How we’re helping to reshape the software supply chain ecosystem securely

As we start the new year, we see ongoing revelations about an attack involving SolarWinds and others, that in turn led to the compromise of numerous other organizations. Software supply chain attacks like this pose a serious threat to governments, companies, non-profits, and individuals alike. At Google, we work around the clock to protect our users and customers. Based on what is known about the attack today, we are confident that no Google systems were affected by the SolarWinds event. We make very limited use of the affected software and services, and our approach to mitigating supply chain security risks meant that any incidental use was limited and contained. These controls were bolstered by sophisticated monitoring of our networks and systems. Beyond this specific attack, we remain focused on defending against all forms of supply chain risk and feel a deep responsibility to collaborate on solutions that benefit our customers and the common good of the industry. That’s why today we want to share some of the security best practices we employ and investments we make in secure software development and supply chain risk management. These key elements of our security and risk programs include our efforts to develop and deploy software safely at Google, design and build a trusted cloud environment to deliver defense-in-depth at scale, advocate for modern security architectures, and advance industry-wide security initiatives. To protect the software products and solutions we provide our cloud customers, we have to mitigate potential security risks, no matter how small, for our own employees and systems. To do this, we have modernized the technology stack to provide a more defensible environment that we can protect at scale. For example, modern security architectures like BeyondCorp allow our employees to work securely from anywhere, security keys have effectively eliminated password phishing attacks against our employees, and Chrome OS was built by design to be more resilient against malware. By building a strong foundation for our employees to work from, we are well-prepared to address key issues, such as software supply chain security. Many of these topics are covered more extensively in our book Building Secure and Reliable Systems.How we develop and deploy software and hardware safely at Google Developing software safely starts with providing secure infrastructure and requires the right tools and processes to help our developers avoid predictable security mistakes. For example, we make use of secure development and continuous testing frameworks to detect and avoid common programming mistakes. Our embedded security-by-default approach also considers a wide variety of attack vectors on the development process itself, including supply chain risks. A few examples of how we tackle the challenge of developing software safely: Trusted Cloud Computing: Google Cloud’s infrastructure is designed to deliver defense-in-depth at scale, which means that we don’t rely on any one thing to keep us secure, but instead build layers of checks and controls that includes proprietary Google-designed hardware, Google-controlled firmware, Google-curated OS images, a Google-hardened hypervisor, as well as data center physical security and services. We provide assurances in these security layers through roots of trust, such as Titan Chips for Google host machines and Shielded Virtual Machines. Controlling the hardware and security stack allows us to maintain the underpinnings of our security posture in a way that many other providers cannot. We believe that this level of control results in reduced exposure to supply chain risk for us and our customers. More on our measures to mitigate hardware supply chain risk can be found in this blog post.  Binary Authorization: As we describe in our Binary Authorization whitepaper, we verify, for example, that software is built and signed in an approved isolated build environment from properly checked-in code that has been reviewed and tested. These controls are enforced during deployment by policy, depending on the sensitivity of the code. Binaries are only permitted to run if they pass such control checks, and we continuously verify policy compliance for the lifetime of the job. This is a critical control used to limit the ability of a potentially malicious insider, or other threat actor using their account, to insert malicious software into our production environment. Google Cloud customers can use the Binary Authorization service to define and automatically enforce production deployment policy based on the provenance and integrity of their code. Change Verification: Code and configuration changes submitted by our developers are provably reviewed by at least one person other than the author. Sensitive administrative actions typically require additional human approvals. We do this to prevent unexpected changes, whether they’re mistakes or malicious insertions. Reshaping the ecosystemWe also believe the broader ecosystem will need to reshape its approach to layered defense to address supply chain attacks long-term. For example, software development teams should adopt tamper-evident practices paired with transparency techniques that allow for third-party validation and discoverability. We have published an architectural guide to adding tamper checking to a package manager, and this is implemented for Golang. Developers can make use of our open-source verifiable Trillian log, which powers the world’s largest, most used and respected production crypto ledger-based ecosystem, certificate transparency.Another area for consideration is limiting the effects of attacks by using modern computing architectures that isolate potentially compromised software components. Examples of such architectures are Android OS’s application sandbox, gVisor (an application sandbox for containers), and Google’s BeyondProd where microservice containerization can limit the effects of malicious software. Should any of the upstream supply-chain components in these environments become compromised, such isolation mechanisms can act as a final layer of defense to deny attackers their goals.Our industry commitment and responsibility  The software supply chain represents the links across organizations—an individual company can only do so much on their own. We need to work together as an industry to change the way software components are built, distributed and tracked throughout their lifecycle. One example of collaboration is the Open Source Security Foundation, which Google co-founded last year to help the industry tackle issues like software supply chain security in open source dependencies and promote security awareness and best practices. We also work with industry partners to improve supply chain policies and reduce supply chain risk, and publish information for users and customers on how they can use our technology to manage supply chain risk. Pushing the software ecosystem forwardAlthough the history of software supply chain attacks is well-documented, each new attack reveals new challenges. The seriousness of the SolarWinds event is deeply concerning but it also highlights the opportunities for government, industry, and other stakeholders to collaborate on best practices and build effective technology that can fundamentally improve the software ecosystem. We will continue to work with a range of stakeholders to address these issues and help lay the foundation for a more secure future.Related ArticleMitigating risk in the hardware supply chainGoogle hardware, software, and services are built with security as a primary design concern. Learn more about the steps we take to secure…Read Article
Quelle: Google Cloud Platform

The democratization of data and insights: making real-time analytics ubiquitous

In our first blog post in this series, we talked broadly about the democratization of data and insights. Our second blog took a deeper look at insights derived specifically from machine learning, and how Google Cloud has worked to push those capabilities to more users across the data landscape. In our third and final blog in this series, we’ll examine data access, data insights, and machine learning in the context of real-time decision making, and how we’re working to help all users – business and technical – get access to real-time insights.Getting real about real-time data analysisLet’s start by taking a look at real-time data analysis (also referred to as stream analytics) and the blend of factors that increasingly make it critical to business success. First, data is increasingly real-time in nature. IDC predicts that by 2025, more than 25% of all data created will be real-time in nature. We predict the number of business decisions being made at Google Cloud based on real-time data will be even higher than that. What’s driving that growth? There are a number of factors that represent an overall trend towards digitization in not just business, but society in general. These factors include, but aren’t limited to, digital devices, IoT-enabled manufacturing and logistics, digital commerce, digital communications, and digital media consumption. Harnessing the real-time data created by these activities gives companies the opportunity to better analyze their market, competition, and importantly, customers.Next, customers expect more than ever in terms of personalization; they expect to be a “segment of one” across recommendations, offers, experience, and more. Companies know this and compete with each other to deliver the best user and customer experience possible. Google Cloud customers such as AB Tasty are processing billions of real-time events for millions of users each day to deliver just that for their clients—an experience that’s optimized for smaller and smaller segments of users.With our new data pipeline and warehouse, we are able to personalize access to large volumes of data that were not previously there. That means new insights and correlations and, therefore, better decisions and increased revenue for customers. Jean-Yves Simon, VP Product, AB TastyFinally, real-time analysis is most useful when there’s an opportunity to take quick actions based on the insights. The same digitization driving real-time data generation provides an opportunity to drive immediate action in an instant feedback loop. Whether the action involves on-the-spot recommendations for digital retail, rerouting delivery vehicles based on real-time traffic information, changing the difficulty of an online gaming session, digitally recalibrating a manufacturing process, stopping fraud before a transaction is completed, or countless other examples, today’s technology opens up the opportunity to drive a more responsive and efficient business.Democratizing real-time data analysisWe think of democratization in this space in two different frames. One is the standard frame we’ve taken in this blog series of expanding the capabilities of various data practitioners: “how do we give more users the ability to generate real-time insights?” The other frame, specifically for stream analytics, is democratization at the company level. Let’s start with how we’re helping more businesses move to real-time, and then we’ll dive into how we’re helping across different users.Democratizing stream analytics for all businesses Historically, collecting, processing, and acting upon real-time data was particularly challenging. The nature of real-time data is that its volume and velocity can vary wildly in many use cases, creating multiple layers of complexity for data engineers trying to keep the data flowing through their pipelines. The tradeoffs involved in running a real-time data pipeline led many engineers to implement a lambda architecture, in which they would have both a real-time copy of (sometimes partial) results as well as a “correct” copy of results that took a traditional batch route. In addition to presenting challenges in reconciling data at the end of these pipelines, this architecture multiplied the number of systems to manage, and typically increased the number of ecosystems these same engineers had to manage. Setting this up, and keeping it all working, took large teams of expert data engineers. It kept the bar for use cases high.Google and Google Cloud knew there had to be a better way to analyze real-time data… so we built it! Dataflow, together with Pub/Sub, answers the challenges posed by traditional streaming systems by providing a completely serverless experience that handles the variation in event streams with ease. Pub/Sub and Dataflow scale to exactly what resources are needed for the job at hand, handling performance, scaling, availability, security, and more—all automatically. Dataflow ensures that data is reliably and consistently processed exactly once, so engineers can trust the results their systems produce. Dataflow jobs are written using the Apache Beam SDK, which provides programming language choice for Dataflow (in addition to portability). Finally, Dataflow also allows data engineers to easily switch back and forth between both batch streaming modes, meaning users can experiment between real-time results and cost-effective batch processing with no changes to the code.Google unifies streaming analytics and batch processing the way it should be. No compromises. That must be the goal when software architects create a unified streaming and batch solution that must scale elastically, perform complex operations, and have the resiliency of Rocky Balboa. The Forrester Wave™, Streaming Analytics, Q3 2019, by Mike Gualtieri, Forrester Research, Inc.All together, Dataflow and Pub/Sub deliver an integrated, easy-to-operate experience that opens real-time analysis up to companies that don’t have large teams of expert data engineers. We’ve seen small teams of as few as six engineers processing billions of events per day. They can author their pipelines, and leave the rest to us.Democratizing stream analytics for all personasHaving developed a streaming platform that made streaming available to data engineering teams of all sizes and skills, we set about making it easier for more people to access real-time analysis and drive better decisions as a result. Let’s dive into how we’ve expanded access to real-time analytics.Business and data analystsProviding access to real-time data for data analysts and business analysts starts with enabling data to be rapidly ingested into the data warehouse. BigQuery is designed to be “always fast, always fresh,” and it enables streaming inserts into the data warehouse at millions of events per second. This gives data warehouse users the ability to work on the very freshest data, making their analysis more timely and accurate.In addition to the insights that data analysts typically drive out of the data warehouse, analysts can also apply machine learning capabilities delivered by BigQuery ML against real-time data being streamed in. If data analysts know there’s a source of data that they need to access but that isn’t currently in the warehouse, Dataflow SQL enables them to connect new streaming sources of data with a few simple lines of SQL. The real-time capabilities we describe for data analysts have cascading effects for the business analysts who rely on dashboards sourced from the data warehouse. BigQuery’s BI Engine enables sub-second query response and high concurrency for BI use cases, but including real-time data in the data warehouse gives business analysts (and those who rely on them) a fuller picture of what’s happening in the business right now. In addition to BI, Looker’s data-driven workflows and data application capabilities benefit from fast-updating data in BigQuery. ETL DevelopersData Fusion, Google Cloud’s code-free ETL tool, delivers real-time processing capabilities to ETL developers with the simplicity of flipping a switch. Data Fusion users can easily set their pipelines to process data in real-time and land it into any number of storage or database services at Google Cloud. Further, Data Fusion’s ability to call upon a number of predefined connectors, transformations, sinks, and more – including machine learning APIs – and to do so in real-time gives businesses an impressive level of flexibility without the need to write any code at all.Wrapping upEach blog in this series (catch up on Part 1 and Part 2 if you missed them) has shown how Google Cloud can democratize data and insights. It’s not enough to deliver data access, then simply hope for good things to happen within your business. We’ve observed a clear formula for successfully democratizing the generation of ideas and insights throughout your business:Start by ensuring you can deliver broad access to data that’s relevant to your business. That means moving towards systems that have elastic storage and compute with the ability to automatically scale both. This will enable you to bring in new data sources and new data workers without the need for labor-intensive operations, increasing the agility of your business.Ensure that users can generate insights from within the tools they know and are comfortable with. By delivering new capabilities to existing users within their tools, you can help your business put data to work across the organization. Further, this will keep your workforce excited and engaged as they get to explore new areas of analysis like machine learning.Once you’ve given your employees the ability to access data and the ability to drive insights from the data, give them the ability to analyze real-time data and automate the outcomes of that analysis. This will drive better customer experiences, and help your organization take faster advantage of opportunities in the market.We hope you’ve enjoyed this series, and we hope you’ll consider working with us to help democratize data and insights within your business. A great way to get started is by starting a free trial or jumping into the BigQuery sandbox, but don’t hesitate to reach out if you want to have a conversation with us.The Forrester Wave™, Streaming Analytics, Q3 2019
Quelle: Google Cloud Platform

Backhaul: Satellit kann 5G ermöglichen

Das Deutsche Zentrum für Luft- und Raumfahrt hält die neue Satellitentechnik für tauglich, bei 5G als Backhaul zu agieren. Bis zu 100 MBit/s im Download und 6 MBit/s im Upload seien mit modernen, geostationären Satelliten möglich. (DLR, Satelliteninternet)
Quelle: Golem