a mid-range, and then reaches a high level. With MATCH_RECOGNIZE, you could write a query like this:

In this example, we’re partitioning the data by customer and ordering it by sale_date. The PATTERN clause specifies that we’re looking for one or more “low” sales events, followed by one or more “mid” sales events, followed by one or more “high” sales events. The DEFINE clause then specifies the conditions for a sale to be considered “low”, “mid”, or “high”. The MEASURES clause decides how to summarize each match; here with match_number we are indexing each match starting from 1 and creating a ‘sales’ array that will track every match in order.

Below are example matched customers: 

This data highlights some sales trends and could offer insights for a market analyst to strategize conversion of lower-spending customers to higher-value sales based on these trends.

Use cases for MATCH_RECOGNIZE

The possibilities with MATCH_RECOGNIZE are vast. Here are just a few examples of how you can use this powerful feature:

• Funnel analysis: Track user journeys on your website or app to identify common paths and drop-off points. For example, you could define a pattern for a successful conversion funnel (e.g., view_product -> add_to_cart -> purchase) and analyze how many users complete it.

• Fraud detection: Identify suspicious patterns of transactions that might indicate fraudulent activity. For example, you could look for a pattern of multiple small transactions followed by a large one from a new account.

• Financial analysis: Analyze stock market data to identify trends and patterns, such as a “W” or “V” shaped recovery.

• Log analysis: Sift through application logs to find specific sequences of events that might indicate an error or a security threat.

• Churn analysis: Identify patterns in your data that lead to customer churn and find actionable insights to reduce churn and improve customer sentiment. 

• Network monitoring: Identify a series of failed login attempts to track issues or potential threats.

• Supply chain monitoring: Flag delays in a sequence of shipment events.

• Sports analytics: Identify streaks or changes in output for different players / teams over games, such as winning or losing streaks, changes in starting lineups, etc.

Get started today

Ready to start using MATCH_RECOGNIZE in your own queries? The feature is now available to all BigQuery users! To learn more and dive deeper into the syntax and advanced capabilities, check out the official documentation and tutorial available on Colab, BigQuery, and GitHub.

MATCH_RECOGNIZE opens up a whole new world of possibilities for sequential analysis in BigQuery, and we can’t wait to see how you’ll use it to unlock deeper insights from your data.

For decades, SQL has been the universal language for data analysis, offering access to analytics on structured data. Large Language Models (LLMs) like Gemini now provide a path to get nuanced insights from unstructured data such as text, image and video. However, integrating LLMs into standard SQL flow requires data movement, at least some prompt and parameter tuning to optimize result quality. This is expensive to perform at scale, which keeps these capabilities out of reach for many data practitioners. Today, we are excited to announce the public preview of BigQuery-managed AI functions, a new set of capabilities that reimagine SQL for the AI era. These functions — AI.IF, AI.CLASSIFY, and AI.SCORE — allow you to use generative AI for common analytical tasks directly within your SQL queries, no complex prompt tuning or new tools required. These functions have been optimized for their target use cases, and do not need you to choose models or tune their parameters. Further through intelligent optimizations on your provided prompt and query plans we keep the costs minimal.With these new functions, you can perform sophisticated AI-driven analysis using familiar SQL operators:

• Filter and join data based on semantic meaning using AI.IF in a WHERE or ON clause.

• Categorize unstructured text or images using AI.CLASSIFY in a GROUP BY clause.

• Rank rows based on natural language criteria using AI.SCORE in an ORDER BY clause.

Together, these functions allow answering new kinds of questions previously out of reach for SQL analytics, for example, companies to news articles which mention them even when an old or unofficial name is used.

Let’s dive deeper into how each of these functions works.

Function deep dive

AI.IF: Semantic filtering and joining

With AI.IF, you can filter or join data using conditions written in natural language. This is useful for tasks like identifying negative customer reviews, filtering images that have specific attributes, or finding relevant information in documents. BigQuery optimizes the query plan to reduce the number of calls to LLM by evaluating non-AI filters first. For example, the following query finds tech news articles from BBC that are related to Google.

You can also use AI.IF() for powerful semantic joins, such as performing entity resolution between two different product catalogs. The following query finds products that are semantically identical, even if their names are not an exact match.

AI.CLASSIFY: Data classification

The AI.CLASSIFY function lets you categorize text or image based on labels you provide. You can use it to route support tickets by topic or classify images based on their style. For instance, you can classify news articles by topic and then count the number of articles in each category with a single query.

AI.SCORE: Semantic ranking

You can use AI.SCORE to rank rows based on natural language criteria. This is powerful for ranking items based on a rubric. To give you consistent and high-quality results, BigQuery automatically refines your prompt into a structured scoring rubric. This example finds the top 10 most positive reviews for a movie of your choosing.

Built-in optimizations

These functions allow you to easily mix AI processing with common SQL operators like WHERE, JOIN, ORDER BY, and GROUP BY. BigQuery handles prompt optimization, model selection, and model parameter tuning for you. 

• Prompt optimization: LLMs are sensitive to the wording of a prompt, the same question can be expressed in different ways which affect quality and consistency. BigQuery optimizes your prompts into a structured format specifically for Gemini, helping to ensure higher-quality results and an improved cache hit rate.

• Query plan optimization: Running generative AI models over millions of rows can be slow and expensive. BigQuery query planner reorders AI functions in your filters and pulls AI functions out from join to reduce the number of calls to the model, which saves costs and improves performance.

• Model endpoint and parameter tuning: BigQuery tunes model endpoint and model parameters to improve both result quality and results consistency across query runs.

Get started

The new managed AI functions — AI.IF() , AI.SCORE() and AI.CLASSIFY() — complement the existing general-purpose Gemini inference functions such as AI.GENERATE from BigQuery. In addition to optimizations discussed above, you can expect future optimization and mixed query processing between BigQuery and Gemini for even better price-performance. You can indicate your interest for early access here. 

What to use and when: When your use case fits them, start with the managed AI functions as they are optimized for cost and quality. Use the AI.GENERATE family of functions when you need control on your prompt and input parameters, and want to choose from a wide range of supported models for LLM inference.

To learn more refer to our documentation. The new managed AI functions are also available in BigQuery DataFrames. See this notebook and documentation for Python examples.

For questions or feedback, reach out to us at bqml-feedback@google.com.

When a major vulnerability makes headlines, CISOs want to know fast if their organization is impacted and prepared. Getting the correct answer is often a time-consuming and human-intensive process that can take days or weeks, leaving open a dangerous window of unknown exposure.

To help close that gap, today we’re introducing the Emerging Threats Center in Google Security Operations. Available today for licensed customers in Google Security Operations, this new capability can help solve the core, practical problem of scaling detection engineering, and help transform how teams operationalize threat intelligence. 

Enabled by Gemini, our detection-engineering agent responds to new threat campaigns detected by Google Threat Intelligence, and includes frontline insights from Mandiant, VirusTotal, and across Google. It generates representative events, assessing coverage, and closing detection gaps. 

The Emerging Threats Center can help you understand if you are impacted by critical threat campaigns, and provides detection coverage to help ensure you are protected going forward. 

Introducing campaign-based prioritization with emerging threats

Protecting against new threats has long been a manual, reactive cycle. It begins with threat analysts pouring over reports to identify new campaign activity, which they then translate into indicators of compromise (IoCs) for detection engineers. Next, the engineering team manually authors, tests, and deploys the new detections. 

Too often, we hear from customers and security operations teams that this labor-intensive process leaves organizations swimming upstream. It was “hard to derive clear action from threat intelligence data,” according to 59% of IT and cybersecurity leaders surveyed in this year’s Threat Intelligence Benchmark, a commissioned study conducted by Forrester Consulting on behalf of Google Cloud.

By sifting through volumes of threat intelligence data, the Emerging Threats Center can help security surface the most relevant threat campaigns to an organization — and take proactive action against them.

Instead of starting in a traditional alert queue, analysts now have a single view of threats that pose the greatest risks to their specific environment. This view includes details on the presence of IOCs in event data and detection rules. 

For example, when a new zero-day vulnerability emerges, analysts don’t have to manually cross-reference blog posts with their alert queue. They can immediately see the campaign, the IOCs already contextualized against their own environment, and the specific detection rules to apply. This holistic approach can help them proactively hunt for the most time-sensitive threats before a major breach occurs.

Making all this possible is Gemini in Security Operations, transforming how we engineer detections. By ingesting a continuous stream of frontline threat intelligence, it can automatically test our detection corpus against new threats. When a gap is found, Gemini generates a new, fully-vetted detection rule for an analyst to approve. This systematic, automated workflow can help ensure you are protected from the latest threats.

Understanding exposure, detailing defensive posture

Our campaign-based approach can provide definitive answers to the two most critical questions a security team faces during a major threat event: How are we affected, and how well are we prepared.

How are we affected?

The first priority is to understand your exposure. The Emerging Threats Center can help you find active and past threats in your environment by correlating campaign intelligence against your data in two ways:

• IOC matches: It automatically searches for and prioritizes campaign-related IoCs across the previous 12 months of your security telemetry.

• Detection matches: It instantly surfaces hits from curated detection rules that have been mapped directly to the specific threat campaign.

Both matches provide a definitive starting point for your investigative workflow.

How are we prepared?

The Emerging Threat Center can also help prove that you are protected moving forward. This capability can provide immediate assurance of your defensive posture by helping you confirm two key facts:

• That you have no current or past IOC or detection hits related to the campaign.

• That you have the relevant, campaign-specific detections active and ready to stop malicious activity if it appears.

Under the hood: The detection engineering engine

The Emerging Threat Center is built on a resilient, automated system that uses Gemini models and AI agents to drastically shorten the detection engineering lifecycle.

Here’s how it works.

First, it ingests intelligence. The system automatically ingests detection opportunities from Google Threat Intelligence campaigns, which are sourced from Mandiant’s frontline incident response engagements, our Managed Defense customers, and Google’s unique global visibility. From thousands of raw sample events from adversary activity, Gemini is able to extract a distinct set of detection opportunities associated with the campaign.

Next, it generates synthetic events. We generate high-fidelity anonymized, synthetic event data that accurately mimics adversary tactics, techniques, and procedures (TTPs) described in the intelligence. We use an automated pipeline to generate a corpus of high-fidelity synthetic log events, providing a robust dataset for testing.

Then, it tests coverage. The system uses the synthetic data to test our existing detection rule set, providing a rapid, empirical answer to how well we are covered for a new threat. This automated testing pipeline quickly provides an answer on detection coverage.

After that, it accelerates rule creation. When coverage gaps are found, the process uses Gemini to automatically generate and evaluate new rules. Gemini drafts a new detection rule and provides a summary of its logic and expected performance, reducing the time to create a production-ready rule from days to hours.

Finally, it requires human review. The new rule is submitted to a human-in-the-loop security analyst who can vet and verify the new rule before deploying it. AI has helped us transform a best-effort, manual process into a systematic, automated workflow. By enabling us to tie new detections directly to the intelligence campaign it covers, we can help you be prepared for the latest threats.

“The real strategic shift is moving past those single indicators to systematically detecting underlying adversary behaviors — that’s how we get ahead and stay ahead. Out-of-box behavioral rules, based on Google’s deep intel visibility, help us get there,” said Ron Smalley, senior vice-president and head of Cybersecurity Operations, Fiserv.

To dive deeper into the complete framework that powers this, read more about our applied threat intelligence and watch our Google Security Talks keynote.