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An explosion of SaaS applications over the last decade has fundamentally changed the security landscape of modern enterprises. According to the Cloud Security Threat Report1, the average organization uses hundreds, possibly upwards of 1,000 of SaaS applications, many of these unsanctioned by IT departments, and this number is only forecasted to increase. Today, we see many organizations trying to secure modern SaaS applications with a legacy, network-based approach, where access might only be given if a user is on the corporate network or connecting through a VPN. But conventional castle-and-moat security strategies are no longer adequate; the network from which you access resources is no longer a reliable indicator of trusted access. This paradigm shift has led to increased adoption of the zero trust model, where no person, device, or network enjoys inherent trust. Instead, trust, which allows access to applications and information, must be earned by demonstrating criteria such as identity and other factors, through policies set by administrators.Transitioning to a zero trust model is no easy feat, which is why we recently introduced BeyondCorp Enterprise to help our customers with this challenge. BeyondCorp Enterprise allows users to implement a zero trust approach based on the same principles we use at Google and manage access to their SaaS applications hosted on Google Cloud, in other clouds, or on-premises. And now, in light of the increase in remote work, secure access to applications has never been a more relevant conversation.BeyondCorp Enterprise makes it easy to enforce granular access policies based on a user’s identity, organizational group, device health, encryption status, geographic origin, form of authentication, and more. But application access is only one part of our zero trust approach; once a user has access to an app, we also want to make sure their data is protected. BeyondCorp Enterprise includes new threat and data protection services, giving users an added layer of security, integrated directly in the browser without the need for an agent.Transitioning to a zero trust model can be a journey, but our solutions can help you get started quickly and easily. One way to do this is to think about your deployment and take a targeted approach, for instance, starting with a group of specific users or a set of SaaS apps you want to secure. For instance, you could think about frontline workers who may only need to access a point-of-sale application, or maybe your organization has a large customer service operation and those employees only need to access call center software. These use cases and ones similar are a great first step as they are straightforward and the use of VPN is almost certainly unnecessary.Our new whitepaper, “Secure access to SaaS applications with BeyondCorp Enterprise,” outlines common scenarios for IT leaders to consider, and provides guidance for how they can approach each one. As with any new deployment, there are a number of security factors organizations must consider, such as:How to govern zero trust access to sanctioned SaaS applicationsHow to prevent leakage of sensitive data from SaaS applicationsHow to prevent malware transfers and lateral movements via sanctioned applicationsHow to prevent visits to phishing URLs embedded in application contentWe dive deeper into each of these, as well as a selection of other scenarios, in the whitepaper. Read it here, and learn more about BeyondCorp Enterprise in our on-demand overview webinar or our product page. 1. Cloud Security Threat ReportRelated ArticleBeyondCorp Enterprise: Introducing a safer era of computingThe GA of Google’s comprehensive zero trust product offering, BeyondCorp Enterprise, brings this modern, proven technology to organizatio…Read Article
Quelle: Google Cloud Platform

9. April 2021

da Agency

Fintech startup, Branch makes data analytics easy with BigQuery

Editor’s note: Here we take a look at how Branch, a fintech startup, built their data platform with BigQuery and other Google Cloud solutions that democratized data for their analysts and scientists. As a startup in the fintech sector, Branch helps redefine the future of work by building innovative, simple-to-use tech solutions. We’re an employer payments platform, helping businesses provide faster pay and fee-free digital banking to their employees. As head of the Behavioral and Data Science team, I was tapped last year to build out Branch’s team and data platform. I brought my enthusiasm for Google Cloud and its easy-to-use solutions to the first day on the job. We chose Google Cloud for ease-of-use, data & savings I had worked with Google Cloud previously, and one of the primary mandates from our CTO was “Google Cloud-first,” with the larger goal of simplifying unnecessary complexity in the system architecture and controlling the costs associated with being on multiple cloud platforms. From the start, Google Cloud’s suite of solutions supported my vision of how to design a data team. There’s no one-size-fits-all approach. It starts with asking questions: what does Branch need? Which stage are we at? Will we be distributed or centralized? But above all, what parameters in the product will need to be optimized with analytics and data science approaches? With team design, product parameterization is critical. With a product-driven company, the data science team can be most effective by tuning a product’s parameters—for example, a recommendation engine for an ecommerce site is driven by algorithms and underlying models that are updating parameters. “Show X to this type of person but Y to this type of person,” X and Y are the parameters optimized by modeling behavioral patterns. Data scientists behind the scenes can run models as to how that engine should work, and determine which changes are needed. By focusing on tuning parameters, the team is designed around determining and optimizing an objective function. That of course relies heavily on the data behind it. How do we label the outcome variable? Is a whole labeling service required? Is it clean data with a pipeline that won’t require a lot of engineering work? What data augmentation will be needed? With that data science team design envisioned, I started by focusing on user behavior—deciding how to monitor and track it, how to partner with the product team to ensure it’s in line with the product objectives, then spinning up A/B testing and monitoring. On the optimization side, transaction monitoring is critical in fintech. We need to look for low-probability events and abnormal patterns in the data, and then take action, either reaching out to the user as quickly as possible to inform them, or stopping the transaction directly. In the design phase, we need to determine if these actions need to be done in real-time or after the fact. Is it useful to the user to have that information in real time? For example, if we are working to encourage engagement, and we miss an event or an interaction, it’s not the end of the world. It’s different with a fraud monitoring system, for which you’ve got to be much more strict about real-time notifications.Our data infrastructureThere are many use cases at Branch for data cloud technologies from Google Cloud. One is with “basic” data work. It’s been incredibly easy to use BigQuery, Google’s serverless data warehouse, which is where we’ve replicated all of our SQL databases, and Cloud Scheduler, the fully managed enterprise-grade cron job scheduler. These two tools, working together, make it easy to organize data pipelining. And because of their deep integration, they play well with other Google Cloud solutions like Cloud Composer and Dataform, as well as with services, like Airflow, from other providers. Especially for us as a startup, the whole Google Cloud suite of products accelerates the process of getting established and up and running, so we can perform the “bread-and-butter” work of data science. We also use BigQuery as a holder of heavier stats, and we train our models there, weekly, monthly, nightly, depending on how much data we collect. Then we leverage the messaging and ingestion tool Pub/Sub and its event systems to get the response in real time. We evaluate the output for that model in a Dataproc cluster or Dataform, and run all of that in Python notebooks, which can call out to BigQuery to train a model, or get evaluated and pass the event system through.Full integration of data solutionsAt the next level, you need to push data out to your internal teams. We are growing and evolving, so I looked for ways to save on costs during this transition. We do a heavy amount of work in Google Sheets because it integrates well with other Google services, getting data and visuals out to the people who need them; enabling them to access raw data and refresh as needed. Google Groups also makes it easy to restrict access to data tables, which is a vital concern in the fintech space. The infrastructure management and integration of Google Groups make it super useful. If an employee departs the organization, we can easily delete or control their level of access. We can add new employees to a group that has a certain level of rights, or read and write access to the underlying databases. As we grow with Google Cloud, I also envision being able to track the user levels, including who’s running which SQLs and who’s straining the database and raising our costs. A streamlined data science team saves costsI’d estimate that Google Cloud’s solutions have saved us the equivalent of one full-time engineer we’d otherwise need to hire to link the various tools together, making sure that they are functional and adding more monitoring. Because of the fully managed features of many of Google Cloud’s products, that work is done for us, and we can focus on expanding our customer products. We’re now 100% Google Cloud for all production systems, having consolidated from IBM, AWS, and other cloud point solutions. For example, Branch is now expanding financial wellness offerings for our customers to encourage better financial behavior through transaction monitoring, forecasting their spend and deposits, and notifying them of risks or anomalies. With those products and others, we’ll be using and benefiting from the speed, scalability, and ease of use of Google Cloud solutions, where they always keep data—and data teams—top of mind. Learn more about Branch. Curious about other use cases for BigQuery? Read how retailers can use BigQuery ML to create demand forecasting models.Related ArticleInventory management with BigQuery and Cloud RunBuilding a simple inventory management system with Cloud Run and BigQueryRead Article
Quelle: Google Cloud Platform