Editor’s note: Today’s post is a Q&A with the VCCP London and VCCP CX team. VCCP London conceived of and built the Cadbury Worldwide Hide platform using Google Maps Platform as a way to get consumers ‘hiding’ eggs and engaging with loved ones during a time when they could not be physically together.

How did the team come up with the idea for the ‘Cadbury Worldwide Hide’?

VCCP London is the agency of record for Cadbury both locally in the UK and centrally with the Global team. So, when Cadbury briefed us in May for their Easter 2021 campaign, they wanted us to come up with a creative way to encourage people to hide eggs and get consumers excited about interacting with loved ones. At the time, the pandemic was constantly changing, and it was looking like we were going to continue to be in lock-down for the foreseeable future, into the Easter season.

We then came up with an idea: wouldn’t it be really cool if somehow you could still hide a real Easter egg for someone you love, but do it virtually. And then once it’s found, that real egg could be delivered to the seeker’s home. With the use of some creativity and technology, we brought this idea to life. The experience we developed allowed our users to purchase a real Cadbury Easter Egg, hide it virtually on the map in a special location, then write the recipient a personalized clue for him to find the egg. Once the seeker found the egg, they would receive a real, physical egg the hider bought for her delivered to her home.

We wanted it to be a truly meaningful one-to-one connection, to bring back some lovely memories for people, and to allow a real chocolate egg to be hidden for a loved one no matter where they were.

Why was this important to Cadbury?

Generosity is at the heart of Cadbury’s brand, and Easter is our opportunity to show that ‘there’s a glass and a half in everyone’. As we enter the second year of our campaign ‘Show you care, hide it’, we are flipping the Easter ritual on its head and showing that the generous act is in hiding an egg for someone you love.

Physical connection has been restricted by the global pandemic and that’s why this year’s Easter campaign sets out to connect people across the UK through the power of generosity.

Tell us a little bit about the technical side of the project. Which Google Maps Platform products did you use to create the user experience?

The Cadbury Worldwide Hide launched across 4 markets (UK, IE, AU, NZ) simultaneously. Integration with regional e-commerce and CRM partners brought the activation into the real world with chocolate eggs being delivered throughout the campaign as seekers found them.

Providing an engaging map experience to our users was key to the execution and by leveraging the Google Maps interface consumers already use on a daily basis, we were able to focus on our core campaign message. We built the platform using both the Maps Javascript API to render the 2D maps and the Street View API, which allowed users to hide their egg anywhere in the world for their loved one to find. We also used Place Autocomplete powered search allowing users to search for their favorite location while contextual hints kept hiders on track. Seekers were aided with a distance meter and hints system if they got stuck. Our Design and Engineering team used Google Maps Platform Cloud-based maps styling to customize the map.

To get the campaign to as many people as possible we prioritized accessibility throughout the site, from screen-reader support and relevant tab indexes through to full keyboard shortcuts within the map experience – allowing users to hide (or find!) their egg without ever using a mouse. Real user testing was done throughout the UX, design and development process to ensure best practices were being followed.

How long did it take the VCCP team to build-out the solution?

Discovery to the roll-out of the solution took about 7 months. Our Design and Engineering team started with a 4-week discovery phase in September 2020 where we developed a service blueprint that set the foundations of the project. By visualizing the entire process of a service from start to finish, listing all the activities that happened at each stage, and the different roles, actions, processes and systems involved, the blueprint allowed all stakeholders to align on the solution.

We started iterative cycles of development in November 2020, beginning with UX (prototype for user testing), UI (look and feel and customization of Google Maps using Cloud-based Maps styling), and then kicked off front end and back end development in December. We launched the Cadbury Worldwide Hide platform in early March 2021—just in time for millions of users around the world to enjoy ahead of Easter.

Did you experience any challenges as you developed the experience?

The biggest challenge was actually around adapting to change in plans in response to the desire to launch the platform across more markets than originally intended. During the development phase, we rapidly scaled up to develop the platform for Ireland, Australia and New Zealand in addition to the UK within the same timeframe.

What results were you able to achieve and how did you measure the success of the project?

One week before Easter Sunday, we had sold out of Cadbury Worldwide Hide chocolate eggs. There were over 2.26 million site visits with an average time spent on the platform of almost five minutes. Over 809k virtual eggs were hidden in total and 14.5k real Cadbury Easter eggs bought. The Cadbury Worldwide Hide platform was the number one Mondelēz International website globally, and a couple even used the platform for a marriage proposal!

Would you recommend this type of campaign and user engagement to other B-to-C brands, if so, why?

Direct to consumer capabilities are increasingly important for brands, particularly in the FMCG (Fast Moving Consumer Goods) space. Local lockdowns and restrictions on physical retail have accelerated our adoption of ecommerce. Not only have brands had to adapt quickly, but consumers are beginning to expect direct-to-consumer capabilities from their favorite brands. Cadbury recognized this behavior shift early. What the Cadbury Worldwide Hide did well was to innovate beyond the traditional DTC and ecommerce experience by gamifying the platform and enabling moments of human connection at a time when physical connection was impossible. 

What advice would you give to other agencies or brands thinking about creating user experiences with Google Maps Platform?

We learned a great deal taking on this project. Here are just a few highlights:

• Assume anything is possible.
• Our ‘Mobile First’ approach allowed consumers to access the platform from any device with consistent, engaging brand experience.
• Think big and beyond the traditional use of Google Maps and treat it as a foundation platform to build upon.
• Prototype and test early to validate your hypotheses. We created a technical proof of concept which enabled us to test using ‘real’ Google Maps and real people early in our design process.
• Don’t assume everything is accessible to everyone. You may need to build upon the ‘out the box’ functionality to ensure as many people as possible can use your solution.

For more information on Google Maps Platform, visit our website.

Picking the right database for your application is not easy. The choice depends heavily on your use case—transactional processing, analytical processing, in-memory database, and so on—but it also depends on other factors. This post covers the different database options available within Google Cloud across relational (SQL) and non-relational (NoSQL) databases and explains which use cases are best suited for each database option. 

Relational databases 

In relational databases information is stored in tables, rows and columns, which typically works best for structured data. As a result they are used for applications in which the structure of the data does not change often. SQL (Structured Query Language) is used when interacting with most relational databases. They offer ACID consistency mode for the data, which means:

• Atomic: All operations in a transaction succeed or the operation is rolled back.
• Consistent: On the completion of a transaction, the database is structurally sound.
• Isolated: Transactions do not contend with one another. Contentious access to data is moderated by the database so that transactions appear to run sequentially.
• Durable: The results of applying a transaction are permanent, even in the presence of failures.

Because of these properties, relational databases are used in applications that require high accuracy and for transactional queries such as financial and retail transactions. For example: In banking when a customer makes a funds transfer request, you want to make sure the transaction is possible and it actually happens on the most up-to-date account balance, in this case an error or resubmit request is likely fine.

There are three relational database options in Google Cloud: Cloud SQL, Cloud Spanner, and Bare Metal Solution.

Cloud SQL: Provides managed MySQL, PostgreSQL and SQL Server databases on Google Cloud. It reduces maintenance cost and automates database provisioning, storage capacity management, back ups, and out-of-the-box high availability and disaster recovery/failover. For these reasons it is best for general-purpose web frameworks, CRM, ERP, SaaS and e-commerce applications.

Cloud Spanner: Cloud Spanner is an enterprise-grade, globally-distributed, and strongly-consistent database that offers up to 99.999% availability, built specifically to combine the benefits of relational database structure with non-relational horizontal scale. It is a unique database that combines ACID transactions, SQL queries, and relational structure with the scalability that you typically associate with non-relational or NoSQL databases. As a result, Spanner is best used for applications such as gaming, payment solutions, global financial ledgers, retail banking and inventory management that require ability to scale limitlessly with strong-consistency and high-availability. 

Bare Metal Solution: Provides hardware to run specialized workloads with low latency on Google Cloud. This is specifically useful if there is an Oracle database that you want to lift and shift into Google Cloud. This enables data center retirements and paves a path to modernize legacy applications. 

Non-relational databases

Non-relational databases (or NoSQL databases) store compex, unstructured data in a non-tabular form such as documents. Non-relational databases are often used when large quantities of complex and diverse data need to be organized. Unlike relational databases, they perform faster because a query doesn’t have to access several tables to deliver an answer, making them ideal for storing data that may change frequently or for applications that handle many different kinds of data. 

For example, an apparel store might have a database in which shirts have their own document containing all of their information, including size, brand, and color with room for adding more parameters later such as sleeve size, collars, and so on.

Qualities that make NoSQL databases fast:

• Eventual consistency: stores usually exhibit consistency at some later point (e.g., lazily at read time)
• Horizontal scaling, usually using hashed distributions
• Typically, they are optimized for a specific workload pattern (i.e., key-value, graph, wide-column)
• Typically, they don’t support cross shard transactions or flexible isolation modes.

Because of these properties, non-relational databases are used in applications that require large scale, reliability, availability, and frequent data changes.They can easily scale horizontally by adding more servers, unlike some relational databases, which scale vertically by increasing the machine size as the data grows. Although, some relations databases such as Cloud Spanner support scale-out and strict consistency.

Non-relational databases can store a variety of unstructured data such as documents, key-value, graphs, wide columns, and more. Here are your non-relational database options in Google Cloud: 

• Document databases: Store information as documents (in formats such as JSON and XML). For example: Firestore
• Key-value stores: Group associated data in collections with records that are identified with unique keys for easy retrieval. Key-value stores have just enough structure to mirror the value of relational databases while still preserving the benefits of NoSQL. For example: Datastore, Bigtable, Memorystore
• In-memory database: Purpose-built database that relies primarily on memory for data storage. These are designed to attain minimal response time by eliminating the need to access disks. They are ideal for applications that require microsecond response times and can have large spikes in traffic. For example: Memorystore
• Wide-column databases: Use the tabular format but allow a wide variance in how data is named and formatted in each row, even in the same table. They have some basic structure while preserving a lot of flexibility. For example: Bigtable
• Graph databases: Use graph structures to define the relationships between stored data points; useful for identifying patterns in unstructured and semi-structured information. For example: JanusGraph

There are three non-relational databases in Google Cloud:

• Firestore: Is a serverless document database which scales on demand and acts as a backend-as-a-service. It is DBaaS that increases the speed of building applications. It is perfect for all general purpose uses cases such as ecommerce, gaming, IoT and real time dashboards. With Firestore users can interact with and collaborate on live and offline data making it great for real-time application and mobile apps.  
• Cloud Bigtable: Cloud Bigtable is a sparsely populated table that can scale to billions of rows and thousands of columns, enabling you to store terabytes or even petabytes of data. It is ideal for storing very large amounts of single-keyed data with very low latency. It supports high read and write throughput at sub-millisecond latency, and it is an ideal data source for MapReduce operations. It also supports the open-source HBase API standard to easily integrate with the Apache ecosystem including HBase, Beam, Hadoop and Spark along with Google Cloud ecosystem.
• Memorystore: Memorystore is a fully managed in-memory data store service for Redis and Memcached at Google Cloud. It is best for in-memory and transient data stores and automates the complex tasks of provisioning, replication, failover, and patching so you can spend more time coding. Because it offers extremely low latency and high performance, Memorystore is great for web and mobile, gaming, leaderboard, social, chat, and news feed applications.

Conclusion

Choosing a relational or a non-relational database largely depends on the use case. Broadly, if your application requires ACID transactions and your data structure is not going to change much, select a relational database. 

In Google Cloud use Cloud SQL for any general-purpose SQL database and Cloud Spanner for large-scale globally scalable, strongly consistent use cases. In general, if your data structure may change later and if scale and availability is a bigger requirement than consistency then a non-relational database is a preferable choice.  Google Cloud offers Firestore, Memorystore, and Cloud Bigtable to support a variety of use cases across the document, key-value, and wide column database spectrum.

For more comparison resources on each database check out the overview. For more hands-on experience with Bigtable, check out our on-demand training here and learn about migrating databases to managed services check out this whitepaper.  

https://youtube.com/watch?v=2TZXSnCTd7E%3Fenablejsapi%3D1%26

For more #GCPSketchnote, follow the GitHub repo. For similar cloud content follow me on Twitter @pvergadia and keep an eye out on thecloudgirl.dev.

Time to go outside again, I guess. I’ll need a sun hat. Sunscreen. Maybe some new sandals? What else?

With the Recommendations AI service, I might be reminded to grab a reusable water bottle and a swimsuit. Or some after-sun aloe lotion. Good thing, cause I’ll need it.

Recommendations AI is a solution that uses machine learning to bring product recommendations to their shoppers across any catalog or client list. This service is part of our full suite of Retail solutions. When you integrate with the Retail API, you get the benefit of Google’s Product Discovery. Integrating once to reap the benefits over and over. Recommendations is the starting point, and you can easily extend into Retail Search and Vision Product Search in the upcoming future. 

The Recommendations solution is fully managed, global-scale and powered by deep learning, so you can focus on a great shopping experience and let someone else worry about the infrastructure.

Compared to baseline recommendation systems used by customers, Recommendations AI showed double digit uplift in conversion and clickthrough rates in A/B experiments controlled by the customers. You can optimize for click-through, conversion or session revenue, and fine tune the models to make sure you omit out-of-stock items or duplicates, for example.

So how does it work, and how do you get started? Read on, and we’ll walk you through the pipeline, starting with the data you already have to placement in your online store. 

Formula: Data -> Model -> Placement

You start with your catalog, the list of all the things (postcards, movies, pie recipes) that you want to show your customers. Then you ingest your PII-redacted user events -this is the historic event data like home page views, add to cart events and more along with real time user events. This user event is joined with the product catalog and items that allows us to construct the sequence of shoppers’ activity, thus being able to predict what the shopper has a high propensity to purchase next. The user events can come from both online activity across devices or offline store purchases

The recommendation model will return a list of products, which are the recommendations. The brains of the operation, if you will. This model is trained using all the data that you ingest, using the latest neural network models and techniques that Google has built expertise over the years in flagship products like Youtube and News, that allows us to uncover shopper intent,  so it can best predict the right recommendations to show to the right people.

Every model outputs a list of product identifiers, but where do they go? They go into placements, the spots, panels, carousels on your customer’s journey interacting with your brand that you’ve set aside to highlight recommendations. A model can send recommendations to one or more placements, but each placement only receives information from one recommendation model. Your pages will then need to render the products with the right images, text or other metadata, using the product ID that is returned by the model.

What do recommendations look like?

Let’s start by browsing our postcard-selling website, where I’ve been buying some vintage California postcards already. The recommendations algorithm has caught on to my interest, showing me other potential cards to purchase based on my history:

Put your data to work

To get started we need to bring your data into the recommendation model, so it can understand your customers, your inventory, and your sales patterns. 

The model takes in the product catalog you use, and metadata about those products to better understand nuances in assortment, pricing and variables like size and style. You might already have this data stored in BigQuery or Merchant Center, and hence we provide easy integrations that you can leverage to get started even faster.

As for the user events, don’t worry if you already have systems in place to capture web and mobile activity. We make it easy to bring in your real time event logs by providing seamless integrations with Google Tag Manager, Javascript pixel, or even historic events from Cloud Storage, BigQuery or using inline API or JSON, so you can immediately train the models on this imported  data. All this allows you to kickstart integrating with Recommendations AI in a matter of days.

The models then construct a sequence of activities that the user went through and joins with the products that the user engaged with. Once your data is ready to go, it takes a few days to train the model. Next onto making the data work for you.

Quickly customize your model

Setting up your own recommendations project in the console gives you the ability to choose what sort of model to train (based on what recommendations you want to generate) and your objective. Are you optimizing for click-through rate–more people click on the recommendation links or products–or for conversion rate–more people choose or buy what was suggested or revenue ?

Different models can be optimized for different optimization goals.; the GCP console explains what each one can do and how you can choose to optimize it.

Let’s unpack some of this terminology real quick.

We’ve got three model types:

• Recommended for you – Means we think these are items you’ll want to buy, based on your history; this is usually used on a home page to showcase items.
• Others you may like – Means if you’re browsing the page of a water bottle, we will recommend  alternative brands of water bottles that you may like as well as a backpack, based on your engagement  history.
• Frequently bought together – Means that when anyone buys sunscreen, we notice that they often also buy aloe lotion, so we will surface those items when someone adds any one of them to their cart.

And then we have three business objectives that the models optimize for:

• Click-through rate – How frequently did somebody click on a recommended item?
• Conversion rate– How frequently did somebody add a recommended item to their cart?
• Revenue per session – How much money did the recommendations generate for you?

Deliver anywhere along the journey

Now that you’re all set up in the Retail AI console, you can test out the recommendations in the console, even before you deploy to production.

You can integrate Recommendations into your frontend by calling the Predict APIt. The placements of recommendations will report data back into the dashboard and you can analyze and measure success for future iterations. 

On top of that you can use the recommendations for other parts of your customer’s journey. Email promotions, storefront kiosks, display ads or follow-up notifications can include recommendations based on past activity and cart contents. The model gives you useful product recommendations for a wide variety of touchpoints and steps in the purchasing process.

More best practices, and guides, are available inside our documentation.

How to get started

Training your own models can be tedious, time-consuming, and expensive. On top of that it requires deeper data science expertise to set up. Let us do it instead!

You can see how IKEA Retail uses Recommendations AI in this recent talk and blog from the Google Cloud Retail Summit..

To get started today you’ll need to make a Cloud project and enable the Retail API, which then allows you to access all the recommendation tools in one menu. Bring in your catalog and purchasing data, define a placement or two, and you can start putting recommendations on your site in a matter of days.

Google Cloud Results

• Replaced conflicting, time-consuming paper logs with a near real-time, transparent app
• Scaled rapidly and easily to accommodate new teams thanks to Google App Engine
• Enables photographic and video communication to replace confusing text
• New factory solution rolled out in just 15 days

In 1877, Pietro Barilla set up a small bakery to make pasta and baked goods for the people of Parma, Italy. Today, Barilla applies its 140 years of baking knowledge on a global scale, with six major manufacturing sites in Italy and an international network employing over 8,000 people. As the world’s leading producer of pasta, Barilla knows that when it comes to making quality food, great communication is key. That’s why the company plans to become a completely digital, looking to technology to improve the way it works.

Teams at the Barilla factory in Cremona work along a production line more than one-kilometer long, staffed by three shifts of workers a day. When one shift handed over to the next or requested machine maintenance teams, they used paper notebooks and unofficial instant messaging to communicate. That meant there was no authoritative, real-time record of events, communication was messy, oversight was poor, and teams had to hold daily morning meetings to synchronise notes.

Barilla worked with the Google Cloud Partner Injenia to create a solution, beginning with a consultative process on the factory floor.

“We had the idea to to start from the bottom and work up,” says Cristiano Boscato at Injenia. “Barilla’s top staff were brilliant about letting us do it. Eight of us from Injenia spent months on the factory lines with Barilla workers, collecting ideas on Google Docs, making presentations with Slides and collecting feedback with Forms. The CollaborAction app we created is the result of an amazing partnership.”

Co-designing a team social network

“Everything at the Cremona plant was managed offline, with paper,” explains Alessandra Ardrizzoia, Digital Engagement Senior Manager at Barilla. “Workers on the line would track events in notebooks, the shift leader would have another notebook, and the leader of the maintenance team would have yet another notebook. Everybody wrote their own text description of events, so there would be mismatches in the information going around.”

To resolve this, teams would meet at 8:30am every day to reconstruct a consistent narrative. In addition, machine maintenance workers were already using instant messaging to communicate with the line. Barilla and Injenia looked for a solution that could deliver a searchable, single version of events, with the ease of use of a mobile messaging application.

After consulting factory workers for ideas, Injenia created CollaborAction, a custom-built app that brought G Suite collaboration tools together on an Google App Engine platform, using Google Cloud SQL to index files. Google+, Google Drive and Hangouts were not only highly available and easy-to-use, they also “helped with fast adoption, with interfaces that workers could already relate to.” Meanwhile Google App Engine enabled the Injenia team to deliver updates and new versions at speed, as part of a feedback process with workers who offered suggestions through a link to Forms embedded in the app.

Google+ provides an intuitive social media dashboard that workers felt comfortable with. Now teams use company tablets placed at intervals along the line to log in, report issues to other teams, photograph problems, schedule maintenance, give status updates through Hangouts chat, and have visibility on the whole process as it takes place.

“Everyone in the Cremona plant was really happy with the new social collaboration process. Because they were involved in designing the solution, they felt involved and really engaged with the process,” says Alessandra. “And now that everyone is aligned with CollaborAction, all the work in the plant is more effective. They are more agile and can use their time in more added-value activities.”

Optimization and a national roll-out

Created in Cremona, now CollaborAction connects over 1,000 users in six of Barilla’s factories in Italy. “The pilot at Cremona took one month, and adoption has been easier and faster in every plant we’ve taken it to,” says Cristiano. “We have another five or six plants more, and it takes no more than 15 days to introduce. That’s incredible.”

Because CollaborAction is a mobile app built on Google App Engine, scaling to meet new demand has been simple. Now maintenance teams use the app on smartphones, line workers use it on tablets, and shift leaders use it on laptops, so the entire team is aligned in close to real-time on a single version of events. And now teams communicate with video and photographs as well as text, there’s less room for confusion, as Alessandra explains. “It’s no problem understanding what’s happening in a video or picture, compared to a message that just says ‘something is going wrong.’ On a production line, where one part leads into the next, that speed makes a difference, and means we don’t have to throw as much food away when something breaks down.”

“Now we’re collecting feedback from all of the plants using CollaborAction and using it to create a standardised solution that we can apply across all of our plants,” says Alessandra. “We’re side-by-side with the workers in that sense, trying to address their needs with new features. It’s a way to make the workers feel like part of the solution, and that the app represents their needs and their voice.”

Solving a universal problem

By the end of 2018, Barilla and Injenia aim to have deployed CollaborAction to 2,700 employees at 18 factories worldwide. Barilla has already collected more than 50,000 posts with the app, including around 20,000 photographs and videos, and is now considering ways to apply Cloud Machine Learning Engine to create a maintenance chatbot or direct IoT connection with machinery.

“CollaborAction hasn’t just made our maintenance processes faster and more efficient, its also exponentially increased the knowledge and understanding employees have about their work,” says Alessandra. “It’s improving team spirit, too, such as when employees use CollaborAction to arrange to play soccer. It’s become the main communication tool for the entire plant.”

I’m excited to share our first Cloud CISO Perspectives post of 2022. It’s already shaping up to be an eventful year for our industry and we’re only in month one. There’s a lot to recap in this post, including the U.S. government’s recent efforts to address critical security issues, like open source software security and zero trust architectures. We’ve also released new resources from our Google Cybersecurity Action Team like the Cloud Security Megatrends and the Boards of Directors whitepaper on cloud risk governance. 

Cloud Security Megatrends 

We’re often asked if the cloud is more secure than on-prem (and why) so we shared our answer in a recent blog post. At Google Cloud, security by design is our priority. We’ve long adopted zero-trust principles for our baseline security architectures and built a global network that relies on defense in depth layers to protect against configuration errors and attacks. But security is always evolving and that is why we also take advantage of the following megatrends:

1. Economy of scale: Decreasing the marginal cost of security raises the baseline level of security. 
2. Shared fate: A flywheel of increasing trust drives more transition to the cloud, which compels even higher security and even more skin-in-the-game from the cloud provider.
3. Healthy competition: The race by deep-pocketed cloud providers to create and implement leading security technologies is the tip of the spear of innovation. 
4. Cloud as the digital immune system: Every security update the cloud gives the customer is informed by some threat, vulnerability, or new attack technique often identified by someone else’s experience. Enterprise IT leaders use this accelerating feedback loop to get better protection.
5. Software-defined infrastructure: Cloud is software defined, so it can be dynamically configured without customers having to manage hardware placement or cope with administrative toil. From a security standpoint, that means specifying security policies as code, and continuously monitoring their effectiveness.
6. Increasing deployment velocity: Because of cloud’s vast scale, providers have had to automate software deployments and updates, usually with automated continuous integration/continuous deployment (CI/CD) systems. That same automation delivers security enhancements, resulting in more frequent security updates.
7. Simplicity: Cloud becomes an abstraction-generating machine for identifying, creating and deploying simpler default modes of operating securely and autonomically. 
8. Sovereignty meets sustainability: The cloud’s global scale and ability to operate in localized and distributed ways creates three pillars of sovereignty. This global scale can also be leveraged to improve energy efficiency.

If you’re an IT decision maker, pay attention to these megatrends that will continue to drive and reinforce cloud security and will outpace the security of on-prem infrastructure well into the future. 

U.S. Federal government cybersecurity momentum 

• Open source software security: Earlier this month, Google participated in the White House Summit on open source software security. The meeting came at a critical time for the industry following December’s Log4j vulnerabilities and was both a recognition of the challenge and an important first step towards addressing it. The open source software ecosystem is not homogenous, despite the fact that the industry often thinks of or treats it this way. Some of it, like Linux, is highly curated, while other critical software is supported through diffuse communities including technology companies and other stakeholders. There is also a long tail of many other critical projects driven by a dedicated community of maintainers around the world, including Googlers. In light of this reality, we welcomed the chance to share our recommendations to advance the future of open source software security. Some work we’ve done includes founding the Open Source Security Foundation, which has been instrumental already in making security improvements. We’ve also helped drive a number of key security initiatives within the open source community including security scorecards, the SLSA framework to improve the security and integrity of open source packages, and Secure Open Source Rewards to financially incentivize improvements to critical open source security projects.  
• OMB’s Federal zero trust strategy: The publication of the Office of Management and Budget’s zero trust architecture strategy marks an important step for the U.S. federal government’s efforts to modernize under Executive Order 14028. Google Cloud supports this approach, which recognizes the immense security benefits offered by modern computing architectures. For the past decade, Google has successfully applied zero trust principles through our BeyondCorp and BeyondProd frameworks for providing end-user access and securing our cloud workloads. And we’ve brought these best practices from our own journey to global governments and businesses of any size through solutions like BeyondCorp Enterprise and capabilities like Binary Authorization and Anthos Service Mesh, which are embedded in Anthos, our managed application platform. For Federal agencies embarking on this zero trust journey, the Google Cybersecurity Action Team will offer our expertise by conducting Zero Trust Foundations strategy workshops, which can help organizations in the public and private sectors develop actionable and achievable strategies and plans for zero trust implementation. 

Google Cybersecurity Action Team Highlights 

Here are the latest updates, products, services and resources across our security teams this month: 

Security

• Democratizing security operations: We recently announced that Siemplify, a leading security orchestration, automation and response (SOAR) provider, is joining Google Cloud to help companies better manage their threat response. Providing a proven SOAR capability with Chronicle’s approach to security analytics is an important step forward in our vision to advance invisible security and democratize security operations for every organization.
• Security by design: The Highmark Health security team is using “secure-by-design” techniques to address the security, privacy, and compliance aspects of its Living Health solution with Google Cloud’s Professional Services Organization (PSO). Google has long advocated for and followed security by design principles, which is why we’re continuously building enhanced security, controls, resiliency and more into our cloud products and services. 
• Secure collaboration for hybrid work environments: The Google Workspace team shared its recommendations for businesses as they prepare for the future of work,  where the hybrid/flexible work model is becoming standard practice and a new approach to security is essential.
• Anthos Policy Controller CIS Benchmark enforcement: A big part of our shared fate philosophy is to build secure products and not just security products. A recent example of this in action is embedding CIS benchmark policy conformance in the Anthos Policy Controller. We believe the more we embed approaches like this into our products, the more application and infrastructure teams can intrinsically embed security at the start and reduce toil for the security team.
• DevOps for technology-driven organizations and startups: A key success factor for many security programs is the partnership and integration with development teams, and there are some great resources and lessons in our DORA research.
• Security by design with Chrome OS: ABN AMRO’s Asia-Pacific region team recently shared how they are using Chrome OS and CloudReady to work securely in the cloud, reduce total cost of ownership, and add flexibility for employees. This is a great example of secure by design principles in the use of Chromium.

Risk & Compliance

• Boards of Directors summary guide to cloud risk governance: The latest whitepaper from the Google Cybersecurity Action Team outlines how boards of directors can prioritize safe, secure, and compliant adoption processes for cloud technologies within their organizations.  
• TruSight Risk Assessment of Google Cloud: TruSight recently released a comprehensive
  risk assessment report on Google Cloud. Our Enterprise Trust team collaborated on this robust assessment of Google Cloud services to validate the design and implementation of controls. TruSight’s risk assessment of our security controls will help customers accelerate and complete their risk management due diligence.
• Data governance: Check out this new blog series on data governance where our teams explain the role of data governance, its importance, and the necessary processes to run an effective data governance program. Implementing data governance will help maximize value derived from business data, build user trust, and ensure compliance with required security measures.

Controls and Products

• Encrypting Data Fusion: To help meet the security, privacy and compliance requirements of customers in regulated industries like finance or public sector, we announced the general availability of Customer Managed Encryption Keys (CMEK) integration for Cloud Data Fusion, which enables encryption of both user data and metadata at rest with a key that customers can control through our Cloud Key Management Service (KMS). 

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