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AI and IoT Applied to Supply Chains Are Driving Digital Twins 

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GE is working in Paris with Ansys to build a digital twin of a wind turbine in the North Sea, an example of how digital twins are being employed in the supply chain. (Credit: Getty Images) 

By AI Trends Staff 

The combination of IoT and machine learning growing at the same time is leading to a rise in the use of digital twins in the supply chain, as a digital replica that can be used for various purposes. The connection with the physical model and the corresponding virtual model is established by generating real time data using sensors.  

The Digital Twin Consortium, launched in August as a program of the Object Management Group, is working on defining a taxonomy and standards and enabling technology including AI and simulation. Engineers are being attracted to the work. Founding members include Ansys, Dell, GE, Lendlease, Microsoft and Northrop Grumman.   

Scott Lundstrom, analyst focused on the intersection of AI, IoT and Supply Chains

IoT and ML are the raw materials and the toolsthe insight is in the repository where we model processes and create context. While this might be a database or a data lake, the most interesting example of this for me is the digital twin,” wrote Scott Lundstrom, an analyst focused on the intersection of AI, IoT and Supply Chains, on his blog, Supply Chain Futures.  

The digital twin in the supply chain allows a comparison between current and historical data on performance, wherever a sensor is located. It could be a component such as a thermostat, an asset such as a truck or a machine, an employee such as a service technician, or a process, as in manufacturing. “Part of the capability of the digital twin is driven by this complexity of having models of models to describe complex assets, processes, and systems,” Lundstrom wrote.  

In the supply chain, the digital twin model can encompass items packed in containers, moving through the physical world to distributors and customers. The model could inherit data from the process that created the product at one end of the chain, and inform a customer model at the other end.  

Supply chains and manufacturing assets are just the beginning. As this technology becomes better understood, and deployments become easier, use will grow into increasing complex spaces. There is already development of digital twins in life sciences in support of systems biology modeling complex organs like the human heart,” Lundstrom wrote. (See “Virtual Twins: Their Roles in Healthcare, Drug Discovery and Pandemic Response,” in BioITWorld.) 

Ideally for the supply chain, characterized by many complex, multi-model use cases, the inclusive digital twin can have a view of the entire supply chain from the supplier’s supplier to the customer’s customer. An understanding of the status and history of assets and processes allows machine learning tools to be brought into the equation to execute simulations, optimizations, and predictive capabilities to the models, Lundstrom suggests.   

“To realize the benefits of this tremendous opportunity we need standards, agreed upon taxonomies, and commercial development tools and platforms for this market to flourish,” he stated. “The supplier community is reacting to this opportunity, and many practitioners from the PLM [product lifecycle management], IoT, and analytics/data science market are beginning to focus on resolving some of these foundational standards.”   

The large platform suppliers are moving forward with tools and platform as a service (PaaS) offerings to try to win share and develop “de facto” standards. Amazon Web Services (AWS), Google Cloud Platform (GCP), Predix Platform from GE, IBM and Microsoft are all building extensions to their existing IoT tools and platforms to add support for the creation of digital twins.   

Lundstrom pointed to Microsoft’s Azure Digital Twins as one of the more complete early offerings. Featured at the Microsoft Build 2020 event, held virtually in May, the preview release supports a new Digital Twin Definition Language (DTDL) based on an implementation of JSON-LD (JavaScript Object Notation for Linked Data). 

“By leveraging JSON-LD, a well-accepted and simple object framework, Microsoft is supporting an open standard from the beginning,” Lundstrom writes. “This is a key requirement as users begin to understand that digital twins require an open object-oriented approach to support the requirements for inheritance, and multiple instances in creating complex multitier models that are portable and support the use of widely available cloud platforms and AI frameworks.”  

Are Supply Chain Digital Twins Just Another Fad? 

Is the supply chain digital twin just another fad, asked a blog post on the site of River Logic, a supplier of prescriptive analytics technology for supply chain optimization using digital twins. In business since 2000 in Dallas, the company offers pre-built applications with knowledge of business planning and optimization.  

Simulation and modeling software allows organizations to create realistic and verifiable supply chain digital twins of their supply chains. Data mining techniques along with inputs from Internet of Things (IoT) sensors allow real-time data to be fed into models. The models can monitor and determine what’s happening in the real world and plan the appropriate corrective action. 

Gartner study on IoT implementation in July 2018 showed that 13% of companies working with IoT projects already had digital twins, while another 62% were working toward their implementation. “It seems that digital planning twins are more than just a fad,” the River Logic post stated. 

Engineers in the 1970s and ‘80s were using three-dimensional CAD models of complex engineering equipment to conduct virtual walkthroughs. As the CAD technology advanced, it became possible to represent physical stress, making it possible to conduct virtual stress testing. Today it is possible to construct “almost perfect” digital models of real equipment, such as aircraft, autonomous vehicles and drilling equipment, and by inputting real data, such as the static and dynamic loads experienced during aircraft takeoff, to measure performance.  

“In this way, it’s possible to simulate the real world and bridge the gap between our physical and digital environment,” River Logic states. Several company experiences with digital twins are highlighted on the River Logic website. 

Digital Twin of a Warehouse in Pacific-Asia Built by DHL Supply Chain 

DHL Supply Chain built its first digital twin of a warehouse in Pacific-Asia for Tetra Pak, a multinational food packaging and processing company based in Switzerland. The digital twin is supplied with real-time data on a consistent basis from a physical warehouse in Singapore, which DHL developed to be integrated into the supply chain, according to an account in Supply Chain magazine.  

Gillet Jerome, CEO, DHL Supply Chain Singapore, Malaysia, Philippines

“The joint implementation of such a digital solution to improve Tetra Pak’s warehousing and transport activities is an excellent example of the smart warehouses of the future,” stated Gillet Jerome, CEO, DHL Supply Chain Singapore, Malaysia, Philippines. “This enables agile, cost-effective and scalable supply chain operations.”  

At the warehouse, the DHL Control Tower tracks incoming and outgoing goods to ensure all goods are stored in the correct way within 30 minutes of receipt. Incoming trucks are outfitted with IoT technology. A smart storage solution developed by Tetra Pak tracks and simulates the physical condition and individual stock levels in real-time, allowing non-stop coordination of operations. .   

“We expect the partnership with DHL Supply Chain to further increase our productivity and maintain high standards in our supply chains,” commented Devraj Kumar, Director, Integrated Logistics, South Asia, East Asia & Oceania for Tetra Pak.  

Digital Twins in Paris Will Protect Wind Turbine from North Sea Gales  

GE engineers in Paris are partnering with Ansys, a global supplier of engineering simulation software, to build a digital twin of a wind turbine in the North Sea. One goal is to maximize output and minimize downtime by spotting problems before they lead to an unplanned outage. The predictive maintenance relies not only on physical sensors on the machines, but also virtual sensors put in places where physical sensors cannot be used, according to an account from GE News.  

The virtual sensor has the ability to guess with fair precision a value such as temperature of pressure, by using other data from sensors and smart algorithms based on historical data or models.  

For example, the GE engineers have developed a digital twin of the Haliade 150-6 wind turbine’s yaw motors, which enable the 6-megawatt turbine to rotate and position itself into the wind. Using virtual sensors, this digital twin simulates the temperature at various parts of the motors. 

Hervé Sabot, engineering director at GE’s Digital Foundry in Paris

The better you monitor the temperature, the better you know the impact of the way you are using it,” stated Hervé Sabot, engineering director at GE’s Digital Foundry in Paris. “The challenge here is to boost the capacity of our customer’s assets to avoid outages and have them perform as fast as possible.” 

Sabot and his team used the Ansys simulation tools to computer the motor’s internal temperature from a model. They accomplished this by tracking the electrical current feeding into the wind turbine motors.   

Using algorithms built on Predix, the GE software platform for the industrial internet, and a modeling approach developed by Ansys, the engineers can now estimate the motor temperature at any given moment. At the Foundry, they can also monitor how the motors perform under different strains over time. In the field, engineers are able to use an app with a dashboard connected to the twin, to monitor the motor’s temperature.  

“For the simulation, thanks to the digital twin we only need to know the current to understand the temperature and optimize the use of the motor,” Sabot stated.  

GE reports it has 1.2 million digital twins of jet engines, gas turbines and locomotives already working in the field.  

Read the source articles and accounts at  Supply Chain FuturesDigital Twin Consortium, the blog of River LogicSupply Chain magazine and from GE News. 

Source: https://www.aitrends.com/iot/ai-and-iot-applied-to-supply-chains-are-driving-digital-twins/

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Emerging Technologies Achievable Through The Cloud: 4 Practical Examples

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Steve Sangapu

Cloud computing is the foundation beneath some of the fastest growing industries in the world, So it’s not difficult to get lost in all the buzzwords that are thrown around cloud computing and digress from actual technological advances and benefits that are achievable with smart and efficient use of the cloud.

So what’s behind the hype? Some extremely powerful technologies and workflows. And that’s exactly what we’re going to take a look at in this article — the top 4 practical examples of technologies achievable through the cloud in 2020.

Contrary to popular belief, information alone won’t give companies a competitive advantage — executives also need to be able to base their decisions on data before the opportunities pass. However, most companies generate terabytes of data every week but are unable to capitalize on any of the data. Big data analytics is a solution to this problem.

Thanks to the advanced evolution of the cloud, companies are able to gather and analyze data at a nearly instantaneous rate. Leveraging big data analytics empowers organizations to run more efficiently in terms of cost and decision making. Companies can make data-driven decisions brought to them by data analysis tools that are provided through the cloud.

BigQuery from Google Cloud has many powerful features that allow users to view their data in real-time, providing continual up-to-date information to help guide business decisions. Big Query is a serverless NoOps (no operations) platform that separates compute and storage, meaning that better autoscaling is offered as they can be independently scaled as required. BigQuery’s Machine Learning and Business Intelligence Engine analysis of various data models are quite powerful. It integrates seamlessly with the Google Cloud AI platform and other tools like Data Studio.

Cloud service providers like Google Cloud Platform (GCP) use shared computing to process large datasets extremely quickly. Also known as cluster computing, Google uses hundreds of computers interconnected together for quick data analysis and completing complex computing tasks. Businesses like yours can also make use of similar services like cloud service providers to improve insights and decision-making.

(Sources: SAS, Google Cloud, Hostingtribunal)

1. How VR could bring transhumanism to the masses

2. How Augmented Reality (AR) is Reshaping the Food Service Industry

3. ExpiCulture — Developing an Original World-Traveling VR Experience

4. Enterprise AR: 7 real-world use cases for 2021

Automating mundane and repetitive tasks is and should be the top priority for businesses in this age. Even automating the simplest tasks, most business environments can free up to 30% time for employees — allowing them to focus on more important matters.

Cloud service providers have made it extremely easy for businesses of all sizes to dabble with business process automation. For instance, at the most fundamental level, businesses can automate how they receive and sort documents through document management, to automating entire workflows including delivery pipelines and testing updates in a controlled cloud environment. Tools such as Google’s Document Understanding AI can actually help you ensure your data is accurate and compliant. This is especially helpful in highly regulated industries where accuracy and precision are crucial to operations. It is also quick and easy to request more compute if needed for deep learning and complex ML training by requesting GPUs or using a managed service like Kubeflow.

Another emerging technology that is now accessible to small to medium enterprises is machine learning. Put simply, machine learning refers to training computer algorithms to interpret and interact with data without human interference. With increasing accuracy, MI (a subset of AI) is becoming incredibly valuable to businesses as it has virtually unlimited use cases.

You can read more about how cloud solutions using AI and ML can help save time, cut costs, and improve rates of human error.

(Sources: Google Cloud, Interactions)

Although lesser-known among legacy businesses, the Internet of Things is one of the fastest-growing industries in the world and was valued at $190 billion in 2018. Alexa and Google Home are two of the most popular examples of IoT devices of which you’re most likely very familiar. Apart from that, smart TVs, smart refrigerators, smart LEDs, security systems, thermostats, and even cars (think Tesla) that operate over WiFi are all a part of the internet of things.

Think of IoT devices as part of a much larger network all of which have a backbone in the cloud. Aside from pure convenience, IoT can be seen making significant breakthroughs in other spaces such as health tech. Fitbit, for example, has partnered with Google to transform how their product integrates between fitness and the cloud. The device uses Google’s Cloud Healthcare API. The API is a service that “helps facilitate the exchange of data among healthcare applications and services that run on Google’s Cloud.” Even more interesting is that the API also integrates analytics tools like BigQuery, AI tools like AI Platform, and data processing tools like Dataflow.

Similar tools and APIs are available for businesses in different industries so they too can help connect their device to an online network and introduce security patches, fix bugs, add features, and more.

(Sources: internetofbusiness, Google Healthcare API)

Though it has become significantly more popular in the last few years, augmented and virtual reality are not new technologies. Leftronic reports that the number of augmented reality users will reach 3.5 billion by 2023. Furthermore, they estimate that the AR and VR device market will hit $198 billion by 2025. In fact, large institutions like Boeing and NASA have been developing their own AR and VR technologies for training purposes for quite some time now. However, thanks to cloud proliferation, technologies like virtual reality are finally becoming accessible and more importantly, affordable for the average business to experiment with.

So how does it work?

When applications superimpose a CG image into the real world, they create an augmented reality experienced. Augmented reality places computer-generated objects in the human world, whereas virtual reality places you into a computer-generated world. Businesses can use this technology in a number of ways including giving consumers a virtual reality tour of their product or use it for training in a safe environment.

It’s also quite easy to get started with. Google’s Cloud Anchor allows developers to create experiences within their app for users to add virtual objects into an augmented reality environment. Thanks to Google’s ARCore Cloud Anchor service, experiences are allowed to be hosted and shared between users. Virtual Reality allows you to be transported to distant places and immerse yourself in foreign environments. Devices such as the Oculus Rift or Quest and the HTC Vive provide outstanding experiences that can run independently of a computer. When used at its capacity, Virtual Reality can be transformative for gaming, education, and immersive experiences.

These emerging technologies unlock a completely new frontier that businesses can compete in without exorbitant investments or technical knowledge. With all the right tools already available at their disposal, most businesses only need a helping hand to get started. If your organization is considering using the cloud to leverage an emerging technology but are unsure about the intricacies, reach out to D3V and set up a free strategic consultation with our certified cloud experts. Our team can help determine the best set of options for your company based on your business needs and aspirations.

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Source: https://arvrjourney.com/emerging-technologies-achievable-through-the-cloud-4-practical-examples-3e2b72d5e349?source=rss—-d01820283d6d—4

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Optimal Dynamics nabs $22M for AI-powered freight logistics

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Optimal Dynamics, a New York-based startup applying AI to shipping logistics, today announced that it closed a $18.4 million round led by Bessemer Venture Partners. Optimal Dynamics says that the funds will be used to more than triple its 25-person team and support engineering efforts, as well as bolster sales and marketing departments.

Last-mile delivery logistics tends to be the most expensive and time-consuming part of the shipping process. According to one estimate, last-mile accounts for 53% of total shipping costs and 41% of total supply chain costs. With the rise of ecommerce in the U.S., retail providers are increasingly focusing on fulfilment and distribution at the lowest cost. Particularly in the construction industry, the pandemic continues to disrupt wholesalers — a 2020 Statista survey found that 73% of buyers and users of freight transportation and logistics services experienced an impact on their operations.

Founded in 2016, Optimal Dynamics offers a platform that taps AI to generate shipment plans likely to be profitable — and on time. The fruit of nearly 40 years of R&D at Princeton, the company’s product generates simulations for freight transportation, enabling logistics companies to answer questions about what equipment they should buy, how many drivers they need, daily dispatching, load acceptance, and more.

Simulating logistics

Roughly 80% of all cargo in the U.S. is transported by the 7.1 million people who drive flatbed trailers, dry vans, and other heavy lifters for the country’s 1.3 million trucking companies. The trucking industry generates $726 billion in revenue annually and is forecast to grow 75% by 2026. Even before the pandemic, last-mile delivery was fast becoming the most profitable part of the supply chain, with research firm Capgemini pegging its share of the pie at 41%.

Optimal Dynamics’ platform can perform strategic, tactical, and real-time freight planning, forecasting shipment events as far as two weeks in advance. CEO Daniel Powell — who cofounded the company with his father, Warren Princeton, a professor of operations research and financial engineering — says that the underlying technology was deployed, tested, and iterated with trucking companies, railroads, and energy companies, along with projects in health, ecommerce, finance, and materials science.

“Use of something called ‘high-dimensional AI’ allows us to take in exponentially greater detail while planning under uncertainty. We also leverage clever methods that allow us to deploy robust AI systems even when we have very little training data, a common issue in the logistics industry,” Powell told VentureBeat via email. “The results are … a dramatic increase in companies’ abilities to plan into the future.”

The global logistics market was worth $10.32 billion in 2017 and is estimated to grow to $12.68 billion USD by 2023, according to Research and Markets. Optimal Dynamics competes with Uber, which offers a logistics service called Uber Freight. San Francisco-based startup KeepTruckin recently secured $149 million to further develop its shipment marketplace. Next Trucking closed a $97 million investment. And Convoy raised $400 million at a $2.75 billion valuation to make freight trucking more efficient.

But 25-employee Optimal Dynamics investor Mike Droesch, a partner at BVP, says that demand remains strong for the company’s products. “Logistics operators need to consider a staggering number of variables, making this an ideal application for a software-as-a-service product that can help operators make more informed decisions by leveraging Optimal Dynamics industry leading technology. We were really impressed with the combination of their deep technology and the commercial impact that Optimal Dynamics is already delivering to their customers,” he said in a statement.

With the latest funding round, a series A, Optimal Dynamics has raised over $22 million to date. Beyond Bessemer, Fusion Fund, The Westly Group, TenOneTen Ventures, Embark Ventures, FitzGate Ventures, and John Larkin and John Hess also contributed .

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Source: https://venturebeat.com/2021/05/13/optimal-dynamics-nabs-22m-for-ai-powered-freight-logistics/

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Code-scanning platform BluBracket nabs $12M for enterprise security

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Code security startup BluBracket today announced it has raised $12 million in a series A round led by Evolution Equity Partners. The capital will be used to further develop BluBracket’s products and grow its sales team.

Detecting exploits in source code can be a pain point for enterprises, especially with the onset of containerization, infrastructure as code, and microservices. According to a recent Flexera report, the number of vulnerabilities remotely exploitable in apps reached more than 13,300 from 249 vendors in 2020. In 2019, Barracuda Networks found that 13% of security pros hadn’t patched their web apps over the past 12 months. And in a 2020 survey from Edgescan, organizations said it took them an average of just over 50 days to address critical vulnerabilities in internet-facing apps.

BluBracket, which was founded in 2019 and is headquartered in Palo Alto, California, scans codebases for secrets and blocks future commits from introducing new risks. The platform can monitor real-time risk scores across codebases, git configurations, infrastructure as code, code copies, and code access and resolve issues, detecting passwords and over 50 different types of tokens, keys, and IDs.

Code-scanning automation

Coralogix estimates that developers create 70 bugs per 1,000 lines of code and that fixing a bug takes 30 times longer than writing a line of code. In the U.S., companies spend $113 billion annually on identifying and fixing product defects.

BluBracket attempts to prevent this by proactively monitoring public repositories with the highest risk factors, generating reports for dev teams. It prioritizes commits based on their risk scores, minimizing duplicates using a tracking hash for every secret. A rules engine reduces false positives and scans for regular expressions, as well as sensitive words. And BluBracket sanitizes commit history both locally and remotely, supporting the exporting of reports via download or email.

BluBracket offers a free product in its Community Edition. Both it and the company’s paid products, Teams and Enterprise, work with GitHub, BitBucket, and Gitlab and offer CI/CD integration with Jenkins, GitHub Actions, and Azure Pipelines.

BluBracket

Above: The Community Edition of BluBracket’s software.

Image Credit: BluBracket

“Since our introduction early last year, the industry has seen through Solar Winds how big of an attack surface code is. Hackers are exploiting credentials and secrets in code, and valuable code is available in the public domain for virtually every company we engage with,” CEO Prakash Linga, who cofounded BluBracket with Ajay Arora, told VentureBeat via email.

BluBracket competes on some fronts with Sourcegraph, a “universal code search” platform that enables developer teams to manage and glean insights from their codebase. It has another rival in Amazon’s CodeGuru, an AI-powered developer tool that provides recommendations for improving code quality. There’s also cloud monitoring platform Datadog, codebase coverage tester Codecov, and feature-piloting solution LaunchDarkly, to name a few.

But BluBracket, which has about 30 employees, says demand for its code security solutions has increased “dramatically” since 2020. Its security products are being used in “dozens” of companies with “thousands” of users, according to Linga.

“DevSecOps and AppSec teams are scrambling, as we all know, to address this growing threat. By enabling their developers to keep these secrets out of code in the first place, our solutions make everyone’s life easier,” Linga continued. “We are excited to work with Evolution on this next stage of our company’s growth.”

Unusual Ventures, Point72 Ventures, SignalFire, and Firebolt Ventures also participated in BluBracket’s latest funding round. The startup had previously raised $6.5 million in a seed round led by Unusual Ventures.

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Source: https://venturebeat.com/2021/05/13/code-scanning-platform-blubracket-nabs-12m-for-enterprise-security/

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Data governance and security startup Cyral raises $26M

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Data security and governance startup Cyral today announced it has raised $26 million, bringing its total to date to $41.1 million. The company plans to put the funds toward expanding its platform and global workforce.

Managing and securing data remains a challenge for enterprises. Just 29% of IT executives give their employees an “A” grade for following procedures to keep files and documents secure, according to Egnyte’s most recent survey. A separate report from KPMG found only 35% of C-suite leaders highly trust their organization’s use of data and analytics, with 92% saying they were concerned about the reputational risk of machine-assisted decisions.

Redwood City, California-based Cyral, which was founded in 2018 by Manav Mital and Srini Vadlamani, uses stateless interception technology to deliver enterprise data governance across platforms, including Amazon S3, Snowflake, Kafka, MongoDB, and Oracle. Cyral monitors activity across popular databases, pipelines, and data warehouses — whether on-premises, hosted, or software-as-service-based. And it traces data flows and requests, sending output logs, traces, and metrics to third-party infrastructure and management dashboards.

Cyral can prevent unauthorized access from users, apps, and tools and provide dynamic attribute-based access control, as well as ephemeral access with “just-enough” privileges. The platform supports both alerting and blocking of disallowed accesses and continuously monitors privileges across clouds, tracking and enforcing just-in-time and just-enough privileges for all users and apps.

Identifying roles and anomalies

Beyond this, Cyral can identify users behind shared roles and service accounts to tag all activity with the actual user identity, enabling policies to be specified against them. And it can perform baselining and anomaly detection, analyzing aggregated activity across data endpoints and generating policies for normal activity, which can be set to alert or block anomalous access.

“Cyral is built on a high-performance stateless interception technology that monitors all data endpoint activity in real time and enables unified visibility, identity federation, and granular access controls. [The platform] automates workflows and enables collaboration between DevOps and Security teams to automate assurance and prevent data leakage,” the spokesperson said.

Cyral

Existing investors, including Redpoint, Costanoa Ventures, A.Capital, and strategic investor Silicon Valley CISO Investments, participated in Cyral’s latest funding round. Since launching in Q2 2020, Cyral — which has 40 employees and occupies a market estimated to be worth $5.7 billion by 2025, according to Markets and Markets — says it has nearly doubled the size of its team and close to quadrupled its valuation.

“This is an emerging market with no entrenched solutions … We’re now working with customers across a variety of industries — finance, health care, insurance, supply chain, technology, and more. They include some of the world’s largest organizations with complex environments and some of the fastest-growing tech companies,” the spokesperson said. “With Cyral, our company was built during the pandemic. We have grown the majority of our company during this time, and it has allowed us to start our company with a remote-first business model.”

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Source: https://venturebeat.com/2021/05/13/data-governance-and-security-startup-cyral-raises-26m/

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