Today, we published a new Google research report on software supply chain security because we’ve seen a sharp rise in software supply chain attacks across almost every sector —and expect these trends to continue for the foreseeable future. We urge all organizations to act now to improve their software supply chain security.

Among the report’s conclusions, there are two key findings we want to highlight. First, the lessons we’ve learned from various security events call for a more holistic approach to strengthen defenses against software supply chain attacks. Second, we have worked with the security community to develop and deploy a common Supply-chain Levels for Software Artifacts (SLSA) framework that can mitigate threats across the entire software supply chain ecosystem. These frameworks can help organizations securely build and verify the integrity of software. You can find more information on the report’s conclusions here.

We know that modern day software supply chains continue to grow deeper, wider, and more complex. That complexity can make it challenging for customers to even know where to begin analyzing their supply chains for security issues. Our research shows that organizations must deal with these same complex issues regardless of which environments they operate in.

At Google Cloud, we’re deeply committed to working with our customers to help ensure that they have the support they need to evaluate their security posture, resiliency, and hygiene. Below, we suggest five steps to protect software across processes and systems, and tap into relevant Google Cloud products and services. These recommendations can enable customers to benefit from Google’s extensive security experience and reduce their need to develop, maintain, and operate complex processes to secure their open source dependencies.

Implementing best practices with Google Cloud

Customers who are interested in improving their software supply chain security can take immediate steps to implement best practices.

1. Enhance your existing Google Cloud security features with the Google Cloud security foundation guide. The guide can help you weigh important considerations including organizational structure, authentication and authorization, resource hierarchy, networking, logging, and detective controls. You can further engage Mandiant experts to assess your readiness.

You can also view centralized information about vulnerabilities and possible risks using Google Cloud services like Security Command Center, and get information about your service usage with Recommender, including recommendations that can help you to reduce risk. For example, you can identify IAM principals with excess permissions or unattended Google Cloud projects. You can also find additional resources from the Google Cybersecurity Action Team (GCAT), our premier security advisory team, here.

2. Explore fast software delivery and reliable and secure software with Google Cloud’s DevOps capabilities. You also should review foundational practices for designing, developing, and testing code that apply to most programming languages.

We strongly recommend you evaluate how you distribute software and the terms of software licenses in all of your dependencies. For more information on Google’s approach to helping organizations address vulnerabilities in open source software, see Appendix B in the research report.

3. Document the policies for your organization and incorporate validation of policies into your development, build, and deployment processes as you implement best practices. For example, your organization’s policies might include criteria for deployment that you implement with Binary Authorization. GCAT has published additional information on security policies and other cloud security transformation tips for CISOs here.

You can also explore Minimum Viable Secure Product, a security checklist of controls to establish a baseline security posture for a product. You can use the checklist to establish your minimum security control requirements and to evaluate software by third-party vendors.

Tapping into new Google product and service offerings

At Google Cloud, we continue to focus on delivering new and innovative security capabilities to help customers address the latest security threats. From the attack on SolarWinds to the community response to open source vulnerabilities such as Log4j, we’re seeing a spike in demand from customers on what we can do to help them manage software supply chain risk. We’ve made several recent announcements on that front that can help customers get started with Google Cloud today.

4. Use Google Cloud’s Software Delivery Shield. It provides a fully managed software supply chain security solution that offers a modular set of capabilities to help equip developers, DevOps, and security teams with the tools they need to build secure cloud applications. Software Delivery Shield spans across a family of Google Cloud services from developer tooling to runtimes including GKE, Cloud Code, Cloud Build, Cloud Deploy, Artifact Registry, and Binary Authorization. To learn more about Software Delivery Shield, check out the solution page, or watch this Google Cloud Next session to get a quick overview of Software Delivery Shield.
5. Enable our Assured Open Source Software (OSS) service, which can help enterprise and public sector open source software users to easily incorporate the same OSS packages that we use at Google into their own developer workflows. Packages curated by the Assured OSS service:

• are regularly scanned, analyzed, and fuzz-tested for vulnerabilities;
• have corresponding enriched metadata incorporating Container/Artifact Analysis data;
• are built with Cloud Build including evidence of verifiable Supply chain Levels for Software Artifacts (SLSA)-compliance;
• are verifiably signed by Google;
• and are distributed from an Artifact Registry that is secured and protected by Google.

If you are interested in learning more about software supply chain security in general, please contact us or reach out to your sales representative to schedule a software supply chain security workshop.

Google has long looked to Israel for globally impactful technologies including popular Search features, Waze, Live Caption, Duplex and flood forecasting. At our Decode with Google 15RAEL event last week, we celebrated 15 years of Google innovation in Israel and our longstanding support of the country’s vibrant startup ecosystem. 

Over the years, we’ve expanded our enterprise investments in the country, too. In addition to our over a decade long investment in the space, Google has acquired Israeli-based companies like Alooma, Elastifile and Velostrata, and Uri Frank joined Google Cloud last month to lead our server chip design team from our offices in Tel Aviv and Haifa. 

As we continue to meet growing demand for cloud services in Israel, we’re excited to announce that a new Google Cloud region is coming to Israel to make it easier for customers to serve their own users faster, more reliably and securely.

Our global network of Google Cloud regions are the foundation of the cloud infrastructure we’re building to support our customers. With cloud’s 25 regions and 76 zones around the world, we deliver high-performance, low-latency services and products for Google Cloud’s enterprise and public sector customers. With each new Google Cloud region, customers get access to secure infrastructure, smarter analytics tools, an open platform and the cleanest cloud in the industry. 

Having a region in Israel will help accelerate innovation for customers of all sizes, including PayBox, a digital wallet application owned by Discount Bank, one of Israel’s largest banks. “When we acquired PayBox, our goal was to improve the security and the user experience for its products, but we also wanted to keep the startup’s agility and innovation. Google Cloud has enabled us to do just that,” said Sarit Beck-Barkai, Managing Director of PayBox at Discount Bank.

“We are very excited that leading vendors like Google are investing and launching a local cloud region in Israel. This will make a significant change in the technology landscape of the public-sector, enterprise and SMB markets in Israel. Matrix is proud to be a major part of the transition to the cloud,” said Moti Gutman, CEO at Matrix, technology services company and Google Cloud partner. 

“In the last year, Panorays more than tripled its customer base and scaled its infrastructure, practically at the click of a button. Google Cloud made it easy for us to scale without worrying about DevOps, which meant that our engineers could focus on developing new and better features for our customers. The new region launching in Israel will allow us to serve our local customer base even better, as we’ll be able to experience higher availability and deploy resources in specific regions, thus reducing latency.” said Demi Ben-Ari, Co-founder and CTO, Panorays, a third-party security platform and Google Cloud customer.

“This new cloud region will provide even better access and growth potential for our mutual customers with tech hubs in the region. We are serving hyper growth companies who need Google Cloud’s services and will benefit greatly from this regional presence,” said Yoav Toussia-Cohen, CEO of DoiT International.

When it launches, the Israel region will deliver a comprehensive portfolio of Google Cloud products to private and public sector organizations locally. We look forward to welcoming you to the Israel region, and we’re excited to support your growing business on our platform. 

Learn more about our global cloud infrastructure, including new and upcoming regions, here.

As your organization transitions from on-premises to hybrid cloud or pure cloud, how you think about threat detection must evolve as well—especially when confronting threats across many cloud environments. A new foundational framework for thinking about threat detection in public cloud computing is needed to better secure digital transformations.

Because these terms have had different meanings over time, here’s what we mean by threat detection and detection and response. A balanced security strategy covers all three elements of a security triad: prevention, detection, and response. Prevention can improve, but never becomes perfect. Despite preventative controls, we still need to be on the lookout for threats that penetrate our defenses. Finding and confirming malicious activities, and automatically responding to them or presenting them to the security team constitutes detection and response.

Vital changes impact the transition from the traditional environment to the cloud and affect three key areas:

• Threat landscapes
• IT environment
• Detection methods

First, threat landscapes change. This means new threats evolve, old threats disappear, and the importance of many threats changes. If you perform a threat assessment on your environment and then migrate the entire environment to the public cloud, even if you use the lift and shift approach, the threat assessment will look very different. MITRE ATT&CK Cloud can help us understand how some threat activities apply to public cloud computing.

Second, the entire technology environment around you changes. This applies to the types of systems and applications you as a defender would encounter, but also to technologies and operational practices. Essentially, cloud as a realm where you have to detect threats is different —this applies to the assets being threatened and technologies doing the detecting. Sometimes cloud looks to traditional “blue teams” as some alien landscape where they would have only challenges. In reality, cloud does bring a lot of new opportunities for detection. The main theme here is change, some for the worse and some for the better.

After all, cloud is

• Usually distributed—running over many regions and data centers
• Often immutable—utilizes systems that are replaced, rather than updated
• Ephemeral uses workloads often created for the task and then removed
• API driven—enabled by pervasive APIs
• Centered on identity layer—mostly uses identities and not just network perimeter to separate workloads
• Automatically scalable—able to expand with the increasing workload
• Shared with the provider

Sometimes the combination of Distributed, Immutable, and Ephemeral cloud properties is called a DIE triad. All these affect detection for the cloud environment.

Third, telemetry sources and detection methods also change. While this may seem like it’s derived from the previous point we made, that’s not entirely true. For some cloud services, and definitely for SaaS, a popular approach of using an agent such as EDR would not work. However, new and rich sources of telemetry may be available—Cloud Audit Logs are a great example here.

Similarly, the expectation that you can sniff traffic on the perimeter, and that you even will have a perimeter, may not be entirely correct. Pervasive encryption hampers Layer 7 traffic analysis, while public APIs rewrite the rules on what a perimeter is. Finally, detection sources and methods are also inherently shared with the cloud provider, with some under cloud service provider control while others are under cloud user control.

This leads to several domains where we can and should detect threats in the cloud.

Let’s review a few cloud threat detection scenarios.

Everybody highlights the role of identity in cloud security. Naturally, it matters in threat detection as well—and it matters a lot. While we don’t want to repeat the cliche that in a public cloud you are one IAM mistake away from a data breach, we know that cloud security missteps can be costly. To help protect organizations, Google Cloud offers services that automatically and in real-time analyze every IAM grant to detect outsiders being added—even indirectly.

Detecting threats inside compute instances such as virtual machines (VM) using agents seems to be about the past. After all, VMs are just servers, right? However, this is an area where cloud brings new opportunities. For example, VM Threat Detection allows security teams to do completely agentless YARA rule execution against their entire compute fleet.

Finally, products like BigQuery require new ways of thinking about detecting data exfiltration. Security Command Center Premium detects queries and backups in BigQuery that would copy data to different Google Cloud organizations.

Naturally, some things stay the same in the cloud. These include broad threat categories such as insiders or outsiders; steps in the cyber exploit chain such as coarse-grained stages of an attack; and the MITRE ATT&CK Tactics are largely unchanged. It is also likely that broad detection use cases stay the same.

What does that mean for the defenders?

When you move to the cloud, your threats and your IT change—and change a lot.

This means that using on-premises detection technology and approaches as a foundation for future development may not work well.

This also means that merely copying all your on-premise detection tools and their threat detection content is not optimal.

Instead, moving to Google Cloud is an opportunity to transform how you can achieve your continued goals of confidentiality, integrity, and availability with the new opportunities created by the technology and process of cloud.

Call to action:

• Listen to “Threat Models and Cloud Security” (ep12)
• Listen to “What Does Good Detection and Response Look Like in the Cloud? Insights from Expel MDR” (ep72)
• Listen to “Cloud Threats and How to Observe Them” (ep69) and read the related blog “How to think about cloud threats today”
• Review how to test cloud detections
• Read the guidance on cloud threat investigation with SCC and Chronicle

At Google Cloud, we continue to invest in key technologies to progress towards our true north star on invisible security: making strong security pervasive and simple for everyone. Our investments are based on insights from our world-class threat intelligence teams and experience helping customers respond to the most sophisticated cyberattacks. Customers can tap into these capabilities to gain perspective and visibility on the most dangerous threat actors that no one else has. 

Recent advances in artificial intelligence (AI), particularly large language models (LLMs), accelerate our ability to help the people who are responsible for keeping their organizations safe. These new models not only give people a more natural and creative way to understand and manage security, they give people access to AI-powered expertise to go beyond what they could do alone. 

At the RSA Conference 2023, we are excited to announce Google Cloud Security AI Workbench, an industry-first extensible platform powered by a specialized, security LLM, Sec-PaLM. This new security model is fine-tuned for security use cases, incorporating our unsurpassed security intelligence such as Google’s visibility into the threat landscape and Mandiant’s frontline intelligence on vulnerabilities, malware, threat indicators, and behavioral threat actor profiles. 

Google Cloud Security AI Workbench powers new offerings that can now uniquely address three top security challenges: threat overload, toilsome tools, and the talent gap. It will also feature partner plug-in integrations to bring threat intelligence, workflow, and other critical security functionality to customers, with Accenture being the first partner to utilize Security AI Workbench. 

The platform will also let customers make their private data available to the platform at inference time; ensuring we honor all our data privacy commitments to customers. Because Security AI Workbench is built on Google Cloud’s Vertex AI infrastructure, customers control their data with enterprise-grade capabilities such as data isolation, data protection, sovereignty, and compliance support. 

Preventing threats from spreading beyond the first infection

We already provide best-in-class capabilities to help organizations immediately respond to threats. But what if we could not just identify and contain initial infections, but also help prevent them from happening anywhere else? With our AI advances, we can now combine world class threat intelligence with point-in-time incident analysis and novel AI-based detections and analytics to help prevent new infections. These advances are critical to help counter a potential surge in adversarial attacks that use machine learning and generative AI systems. That’s why we’re excited to introduce:  

• VirusTotal Code Insight uses Sec-PaLM to help analyze and explain the behavior of potentially malicious scripts, and will be able to better detect which scripts are actually threats. 
• Mandiant Breach Analytics for Chronicle leverages Google Cloud and Mandiant Threat Intelligence to automatically alert you to active breaches in your environment. It will use Sec-PaLM to help contextualize and respond instantly to these critical findings.

These new updates build on the existing AI in Google’s industry-leading solutions. For example, Chronicle Security Operations already uses frontline intelligence, integrated reasoning, and machine learning to identify initial infections, prioritize impact, and contain threats. Another example is reCAPTCHA Enterprise, which uses image noising capabilities to help protect your site from adversaries that leverage novel AI advances, greatly enhancing our defenses against bots. 

Adding intelligence to reduce toil

At Google Cloud, we help organizations modernize security wherever they are, in part by simplifying their security tools and controls whenever possible. Advances in generative AI can help reduce the number of tools organizations need to secure their vast attack surface areas and ultimately, empower systems to secure themselves. This will minimize the toil it takes to manage multiple environments, to generate security design and capabilities, and to generate security controls. Today, we’re announcing: 

• Assured OSS will use LLMs to help us add even more open-source software (OSS) packages to our OSS vulnerability management solution, which offers the same curated and vulnerability-tested packages that we use at Google.
• Mandiant Threat Intelligence AI, built on top of Mandiant’s massive threat graph, will leverage Sec-PaLM to quickly find, summarize, and act on threats relevant to your organization. 

These announcements build on existing capabilities that help customers centralize visibility and control, detect targets, and improve security across their platform. For example, Security Command Center (SCC) uses always-on machine learning to detect malicious scripts executing in the customer container environment and immediately alert the customer. In addition, Cloud Data Loss Prevention leverages machine learning to find and classify data, and with Confidential Computing you can collaborate on, train, and deploy sensitive and regulated AI models in the cloud, all while preserving confidentiality. 

Evolving how practitioners do security to close the talent gap 

At Google, we believe that to truly democratize security, we need to first acknowledge that AI will soon usher in a new era for security expertise that will profoundly impact how practitioners “do” security. Most people who are responsible for security — developers, system administrators, SRE, even junior analysts — are not security specialists by training. 

Imagine a world where novices and security experts are paired with AI expertise to free themselves from repetition and burnout, and accomplish tasks that seem impossible to us today. To help power this evolution, we’re embedding Sec-PaLM-based features that can make security more understandable while helping to improve effectiveness with exciting new capabilities in two of our solutions: 

• Chronicle AI: Chronicle customers will be able to search billions of security events and interact conversationally with the results, ask follow-up questions, and quickly generate detections, all without learning a new syntax or schema.
• Security Command Center AI: Security Command Center will translate complex attack graphs to human-readable explanations of attack exposure, including impacted assets and recommended mitigations. It will also provide AI-powered risk summaries for security, compliance, and privacy findings for Google Cloud.

These new releases bolster our existing efforts to tackle these issues through capabilities like IAM Recommender, which suggests permissions better suited to actual usage patterns. We will soon be augmenting this capability to cover organizational policies, further enabling the administrator to help improve the security posture of their organization. In addition, Mandiant Automated Defense applies machine learning to help reduce the repetitive Tier 1 alert triage problem and address alert fatigue. 

Offering availability 

VirusTotal Code Insight, available now in Preview, is our first example of putting Security AI Workbench to work for our customers. We will be rolling out other offerings to trusted testers in coming months, and they will be available in Preview more broadly this summer. Click here for the demo. 

Security AI Workbench, including Sec-PaLM and partner integrations, in addition to the product innovations described in our demo, are all building blocks for a larger effort to elevate security across the ecosystem. So far, that effort: 

• Provides assistive functions to rapidly develop IT generalist talent to Tier 1 security operator status in a way that wasn’t previously feasible. Security Command Center now can summarize threat intelligence insights and findings for Google Cloud, and Chronicle can quickly generate YARA-L rules or other detections. 
• Provides advanced functions such as iterative query and multivariate detection generation, conversational filtering and interaction with results, and smart case awareness to empower advanced Tier 2 and 3 security operators to focus on threat analysis instead of struggling with process and toil. Mandiant Threat Intelligence users now can elevate their core competencies to hunt, investigate, and remediate threats — using the same tools our own Mandiant experts use. 
• Fuses threat intelligence and AI-based analytic capabilities, which are unsurpassed in the market. VirusTotal Code Insight enables security teams to help gain insights and identify threats in suspicious code. This can significantly enhance their ability to detect and mitigate potential attacks.

However, this is just an initial step. We’ll continue to iterate and innovate, and we encourage customers and partners to leverage Security AI Workbench in new and exciting ways. Moving forward, we anticipate many new use cases to emerge over time. 

Building a safer future

While generative AI has recently captured the imagination, Sec-PaLM is based on years of foundational AI research by Google and DeepMind, and the deep expertise of our security teams. This work includes new efforts to expand our partner ecosystem to provide businesses with security capabilities at every layer of the cybersecurity stack. We have only just begun to realize the power of applying generative AI to security, and we look forward to continuing to leverage this expertise for our customers and drive advancements across the security community.

In fast-moving organizations, it’s not uncommon for cloud resources, including entire projects, to occasionally be forgotten about. Not only such unattended resources can be difficult to identify, but they also tend to create a lot of headaches for product teams down the road, including unnecessary waste and security risks. 

To help you prune your idle cloud resources, we’re excited to introduce Unattended Project Recommender. It’s a new feature of Active Assist that provides you with a one-stop shop for discovering, reclaiming, and shutting down unattended projects. With actionable and automatic recommendations, you no longer have to worry about wasting money or mitigating security risks presented by your idle resources. Unattended Project Recommender uses machine learning to identify, with a high degree of confidence, projects that are likely abandoned based on API and networking activity, billing, usage of cloud services, and other signals. This feature is available via the Recommender API today, making it easy for you to integrate with your company’s existing workflow management and communication tools, or export results to a BigQuery table for custom analysis.

Thousands of projects can be unattended in large organizations, presenting major security risks

Your cloud projects can go abandoned or unattended for a number of reasons — ranging from a test environment that’s no longer needed, to project cancellation, to project owner switching jobs, and more. Not only can such projects contribute to your cloud bill (waste) but they may contain security issues such as open firewalls or privileged service account keys that attackers can exploit to get a hold of your cloud resources for cryptocurrency mining or, worse, compromise your company’s sensitive data. These security risks tend to grow over time because the latest best practices and patches are usually not applied to unattended projects. 

We experience this issue here at Google, too… In fact, it has been on Google’s internal security team’s radar for some time now, so we joined forces and looked into this problem together, starting with our very own “google.com” organization cloud projects. We quickly found some projects that were unattended, but remediating this issue was easier said than done due to challenges in several areas:

• Detection: With lots of signals available to you via sources like Cloud Monitoring, what are the right ones you should look at (e.g. API, networking, user activity)? How can you tell the difference between an unattended project and a project that has a low level of activity by design (e.g. a “shell” project that holds an auth token)?
• Remediation: Once you have identified a project that seems abandoned, how do you go about ensuring that it’s indeed an unattended project? How do you reduce the risk of deleting something that might be essential to a production workload, causing irreversible data loss? How do you solve this at the scale of your entire organization, beyond a one-time cleanup? 

Over the course of 2021 we built and tested a Google-internal prototype first, cleaning up many of our internal unattended projects, and then worked with a number of Google Cloud customers to build and tune this feature based on real-life data (thank you to all of our early adopters for working with us and your generous feedback that helped us shape this feature!) It was not uncommon for us to come across organizations with thousands of unattended projects, and we’re very excited to bring Unattended Project Recommender to all customers, in public preview.

Discovering and acting on unattended project recommendations

Unattended Project Recommender analyzes usage activity across all projects under your organization, including the following data:

• API activity (e.g. service accounts with authentication activity, API calls consumed)
• Networking activity (ingress and egress)
• Billing activity (e.g. services with billable usage)
• User activity (e.g. active project owners)
• Cloud services usage (e.g. number of active VMs, BigQuery jobs, storage requests)

Based on these signals, it can generate recommendations to clean up projects that have low usage activity (where “low usage” is defined using a machine learning model that ranks projects in your organization by level of usage), or recommendations to reclaim projects that have high usage activity but no active project owners. Here’s what an example post-processed summary list of recommendations can look like for the “foobar” organization that has 3 projects:

  Project ID: demo-project-307815
Recommendation: CLEANUP_PROJECT

Project ID: new-project
Recommendation: N/A

Project ID: bobs-playground-project
Recommendation: RECLAIM_PROJECT

In addition to the recommendations, you can also examine the underlying project activity insights that the recommendations are based upon. The insights provide additional information that can be useful for integration with your organization’s existing workflows and automation (e.g. send an auto-generated email or chat message to project owners based on the list provided by the owners field). Here’s an example insight payload:

  content:
  activeAppengineInstanceDailyCount: 0
  activeCloudsqlInstanceDailyCount: 0
  activeGceInstanceDailyCount: 3
  activeServiceAccountDailyCount: 1
  apiClientDailyCount: 18922           // Daily average API calls produced
  bigqueryInflightJobDailyCount: 0
  bigqueryInflightQueryDailyCount: 0
  bigqueryStorageDailyBytes: 0
  bigqueryTableDailyCount: 0
  consumedApiDailyCount: 0             // Daily average API calls consumed
  datastoreApiDailyCount: 0
  gcsObjectDailyCount: 11
  gcsRequestDailyCount: 0
  gcsStorageDailyBytes: 2663548
  hasActiveOauthTokens: false          // OAuth tokens used in the last 180 days
  hasBillingAccount: true
  numActiveUserOwners: 1
  owners:                              // List of project owners
  - activeOnProject: false
    member: user:user1@example.com
  - activeOnProject: true
    member: user:user2@example.com
  serviceWithBillableUsage:
  – Cloud Storage
  - Compute Engine
  vpcEgressDailyBytes: 264456938       // Daily average VPC egress bytes
  vpcIngressDailyBytes: 392435047      // Daily average VPC ingress bytes
  usagePercentile: 20                  // Level of usage relative to other projects

GCP projects are used in many different ways and for many different purposes. In case you get a recommendation to delete a project that’s being used in a way that’s out of the scope for this feature, you can dismiss the recommendation and it will stop showing up for the given project. 

Restoring deleted projects

When you choose to shut down a project using the projects.delete() method, it gets marked for deletion. After a project is marked for deletion, it becomes unusable, all resources within that project are shut down, and a 30-day wait period for the project and all of its data to get fully deleted begins.

In case a useful project is accidentally shut down, you have the option to restore the project within that 30-day wait period. Since restoring allows you to recover most but not necessarily all of your project data and resources, we recommend carefully examining the utilization insights associated with a project and considering any additional utilization signals that may not be captured by the Unattended Project Recommender before taking the cleanup action.

Early customer success stories

A number of enterprise customers are already using Unattended Project Recommender to keep their organizations clean of unattended projects and resources.

Decathlon, a French sporting goods retailer, is excited for the insight Unattended Project Recommender will bring to their environment, and are already deploying it as a part of their latest cloud security initiatives.

“After a thorough test of this feature and the validation of our CISO, we ended up deleting our first 775 projects, and no one complained! A great help to improve our security. The next step for us will be to operationalize it at scale, and implement a company wide policy for unattended resource management.” —Adeline Villette, Cloud Security Officer

For Veolia, one of the world’s largest water, waste and energy management companies, not only does this feature reduce security risks and waste, but also helps drive cultural shift and alignment with its ecological transformation strategy.

“This feature allows us to reduce our costs and security debt on assets that are no longer in use, and is also fully in line with Veolia’s philosophy of limiting its carbon footprint. After having tested Unattended Project Recommender on more than 3,000 projects throughout our organization, we are looking to bring it as proactive alerts to our project owners at scale.”—Thomas Meriadec, Product Manager

Box, a secure cloud content management provider, views it as a foundation for building a repeatable process to remediate unused resources.

“Unattended Project Recommender is a great fit for us. It gives us a unified view of project usage across our entire organization and enables us to address security risks of legacy projects in a systematic and organized manner, ensuring an even safer environment.” —Matt Bowes, Staff Security Engineer

Getting started with the Unattended Project Recommender

To help you get started, we’ve prepared a Cloud Shell tutorial (source code) that you can use to find unattended project recommendations within your own Projects/Folders/Organization. Click this button to clone the tutorial from GitHub and run in your Cloud Shell environment:

As you can see, listing recommendations for your projects only takes a few clicks with the tutorial (special thanks to Lanre Ogunmola, Security & Compliance Specialist, for making this look so easy)! For additional detail on using the gcloud CLI or API to discover unattended project recommendations, please refer to the documentation page.

You can also automatically export all recommendations from your Organization to BigQuery and then investigate the recommendations with DataStudio or Looker, or use Connected Sheets that let you use Google Workspace Sheets to interact with the data stored in BigQuery without having to write SQL queries.

As with any other Recommender, you can choose to opt out of data processing at any time by disabling the appropriate data groups in the Transparency & control tab under Privacy & Security settings.

We hope that you can leverage Unattended Project Recommender to improve your cloud security posture and reduce cost, and can’t wait to hear your feedback and thoughts about this feature! Please feel free to reach us at active-assist-feedback@google.com and we also invite you to sign up for our Active Assist Trusted Tester Group if you would like to get early access to the newest features as they are developed.