Automation tools help developers streamline their processes to save time, boost productivity and concentrate on their most critical tasks. This guide will break down the following top automation tools in terms of their features, pros, cons and pricing:

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Gradle is a fast and flexible open-source build automation tool that accelerates development while improving software quality.

Gradles list of features is highlighted by:

Gradle helps developers build multiple projects at the same time, making it ideal for large projects with multiple subprojects. Its rich API lets developers customize builds to fit their needs, and the automation tool also manages dependencies so your projects always have the most updated versions they need.

You can use Gradle to run tests to ensure your project is working as intended, plus deploy projects to different targets, such as cloud platforms or servers. Gradle also offers seamless integrations with popular IDEs like Android Studio and IntelliJ IDEA.

Gradles pros include:

Gradle is quite speedy compared to other build automation tools. Its Free plan is ideal for budget-minded individual developers, and the software is quite flexible in terms of supported programming languages and platforms. Gradle is also highly customizable, allowing developers to use it for diverse projects.

Gradles cons include:

Getting to understand Gradle can be a daunting task with its extensive documentation. Building tasks with the tool takes some technical know-how. All of the above make it not too beginner-friendly.

Gradle Enterprise has a free trial and is offered in two pricing tiers:

The Free plan offers unlimited build scans, a visual build timeline, performance data, a dependency graph, custom values, tagging, environment data, test behavior details, an enhanced console log and a build performance summary.

The Core plan offers distributed cache node management, access control, cross-build analysis, build failure aggregation, comprehensive failure metrics, Enterprise REST API and more.

The developer tool also offers separate extensions to meet additional productivity needs, such as test distribution, test failure analytics and predictive test selection.

Apache Maven is an open-source build automation tool. Launched over 20 years ago with Java developers in mind, it is now a popular DevOps tool used by Agile development teams, project managers, etc., for its ability to help them build, publish and deploy several projects simultaneously, dependency and release management features and more.

Some of the features that make Maven a popular DevOps automation tool include:

Maven boosts productivity by letting developers get started with new projects and modules quickly and allowing them to work on multiple projects simultaneously. The automation software is easily extensible via plugins written in Java or scripting languages and an extensive repository of libraries and metadata.

When new features are available, you will not have to worry about time-consuming configuration with Maven. And it also saves time via model-based builds and quickly-generated documentation built off project metadata. Lastly, Maven offers dependency management (transitive dependencies, Ant tasks, automatic updating, etc.) and release management.

Mavens advantages include:

Since Maven is open-source, developers can use it at no cost, which is excellent news if you are on a limited budget. The developer tool is easy to use and configure, consistent since it follows a standardized approach that can easily replicate future processes, and offers fuss-free documentation generation.

Mavens disadvantages include:

While Maven is user-friendly, it may not be beginner-friendly for those unfamiliar with configurations, terminology, etc. Some have complained that the programming tool is lacking in documentation and support. Others, meanwhile, have noted slow performance when dealing with complex projects.

Since Maven is an open-source DevOps tool, developers can enjoy it for free. There may be additional costs for premium extensions or plugins, however.

Travis CI is an easy-to-maintain cloud-based CI/CD tool with time-saving one-command automations that supports over 30 coding languages.

Travis CIs top features that have allowed its popularity to grow in the DevOps community include:

Travis CIs multi-language build matrix supports more than 30 coding languages. Developers can run and test simultaneously in different environments, plus automate tasks for validation, integration and deployment with a single command. The DevOps tool integrates with popular third-party developer tools like Slack, Perforce, Docker, etc., and has a feature that catches code failures and bugs on autopilot.

Travis CIs strengths include:

Developers seeking a fuss-free automation tool get just that with Travis CI. Thanks to its cloud-based options, developers can enjoy Travis CIs time-saving features with minimal setup and maintenance.

Travis CI also employs a lot less code (around one-third less) than competing programmer tools and is quite flexible with its support for over 30 coding languages.

Areas where Travis CI could improve include:

Budget-minded software development teams may get turned off by the lack of a Travis CI free plan. The developer tools cost can creep up quickly as your need for added concurrent jobs grows, and the customer support has received complaints of being slow to respond to issues.

Travis CIs pricing is split into cloud and enterprise options. Cloud pricing is as follows:

Each cloud plan comes with unlimited repositories, collaborators and build minutes, plus a free trial. Choose the self-hosted Enterprise plan, and you will pay $34 per user per month and have the option to host Travis CI on-premise or in your private cloud. The Enterprise plan offers premium support and Subversion and Perforce CI/CD.

Katalon Studio is a codeless test automation tool that offers a low-code experience for beginners and advanced testing for experts.

Some of Katalon Studios best features for testing automation are:

Katalon Studio lets developers automate tests with varying sets of data. This is ideal for applications that handle confidential or sensitive data. The automation software also supports keyword-driven testing, allowing developers to create reusable test scripts for multiple applications.

API testing comes in handy for testing back-end applications and services, and if there are any broken tests, Katalon will fix them automatically. Katalon Studio records and plays back user actions on applications to help create automated tests, and it offers detailed reporting to help troubleshoot.

Katalon Studios pros include:

The programmer tools Free version is a plus for those with limited budgets seeking basic test automation capabilities. Katalon Studios low-code approach makes it beginner-friendly, and the interface is easy to navigate and user-friendly. The fact that the automation software works with multiple environments (Windows, macOS, Google Chrome, Firefox, Android, iOS, etc.) is another plus.

Katalon Studios cons include:

Some have complained that Katalon Studios performance is less than stellar, and the automation tool can sometimes lag or freeze. Support is noted for being slow to respond. Since Katalon is relatively new and has a smaller community, you are less likely to find fast help from colleagues. The desktop app can also be a memory hog when booting or running tests.

Katalon Studio has three pricing plans:

The Free plan offers test automation for mobile, web, API and desktop applications. Enterprise adds debugging, custom reports and advanced API testing. And Ultimate adds 24/7 support and a dedicated onboarding manager.

As the need for automation grows, so does the number of automation tools that hit the market.

How can you pick the right one? Besides looking at the price (some offer free plans) to find an automation solution that fits your budget, read reviews regarding user-friendliness. Depending on your team size and goals, you may want something scalable that can grow as you expand.

Customer support and community size are other factors to consider, as are features. Useful features to look for in automation software include configuration management, reporting, workflow management, CI/CD, monitoring, orchestration, version control support and solid security. You should also look for plenty of third-party integrations with popular developer tools for added functionality, if needed, and support for the programming languages you use.

The automation tools listed above can help developers enjoy increased speed and productivity without sacrificing the quality of their releases. Before choosing an automation tool for your software development team, make sure it fits your needs in terms of features, user-friendliness and pricing.

Also See: Top DevOps Monitoring Tools

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4 Best Automation Tools for Developers in 2023 - TechRepublic

The Internet of Things (IoT), a network of interconnected devices equipped with sensors and software, has revolutionised how we interact with the world around us, empowering us to collect and analyse data like never before.

As technology advances and becomes more accessible, more objects are equipped with connectivity and sensor capabilities, making them part of the IoT ecosystem. The number of active IoT systems is expected to reach 29.7 billion by 2027, marking a significant surge from the 3.6 billion devices recorded in 2015. This exponential growth requires a tremendous demand for solutions to mitigate the safety and computational challenges of IoT applications. In particular, industrial IoT, automotive, and smart homes are three main areas with specific requirements, but they share a common need for efficient IoT systems to enable optimal functionality and performance.

Fig. 1: Overview of VEDLIoT technology layers and components

Increasing the efficiency of IoT systems and unlocking their potential can be achieved through Artificial Intelligence (AI), creating AIoT architectures. By utilising sophisticated algorithms and Machine Learning techniques, AI empowers IoT systems to make intelligent decisions, process vast amounts of data, and extract valuable insights. For instance, this integration drives operational optimisation in industrial IoT, facilitates advanced autonomous vehicles, and offers intelligent energy management and personalised experiences in smart homes.

Among the different AI algorithms, Deep Learning that leverages artificial neural networks is very appropriate for IoT systems for several reasons. One of the primary reasons is its ability to learn and extract features automatically from raw sensor data. This is particularly valuable in IoT applications where the data can be unstructured, noisy, or have complex relationships. Additionally, Deep Learning enables IoT applications to handle real-time and streaming data efficiently. This ability allows for continuous analysis and decision-making, which is crucial in time-sensitive applications such as real-time monitoring, predictive maintenance, or autonomous control systems.

Despite the numerous advantages of Deep Learning for IoT systems, its implementation has inherent challenges, such as efficiency and safety, that must be addressed to fully leverage its potential. The Very Efficient Deep Learning in IoT (VEDLIoT) project aims to solve these challenges.

A high-level overview of the different VEDLIoT components is given in Fig. 1. IoT is integrated with Deep Learning by the VEDLIoT project to accelerate applications and optimise the energy efficiency of IoT. VEDLIoT achieves these objectives through the utilisation of several key components:

VEDLIoT concentrates on some use cases, such as demand-oriented interaction methods in smart homes (see Fig. 2), industrial IoT applications like Motor Condition Classification and Arc Detection, and the Pedestrian Automatic Emergency Braking (PAEB) system in the automotive sector (see Fig. 3). VEDLIoT systematically optimises such use cases through a bottom-up approach by employing requirement engineering and verification techniques, as shown in Fig. 1. The project combines expert-level knowledge from diverse domains to create a robust middleware that facilitates development through testing, benchmarking, and deployment frameworks, ultimately ensuring the optimisation and effectiveness of Deep Learning algorithms within IoT systems. In the following sections, we briefly present each component of the VEDLIoT project.

Fig. 2: Smart mirror demonstrator developed as part of the smart home application in VEDLIoT

Various accelerators are available for a wide range of applications, from small embedded systems with power budgets in the milliwatt range to high-power cloud platforms. These accelerators are categorised into three main groups based on their peak performance values, as shown in Fig. 4.

The first group is the ultra-low power category (< 3 W), which consists of energy-efficient microcontroller-style cores combined with compact accelerators for specific Deep Learning functions. These accelerators are designed for IoT applications and offer simple interfaces for easy integration. Some accelerators in this category provide camera or audio interfaces, enabling efficient vision or sound processing tasks. They may offer a generic USB interface, allowing them to function as accelerator devices attached to a host processor. These ultra-low power accelerators are ideal for IoT applications where energy efficiency and compactness are key considerations, providing optimised performance for Deep Learning tasks without excessive power.

The VEDLIoT use case of predictive maintenance is a good example and makes use of an ultra-low power accelerator. One of the most important design criteria is low power consumption, as it is a battery-powered small box that can externally be installed on any electric motor and should monitor the electronic motor for at least three years without a battery change.

Fig. 4: Performance overview of AI accelerators

The next category is the low-power group (3 W to 35 W), which targets a broad range of automation and automotive applications. These accelerators feature high-speed interfaces for external memories and peripherals and efficient communication with other processing devices or host systems such as PCIe. They support modular and microserver-based approaches and provide compatibility with various platforms. Additionally, many accelerators in this category incorporate powerful application processors capable of running full Linux operating systems, allowing for flexible software development and integration. Some devices in this category include dedicated application-specific integrated circuits (ASICs), while others feature NVIDIAs embedded graphics processing units (GPUs). These accelerators balance power efficiency and processing capabilities, making them well-suited for various compute-intensive tasks in the automation and automotive domains.

The high-performance category (> 35 W) of accelerators is designed for demanding inference and training scenarios in edge and cloud servers. These accelerators offer exceptional processing power, making them suitable for computationally-intensive tasks. They are commonly deployed as PCIe extension cards and provide high-speed interfaces for efficient data transfer. The devices in this category have high thermal design powers (TDPs), indicating their ability to handle significant workloads. These accelerators include dedicated ASICs, known for their specialised performance in Deep Learning tasks. They deliver accelerated processing capabilities, enabling faster inference and training times. Some consumer-class GPUs may also be included in benchmarking comparisons to provide a broader perspective.

Selecting the proper accelerator from the abovementioned wide range of available options is not straightforward. However, VEDLIoT takes on this crucial responsibility by conducting thorough assessments and evaluations of various architectures, including GPUs, field-programmable gate arrays (FPGAs), and ASICs. The project carefully examines these accelerators performances and energy consumptions to ensure their suitability for specific use cases. By leveraging its expertise and comprehensive evaluation process, VEDLIoT guides the selection of Deep Learning accelerators within the project and in the broader landscape of IoT and Deep Learning applications.

Trained Deep Learning models have redundancy that can sometimes be compressed to 49 times their original size, with negligible accuracy loss. Although many works are related to such compression, most results show theoretical speed-ups that only sometimes translate into more efficient hardware execution since they do not consider the target hardware. On the other hand, the process of deploying Deep Learning models on edge devices involves several steps, such as training, optimisation, compilation, and runtime. Although various frameworks are available for these steps, their interoperability can vary, resulting in different outcomes and performance levels. VEDLIoT addresses these challenges through hardware-aware model optimisation using ONNX, an open format for representing Machine Learning models, ensuring compatibility with the current open ecosystem. Additionally, Renode, an open-source simulation framework, serves as a functional simulator for complex heterogeneous systems, allowing for the simulation of complete System-on-Chips (SoCs) and the execution of the same software used on hardware.

Furthermore, VEDLIoT uses the EmbeDL toolkit to optimise Deep Learning models. The EmbeDL toolkit offers comprehensive tools and techniques to optimise Deep Learning models for efficient deployment on resource-constrained devices. By considering hardware-specific constraints and characteristics, the toolkit enables developers to compress, quantise, prune, and optimise models while minimising resource utilisation and maintaining high inference accuracy. EmbeDL focuses on hardware-aware optimisation and ensures that Deep Learning models can be effectively deployed on edge devices and IoT devices, unlocking the potential for intelligent applications in various domains. With EmbeDL, developers can achieve superior performance, faster inference, and improved energy efficiency, making it an essential resource for those seeking to maximise the potential of Deep Learning in real-world applications.

Since VEDLIoT aims to combine Deep Learning with IoT systems, ensuring security and safety becomes crucial. In order to emphasise these aspects in its core, the project leverages trusted execution environments (TEEs), such as Intel SGX and ARM TrustZone, along with open-source runtimes like WebAssembly. TEEs provide secure environments that isolate critical software components and protect against unauthorised access and tampering. By using WebAssembly, VEDLIoT offers a common environment for execution throughout the entire continuum, from IoT, through the edge and into the cloud.

In the context of TEEs, VEDLIoT introduces Twine and WaTZ as trusted runtimes for Intels SGX and ARMs TrustZone, respectively. These runtimes simplify software creation within secure environments by leveraging WebAssembly and its modular interface. This integration bridges the gap between trusted execution environments and AIoT, helping to seamlessly integrate Deep Learning frameworks. Within TEEs using WebAssembly, VEDLIoT achieves hardware-independent robust protection against malicious interference, preserving the confidentiality of both data and Deep Learning models. This integration highlights VEDLIoTs commitment to securing critical software components, enabling secure development, and facilitating privacy-enhanced AIoT applications in cloud-edge environments.

Fig. 5: Requirements framework showing the various architectural views

Additionally, VEDLIoT employs a specialised architectural framework, as shown in Fig. 5, that helps to define, synchronise and co-ordinate requirements and specifications of AI components and traditional IoT system elements. This framework consists of various architectural views that address the systems specific design concerns and quality aspects, including security and ethical considerations. By using these architecture views as templates and filling them out, correspondences and dependencies can be identified between the quality-defining architecture views and other design decisions, such as AI model construction, data selection, and communication architecture. This holistic approach ensures that security and ethical aspects are seamlessly integrated into the overall system design, reinforcing VEDLIoTs commitment to robustness and addressing emerging challenges in AI-enabled IoT systems.

Traditional hardware platforms support only homogeneous IoT systems. However, RECS, an AI-enabled microserver hardware platform, allows for the seamless integration of diverse technologies. Thus, it enables fine-tuning of the platform towards specific applications, providing a comprehensive cloud-to-edge platform. All RECS variants share the same design paradigm to be a densely-coupled, highly-integrated communication infrastructure. For the varying RECS variants, different microserver sizes are used, from credit card size to tablet size. This allows customers to choose the best variant for each use case and scenario. Fig. 6 gives an overview of the RECS variants.

The three different RECS platforms are suitable for cloud/data centre (RECS|Box), edge (t.RECS) and IoT usage (u.RECS). All RECS servers use industry-standard microservers, which are exchangeable and allow for use of the latest technology just by changing a microserver. Hardware providers of these microservers offer a wide spectrum of different computing architectures like Intel, AMD and ARM CPUs, FPGAs and combinations of a CPU with an embedded GPU or AI accelerator.

Fig. 6: Overview of heterogeneous hardware platforms

VEDLIoT addresses the challenge of bringing Deep Learning to IoT devices with limited computing performance and low-power budgets. The VEDLIoT AIoT hardware platform provides optimised hardware components and additional accelerators for IoT applications covering the entire spectrum, from embedded via edge to the cloud. On the other hand, a powerful middleware is employed to ease the programming, testing, and deployment of neural networks in heterogeneous hardware. New methodologies for requirement engineering, coupled with safety and security concepts, are incorporated throughout the complete framework. The concepts are tested and driven by challenging use cases in key industry sectors like automotive, automation, and smart homes.

The VEDLIoT project has received funding from the European Unions Horizon 2020 research and innovation programme under grant agreement No 957197.

Please note, this article will also appear in the fifteenthedition of ourquarterly publication.

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Unlocking the potential of IoT systems: The role of Deep Learning ... - Innovation News Network

By Sandeep Chellingi, Cloud & Infrastructure leader, Orion Innovation

The world has undergone a profound revolution thanks to the emergence of Cloud technologies. Concepts like Platform as a Service (PaaS) and Infrastructure as a Service (IaaS) have taken root, reshaping how the software industry conceives of resilience, deployments, security, and infrastructure.

In the present landscape, the boundaries between the physical and digital realms have been further obscured by technologies like the Internet of Things (IoT) and Edge computing. Machines are no longer mere CPUs following user manuals; theyre now regarded as vital sources of data offering insights into usage patterns and strategies for enhanced efficiency. Businesses that effectively harness these technologies stand at the precipice of transforming their digital infrastructure. Leveraging IoT empowers businesses to amass an array of data from their physical assets. This data can then be channeled into PaaS services like Microsoft Azure Stream Analytics, extracting meaningful patterns and correlations.

These insights, in turn, can trigger subsequent workflows and generate dashboards that steer business decisions toward efficiency, optimisation, and innovation. Coupling IoT with Edge computing confers a distinct advantage, enabling businesses to analyze data from IoT devices with minimal latency and bolstered on-premise security for sensitive data. This approach also promotes compatibility with legacy devices. Edge computing seamlessly complements a companys data center by ensuring critical workloads are processed closer to data sources and consumers, leading to cost reduction and faster processing times. Additionally, it empowers businesses to make real-time decisions and focuses on low-latency processing use cases.

However, its vital to acknowledge that while Edge computing accelerates data processing, it doesnt replicate cloud computings scalability, AI-driven operations, analytics capabilities, or infrastructure cost reduction. It is imperative to integrate on-premise and cloud infrastructures, amplifying processing potential and distributing it across multiple data centers and hyperscale environments to effectively manage and analyze voluminous data. Opening new avenues for data processing inevitably broadens the attack surface, underscoring the need to prioritise robust security measures for both on-premise and cloud infrastructure.

Businesses can secure their cloud environment by leveraging native cloud-based security services like Microsoft Defender for Cloud, offering security posture detection, threat management, and standardised practices to safeguard cloud servers and databases. As for physical on-premise assets in Edge environments, adhering to the same security policies as those applied within on-premise networks is advisedpractices such as least privilege access, regular patch updates, and restricted network access.

Effective data management is another critical facet, especially considering the deluge of data pouring in from physical assets. Without efficient practices and policies in place, capitalising on the full potential of cloud, IoT, and Edge integration becomes challenging. Data must not be processed for its own sake; it should add value to the business. A well-structured data architecture is pivotal; it must acknowledge that stored data must be actionable to derive meaningful insights. The data lakehouse architecture, for instance, facilitates secure data engineering, machine learning, data warehousing, and business intelligence directly on vast data volumes housed in data lakes. This architecture supports unified catalogs, access controls, discovery mechanisms, auditing, and quality management.

To truly enhance digital infrastructure in response to the ever-evolving landscape, businesses must embrace IoT and Edge computing advancements. Over the last decade, these advancements and the integration of cloud technology have formed a comprehensive solution for intelligent data mining, heightened operational efficiency, and refined customer experiences through insight into user interactions and behaviors.

The key lies in deploying an infrastructure that spans multi-cloud and on-premises environments, selecting the appropriate technologies, and distinguishing between datasets suitable for Edge and those more suited for the cloud. All of this hinges on enforcing data management and security policies within computational boundaries to extract secure and pertinent insights for business growth.

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Improving Digital Infrastructure through IoT Connectivity: Digitalising the Physical World with Local and Cloud Infrastructure Integration - Express...

GovInsider speaks with data protection experts on how the implementation of digital sovereignty practices can unite instead of fragment and what that could look like in practice.

The physical world we live in today is demarcated by national borders drawn over the course of time. The digital world today seems to be following in its footsteps.

Increasingly, countries around the world have been adopting numerous regulations that restrict data flow. The Information Technology & Innovation Foundation found that data localisation measures, which stipulate how data should be processed, stored, and perhaps confined within a geographic location, is rapidly increasing. From 2017 to 2021, regulations surrounding data localisation more than doubled.

The shift is often fuelled by geopolitical concerns, Welland Chu, Alliance Director for APAC at French multinational corporation Thales and the author of an ISACA article focusing on digital sovereignty, told GovInsider in a video interview.

A 2022 GovInsider article referenced Chinese-owned social media platform TikToks plan to move the private data of American citizens to cloud servers in the United States to prevent the risk of foreign governments accessing the data. This shift is also observed in Asia with Vietnam most recently imposing data localisation regulations in late 2022.

The same GovInsider article highlighted potential implications on economic growth due to increased costs on companies and the impediment of free trade.

A report funded by the NUS Centre for Trusted Internet and Community purports that Singapore, as a highly connected nation, may suffer from such practices given its reliance on the free flow of data.

Additionally, data localisation could hinder the performance of technologies like artificial intelligence and cloud computing, which work best with access to a broad set of free-flowing data.

The cloud, for example, is an enabler, says Francesco Bonfiglio, CEO of Gaia-X, in an online interview. Gaia-X is a non-profit which aims to promote digital sovereignty and enable the creation of data spaces through the development of trusted platforms and standards.

[The cloud is] a motorway that allows you to openly share data, make available applications, increase the power and performance of those applications, and make the services and products more competitive and meaningful for citizens, he explains.

But organisations are hesitant to openly share this data as there is a lack of platforms that are uniformly trusted on the market today, Bonfiglio says. He cited how adoption rates of cloud technology remain low in Europe.

Hindering innovation will only slow down the development of more efficient and citizen-centric public services. Bonfiglio gave the example of healthcare. Currently, healthcare research is often limited to specific networks of hospitals or research institutions with pre-arranged agreements on how data sharing will be done. But in order to build new drugs and vaccines, it is ideal to collect as much data as possible.

If we could freely create an open and distributed data space to collect all the data to analyse them with the power of genomics, and identify new drugs we could be faster in developing drugs, he says.

But most of the core data of organisations is not on a cloud as they fear making data available on a technology where they have no control, Bonfiglio says. This is why he is promoting the broader concept of digital sovereignty as opposed to simply data localisation.

Digital sovereignty stipulates that information is subject to the rules of its originating jurisdiction (regardless of its actual location), according to a Gartner report published in December 2022. This means that even if data is exported and stored in another country, the use of and access to the data is governed by regulations of its home country.

An example of this is demonstrated by Article 45 of the EUs General Data Protection Regulation (GDPR), which details that personal data can only be transferred should the importing country or organisation have data protection regulations and measures equivalent to that of the EU.

This approach is increasingly in demand today, according to Chu. He cited a survey by the International Data Corporation which predicts that 40 per cent of major enterprises will mandate data sovereignty from their cloud service providers by 2025.

One way organisations can have digital sovereignty is through technical controls which give users the ability to retain control of their data, wherever it is on the cloud, Chu suggests.

Most data today on the cloud is encrypted, which ensures that data is rendered unintelligible for those that do not possess the cryptographic keys. But if the cloud operators are the ones in control of these keys, that means that they have the ability to decrypt the data as they wish, Chu says. Instead, it should be the data exporters that is, the organisations themselves as opposed to the cloud service providers who hold the cryptographic keys. (Note that the terms organisations, customers, and users are used interchangeably in this article. They refer to the entities from whom the sensitive data generate.)

He suggests that this can be done through three methods: Bring Your Own Key, Hold Your Own Key and Bring Your Own Encryption.

First, Bring Your Own Key means that customers are able to generate their own cryptographic key and import it to the cloud to decrypt their data if they require access.

Meanwhile, Hold Your Own Key,such as that offered by AWS or Google, provides an additional layer of security, as it means that the cryptographic keys are always in the hands of the customer. If the cloud service provider or a third party requires access to the data, they will first need to issue a request, which the customer can review and only grant the access based on the context of the request.

Finally, Bring Your Own Encryption means that the cloud users encrypt the data before it even enters the cloud and upload the encrypted data into the cloud.

These approaches ensure that cloud operators or third parties like foreign governments are unable to access and decrypt data that is on the cloud without prior consent by the users who retain control of their own data ultimately, Chu explains.

The importance of this has previously been recognised by the Court of Justice of the European Union (CJEU). In 2020, the CJEU had ruled that appropriate safeguards, enforceable rights and effective legal remedies must be in place before the data are transferred from Europe to the US, Chu had written in the blog post.

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The pursuit of digital sovereignty for a trusted, integrated internet - GovInsider