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Drones like these allow military personnel to remotely conduct surveillance and occasional combat operations if the UAV has onboard weaponry, while minimizing the risks to their soldiers. Although UAVs were initially used for military applications, they have since expanded into various civilian sectors. When people hear the term UAV (Unmanned Aerial Vehicle), the first thing that comes to mind are the drones used by the military, such as the or the RQ-12 Wasp. MQ-1 Predator 1.Delivery One such use for drones is the use in delivery services. By using UAVs for deliveries, companies such as Amazon could save money on shipping costs, which would reduce costs for consumers, who would receive their packages more quickly and reliably. . In November 2016, Domino’s delivered an order of a Peri-Peri Chicken Pizza and a Chicken and Cranberry Pizza to a customer in Whangaparaoa, New Zealand. The first successful delivery with a UAV was a pizza delivery Delivery drones have also been used to such as blood products, vaccines, pharmaceuticals, and medical samples. Whereas ground vehicles may not be able to reach a remote destination due to difficult terrain, a UAV can make the delivery with ease. These drones have been credited with saving lives by , as well as personal protective equipment and COVID-19 tests during the pandemic in the United States, Israel, and Ghana. transport medicinal products delivering blood in Rwanda 2.Journalism In recent years, UAVs have provided journalists with a unique and cost-effective way to capture footage and images from hard-to-reach places. Using a drone, news reporters can cover important events from various angles that would be impossible for a hand-held camera to capture. Additionally, can be used to capture footage from dangerous or inaccessible locations, such as war zones or natural disasters. drones It’s important to note that the use of drones in these situations must comply with , as well as sports league, venue, and local law enforcement agency rules. Federal Aviation Administration (FAA) regulations 3.Search and Rescue Search and rescue operations are often tricky and unpredictable. Time is paramount, as the missing person is most likely trapped in a life-threatening situation. And depending on the location, the terrain can make things exponentially more difficult for the rescue team. Using UAVs, the rescue team can cover more ground faster than if they were using ground-based vehicles. These drones can also provide an aerial view of the area and carry various sensors to identify specific objects and heat signatures on the ground, providing much-needed situational awareness. in June 2017, two hikers were reported missing in Pike National Forest in Colorado. Search and rescue teams, comprising twenty-five volunteers, searched on foot, with ATVs, and with the aid of a K-9 team. But it was the team using a UAV that found the two hikers in an area spanning thousands of acres in just two hours. In one such case https://www.youtube.com/watch?v=Y1VPZ5jt5Xw&embedable=true 4.Disaster Response By utilizing drones in disaster relief efforts, dangerous situations can be assessed quickly without placing risks on personnel. For example, During the Fukushima Daiichi nuclear disaster in 2011, without risking the lives of the relief workers. They have also proven excellent first responders since they can be deployed fast and easily compared to manned aerial vehicles. drones were used to survey the extent of the disaster As previously mentioned, drones are excellent at providing an aerial view of the area and can be equipped with infrared (IR) cameras to detect heat signatures. This is vital not only in search and rescue operations but also in surveying unstable areas. When dealing with the aftermath of an earthquake, a partially damaged building or a civic structure requires a thorough inspection before it can be deemed safe for humans to go there. Drones with sensors like IR cameras can survey these sites easily and safely. 5.Firefighting UAVs can also be a massive asset to the men and women who risk their lives fighting fires. They can be immensely useful in providing situational awareness about the fire and its surroundings. By using drones equipped with thermal cameras, firefighters can determine the extent of the fire and identify its source, which can greatly aid them in bringing the situation under control and conducting search and rescue operations. UAVs have also been used as effective tools in monitoring and suppressing wildfires. By using real-time data, firefighters can determine where a fire will move next, allowing them to make a plan for movement and evacuation quickly. In addition to providing important tactical data, UAVs are more maneuverable than helicopters or planes. 6.Agriculture Some of the more common agricultural applications for UAVs include pest control, plant health monitoring, livestock management, soil analysis, and aerial survey. Farmers must maintain constant vigilance, as their crops are susceptible to disease, fungus, and bugs. They also need to be aware of the carbon levels and nitrates in the soil, which can impact a plant’s productivity. Traditionally, farmers must monitor these factors by conducting visual inspections and sending soil samples to a laboratory. By using UAVs, however, these factors can be measured in real-time by utilizing a combination of RGB cameras, thermal imaging, and multi-spectral imaging. The Future of UAVs In this day and age, where technology is rapidly evolving to meet new demands, UAVs could fulfill a number of roles in current and future job sectors. Additionally, many people operate drones for various hobbies, such as photography and drone racing. However, no matter how UAVs get used, operators must ensure that they comply with any rules regarding drone operation in their country. For instance, drone operators in the United States need to obtain a from the FAA before flying a UAV. Remote Pilot Certificate What do you think about these use cases? Do you own a drone yourself? Let me know what you think in the comments below!

6 Important Use Cases for UAVs (Unmanned Aerial Vehicles)

6 usos comunes de los UAV (vehículos aéreos no tripulados)

6 utilisations courantes des UAV (véhicules aériens sans pilote)

यूएवी (मानवरहित हवाई वाहन) के लिए 6 सामान्य उपयोग

UAV (無人航空機) の 6 つの一般的な用途

6 Usos Comuns para UAVs (Veículos Aéreos Não Tripulados)

6 Cách sử dụng phổ biến cho UAV (Máy bay không người lái)

UAV（无人驾驶飞行器）的 6 种常见用途

Infrastructure drift, unmanaged resources, ghost assets, these are all well-known “silent killers” in our clouds.  Whether AWS, GCP, Kubernetes, Azure or anything else, when deploying our services to multiple clouds, we know that a unified inventory and management of our cloud resources is complicated, and there are many great tools out there that are looking to help solve this growing complexity. ‍ One thing that is often overlooked though is where our SaaS tooling comes into the mix.  When we adopt SaaS tools, we tend to see them as just that–tools, and not what they eventually actually are: additional siloed, unmanaged clouds with their own proprietary inventory of services, objects and resources. ‍ A phenomenon we have encountered often, when helping companies overcome drift, is a common neglect of cloud infrastructure tooling, such as CloudFlare, Okta, Mongo Atlas, Datadog, Git and many other popular SaaS platforms and tools that are part of our core operations.  How can we make these SaaS clouds immutable, versioned, scalable and monitored if these extensions aren't codified?  Is state drift in Okta less troubling than drift in your IAM roles, for example? How can we guarantee proper monitoring if our Datadog dashboards make it possible for anyone to cause drifts? ‍ These are just some of the questions that come to mind when we see this recurring anti-pattern in cloud operations today. But you may ask, why does this matter? ‍ If a growing understanding is dawning on DevOps engineers that it is much safer and less error prone to codify cloud resources, including the inherent benefits of managing these resources like all other code––whether the git history, peer reviews, PR automation and policy enforcement, it seems that SaaS service have not yet undergone a similar evolution and epiphany.  If clickops for cloud configuration has mostly been abandoned for IaC practices, SaaS tooling is predominantly still configured manually via the UI with minimal codification. Not surprisingly this leads to many similar problems you’d find in your cloud operations. Putting the Git in GitOps When it comes to Kubernetes and cloud native systems, that are so commonly associated with GitOps practices, which is considered the best practice and modern way for managing complex Kubernetes operations––the git part of gitops is all but neglected when it comes to managing these systems.  I’ll explain. ‍ If we look at the top downloaded Terraform providers for SaaS applications that are not clouds, the list and data is extremely compelling: ‍ DataDog/datadog 32.4M+ integrations/github 16.9M+ cloudflare/cloudflare 16.8M+ newrelic/newrelic 12.5M+ hashicorp/consul 9.7M+ PagerDuty/pagerduty 8.6M+ grafana/grafana 5.4M+ gitlabhq/gitlab 4.8M+ mongodb/mongodbatlas 4.2M+ okta/okta 4M+ elastic/ec 3.2M+ ‍ While there is an increasing trend towards codifying these resources through Terraform providers, if we take the number of downloads of the AWS Terraform provider or the next most popular cloud Azure, these are 750M+ and 127M+ respectively, placing the next most popular non-cloud provider at about 5% adoption (and ultimately codification). ‍ This is because not only would this codification need to be done for each SaaS tool individually, once these tools are configured through clickops, just translating this to IaC is an extremely complex undertaking (particularly in large organizations with multiple dashboards, clouds, services and other dependencies and resources). ‍ If we come back to thinking about how we convert our git operations to be GitOps native, we’d likely need to follow a post similar to this one that walks you through the process of .  And this is just one tool of many in a huge stack of SaaS tooling that would need to undergo a similar transformation, this is another example of a post that walks you through a .  And the list goes on.  Now imagine having 10s of tools, in large organizations––multiple teams and clouds.  The task is daunting just to think about.  Until now. managing your Github organization with Terraform similar transition for Datadog Codifying Your SaaS When thinking about the critical aspects of codifying your SaaS, there are a few angles it was important for Firefly to focus on to make this transition truly valuable for all DevOps teams.  The first layer of value is in the unified inventory of both cloud assets and SaaS assets in a single place. Just this alone enables DevOps teams to search, understand, and classify assets across all clouds––the operational ones or the tooling clouds.  Something that wasn’t possible before from a single dashboard or tool. ‍ The next aspect is actually getting all of these tools and assets managed as code.  If this has become the cloud standard, it’s not clear why this hasn’t happened for SaaS apps too.  We’ve spoken about the benefits of managing everything as code, but eventually once managed as code with the relevant guidelines and internal engineering practices applied, these can then be automated as part of CI/CD processes and the relevant gating & guardrails applied here too. ‍ Doing so manually would require engineers to translate all of their manual configurations (that are not always found in a single place in the UI, across the many layers of their application) into the relevant code configurations, and usually many times over if there are multiple applications, dashboards or tools.  This is now possible at the click of a single button, for all SaaS tools, in one place. ‍ If we take a look at a typical Firefly dashboard, we can see that typical SaaS tools have as low as 20%+ of codified resources, vs. 50% in cloud service providers. ‍ The companies that have flipped this number and codified these resources, were able to enjoy the IaC advantages of faster deployment cycles and standby configuration templates for disaster recovery scenarios. We hope you found this useful - feel free to drop any questions you may have in the comments. ‍

Codify your SaaS Apps: The Answer to the Unmanaged SaaS Jungle

Alibaba’s the Tmall Future Store shows how technology can help offline merchants thrive in the e-commerce age If you’ve ever waited in line impatiently to pay for groceries, chances are you’ve wished you could escape the typical service experience we all tend to encounter as shoppers. While delivery and e-commerce services offer one alternative for people hoping to avoid the hassle, a more recent effort at Alibaba is making it likely that you’ll soon be shopping in stores that let you walk in, grab what you need, and pay automatically as you walk out, simplifying retail for shoppers and merchants alike. To showcase its progress, Alibaba recently unveiled an experimental 80-square meter installation of its new Tmall Future Store at the 2018 Yunqi Computing Conference, inviting attendees to explore a convenience store without a single employee on staff. Inside, they were able to browse for food, snacks, and beverages while getting help as needed from a digital T-guide assistant, then settle their bills by simply exiting the premises. Visitors line up outside Alibaba’s Tmall Future Store at the 2018 Yunqi Computing Conference To further highlight this new mode of shopping, senior technical expert Shi Wei of Alibaba’s Taobao Technology Department addressed the conference with a speech covering where the emerging field of new retail has been so far and where it is going next. Inside, we look at highlights of his speech and explore the technology that is helping make unmanned retail a crucial part of that evolution today. From New Retail to Unmanned Retail While Tmall’s unmanned Future Store presents an entirely new paradigm for shopping, it is only one part of a broader movement to upgrade offline shopping through new technologies, business models, and channels, known together as ‘new retail’. At the most basic level, new retail ranges from vending machines that accept mobile payments to the cheap, open-shelved mini shops many workplaces now offer thanks to digitalized business models that expedite stocking and reduce overhead. On a larger scale, it also encompasses supermarkets like Alibaba’s Hema that combine features of online and offline commerce, as well as cooperative ventures with independent retailers like Starbucks. At the heart of new retail is data, and ways it can be used to reduce pain points for consumers and merchants alike in traditional retail settings. Market analysis indicates that early efforts to remove human workers from retail have been met with mixed reactions from consumers. Nevertheless, the output value for such ventures in China is predicted to rise from RMB 20 billion in 2017 to RMB 65 billion in 2020, driven mainly by rising labor costs, offline operating costs, and consumer demand. With technology now able to support the truly futuristic experience the Tmall Future Store captures, unmanned retail can focus on giving consumers a whole new way to shop, rather than simply upgrading existing options. According to an iResearch study of the Chinese market, 8.5 percent of the country’s consumers prefer to make purchases from human cashiers, while 23.7 percent prefer to scan a code as they leave the store. Beyond this, 29.8 percent prefer to pick up goods from a smart settlement basket that they retrieve at the exit, while an overwhelming 39 percent would prefer to simply walk out with their groceries in a form of automatic settlement. Having cleared the initial hurdle of cultivating demand for experiences like the Tmall Future Store, Alibaba has since been able to focus on the ways its new model can enhance business for its partners, as well as the efficiency of labor in the broader marketplace. In one instance, moving the group’s Alicool souvenir shop to a completely automated framework increased annual turnover by 75 percent and sales volume by 20 percent, attracting 56 percent more consumers than during the previous year. At just 120 square meters, the store is now able to handle as many as 2,300 visitors per day, largely owing to the average 4.5 seconds it takes shoppers to complete automatic settlement when exiting. In another example, Alibaba collaboratively renovated the Zhida Bookstore in Shanghai to enable automatic facial recognition-based settlement, greatly increasing the store’s daily turnover. Notably, Alibaba was able to apply findings from previous efforts to complete the installation of the new facilities in just 53 days, raising the store’s daily turnover by 78.3 percent. A Look Inside the Tmall Future Store With the Tmall Future Store, Alibaba has entered a phase it calls ‘unmanned retail 2.0’, significantly expanding on the capabilities Alicool and the Zhida Bookstore demonstrate. As well as a technical upgrade, the new phase involves a shift of emphasis from impressing customers to attracting existing merchants to the concept. Technically, the key advance captured in the Tmall Future Store is computer vision. Upon first entering the shop, customers scan a code with their phones to establish their virtual presence there. Once they have done so, a series of roughly 100 screens will respond as they move through the store, making personalized recommendations and providing directions to help locate items. At shelves, the system uses gravity sensors and built-in cameras to identify and add goods to customers’ virtual shopping carts. This enables customers to simply leave when ready, as the system will automatically collect payment for any items which have not been returned as they exit. To document these transactions, the system generates a virtual bill which appears in customers’ Alipay or Mobile Taobao accounts, and the items in the virtual bill will also include links for repurchasing if they are available online. A view of customer guidance systems inside the Tmall Future Store As a whole, this user experience traces the interaction of three core systemic capabilities: global tracking, product identification, and user-item matching. The last of these is by far the most critical, as any confusion that results in a payment being deducted from the wrong customer can invalidate the idea that automation can replace human services. In an example of how this can happen, visitors to the store at the conference occasionally crossed arms reaching for items, swapped items, or offered to help each other return items, challenging the algorithm to sort out conflicting digital impressions. The best solution to such problems is prevention through the effective digitalization of offline behavior, presenting an ongoing challenge for developers as they seek to make these novel capabilities fully marketable. Continuing Development for the Future In its present state, the Tmall Future Store introduces a coherent “6+1” technical architecture, in which five processing ends and a local gateway interact together with a coherent set of business priorities — namely transaction, processing, operating, and data storage in the cloud. While anyone hoping to open a store could work from this architecture, preparing it for widespread marketing still requires the targeted improvement of algorithm capabilities, reduction of hardware costs, and reduction of deployment costs. An overview of Alibaba’s most recent architecture for unmanned retail environments, now undergoing further development One current example of an algorithmic limitation is the need for customers to cooperate with sensors by standing still while selecting items or completing settlement. Ideally, scanners should be equipped to handle some amount of motion or other variations, presenting a complex deep learning problem for optimization. Further, the system’s facial recognition component uses an initial scan image of each customer as a “one-off” point of reference. As even human observers may struggle to tell whether two images from different angles show the same person’s face, refining the recognition algorithm poses formidable challenges. Another outstanding challenge is to make algorithms more adaptable across environments, as each new unmanned store requires deploying and training the model to work within its specific dimensions and conditions. The key to doing so is separating the tuning of specific parameters from the model, which can be done using information gathered through the repeated deployment of its environmental sensors in different environments. In terms of market entry barriers, hardware costs remain preventive, not because they must be high but because the processing challenges developers currently face place heavy demands on equipment. Moreover, transforming a traditional brick-and-mortar shop into an unmanned store takes up a great deal of time, as engineers need to work on a by-case basis to update devices and systems one by one. To reduce costs, Alibaba is working to narrow down the functionality of devices. Because the cost of increasing computing power is not linear, boosting central server power to cover more end functions is not viable in the age of edge computing. To counter this, cameras and screens deployed in unmanned stores will need upgrades to their own computing power. Another cost reduction approach in the works is device standardization. By making all devices replaceable, repairable, and consistent, developers hope deployment and maintenance processes can be streamlined and standard procedures enabled. This would allow for a robotic approach to off-hours maintenance and inspection for both hardware and AI. With the ultimate goal of establishing a device management system, developers are seeking applications for an Alibaba Cloud product called Linkedge, which can be used to develop a hot-swappable HAL layer for the framework that uploads and downloads device information for monitoring. In effect, this means the system will be remotely upgradable, deployable across different devices, and primed for the addition of new functions — in short, perfectly suited to the mobile era. Looking Ahead To summarize the importance of the Tmall Future Store, Alibaba’s Shi Wei described it as an offline Taobao designed to be as interactive as the mobile phones that shoppers carry in with them. Like the first wave of online commerce that Taobao emerged with, it is offering new ways to study people’s habits and preferences as shoppers, making stores both a laboratory for understanding economic activity and a setting for putting that knowledge to use. Some of the many ways data from unmanned retail stores can help merchants thrive With the rise of e-commerce, many have voiced fears that platforms like Taobao will eventually make offline retail obsolete. With its Tmall Future Store, Alibaba is showing how the technology enabling it might instead be used to improve business for offline merchants, helping them to make smarter choices and better returns on investment. As a first step in unmanned retail, its completion should provide a valuable foundation for developments to come. Alibaba Tech First hand and in-depth information about Alibaba’s latest technology → Facebook: . Twitter: . “Alibaba Tech” “AlibabaTech”

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A microservices architecture is the gold standard for building scalable web applications. Gartner estimates that 74% of organizations use microservices for their web applications, with another 23% planning to use them soon. If you’re an IT leader, architect, or developer, you might have experienced the faster deployments, better fault isolation, and easier scaling that come with microservices. However, the old maxim, “there’s no such thing as a free lunch,” rings true here. Because microservices can also lead to significant operational overhead and complexity. Microservices sprawl, unclear component dependencies, service duplication, and anti-patterns like circular dependencies are all part of the chaos that comes hand-in-hand with microservices. Perhaps you’ve seen some of the common symptoms of this microservices chaos: Engineering velocity drastically slows down
Onboarding new developers grows more difficult
Mean time to recovery (MTTR) for outages or performance issues increases
Overall system resiliency diminishes, putting business outcomes at risk While it’s still best practice to modernize monolithic applications by breaking them into microservices, it’s also best practice to proactively guard against the issues that microservices introduce. Architecture Governance to Control the Chaos The best way to proactively guard against these problems is by using architecture governance — a collection of rules, processes, and practices that manage and control your software architecture. With proper software architecture governance, you can reduce microservices complexity, ramp up developers faster, reduce MTTR, and improve the resiliency of your system, all while building a culture of intentionality. (Note that we’re talking about software architecture governance in this article, not enterprise architecture governance, which has been around for some time and is concerned with the policies, processes, and procedures of enterprise architecture strategies.) But exactly how do you build architecture governance? Governance, whether we’re talking about access, data, or architecture, is often just establishing a set of rules. But for architecture governance, you need more than just rules. You must start with architectural observability. Architectural Observability: The Foundation of Good Governance To implement effective governance, you first need comprehensive visibility into your software architecture. Architectural observability gives you a continuous, real-time view of your architecture. It provides your team with the necessary insights to manage the architecture and interconnectedness of distributed applications. Architectural observability provides documentation from a working system, it shows you systems flows, dependencies, what’s changing, and how your systems interact. It gives your team a thorough understanding of how the architecture works, how it drifts from release to release, and how its changes impact dependent services and resources. Without this visibility, you’re in trouble. If you’ve built a microservices project, you’ve probably encountered the results of a lack of visibility: Microservices that are difficult to manage
Unclear component dependencies
Service duplication
Anti-patterns such as circular dependencies that increase the risk of outages
Performance issues Your overall application resiliency, scalability, and engineering velocity are at high risk. And it only gets worse — the longer you let this lack of visibility continue, the more complex and unmanageable your architecture becomes. Good Observability Equals Good Governance However, with good architectural observability, you can visualize your architecture layer in real-time, gain an understanding of how your system works, and monitor changes as your architecture evolves. With observability, your team can proactively address issues, such as service sprawl and dependency mismanagement — before they lead to technical debt or system failures. And once you have that observability in place — once you know what’s happening and what’s changing — you can now create and enforce your rules of governance. You can implement a governance program that sits over your architecture, monitors, and controls changes, and allows you to build and manage microservices effectively by proactively applying standards and rules to your architecture and codebase as it evolves. Sounds great! But how do you actually build this? Let’s look at an example of how to build this in the real world. Implementing Architectural Observability and Governance First, you need to implement your architectural observability. The domain of architectural observability is new and rapidly evolving. For our example, we’ll use one of the platforms at the forefront — vFunction. vFunction is an AI-driven platform that provides visualizations of software architecture. vFunction leverages AI and OpenTelemetry tracing to enable teams to track every aspect of their architecture and monitor changes between releases. It can quickly identify complex flows, architectural, technical debt, and architectural drift. For example, vFunction allows you to better understand your distributed applications: Visualize the architecture and automatically create exportable sequence diagrams of all system and data flows.
Identify drift, such as new services or dependencies.
Identify resource exclusivity changes (such as multiple new services accessing the same database table) versus previous releases.
Identify circular dependencies between services that could impact resiliency.
Identify duplicate functionality/services that may be merged to remove complexity.
Maintain an up-to-date, real-time view of the global microservices ecosystem. vFunction uses the same data that APM tools use (so it’s straightforward to install) but uses a different layer of intelligence to address the architectural root cause of issues. This is especially important for managing the sprawl and complexity that result from the rapid and frequent deployment of microservices. Implementing Rules to Maintain Standards After implementing your observability, you’re ready to create and enforce your rules of governance. vFunction allows you to create architecture rules and tags and then monitor your architecture in real time against those rules, enforcing your critical governance policies. Any time a deployment or merge request breaks these rules, you can block that action. What type of rules might your governance enforce? You could enforce rules such as: Dependency requirements. For example, certain services should or should not communicate with other services.
Restricted resources.
Multiple services should not depend on the same database table.
Changes should not increase service interdependencies.
Restrictions on new multi-hop flows that could impact performance and resiliency. vFunction can watch and send alerts for all of the above. By implementing a focused set of rules and tags, you can create adaptive governance that guides developers in maintaining an optimal, clean, and efficient architecture. You can build architecture governance into your code and your culture. Conclusion Microservices chaos can threaten to derail development velocity, system resiliency, and operational efficiency. Architecture governance offers a way out of this chaos — a way to regain control over your architecture and steer it toward greater scalability and stability. Whether your organization is just starting to scale with microservices or is already in the depths of microservices sprawl, it’s the right time to introduce architecture governance to ensure that your architecture is efficient and resilient — and ready to support your next phase of growth. A microservices architecture is the gold standard for building scalable web applications. Gartner estimates that 74% of organizations use microservices for their web applications , with another 23% planning to use them soon. 74% of organizations use microservices for their web applications If you’re an IT leader, architect, or developer, you might have experienced the faster deployments, better fault isolation, and easier scaling that come with microservices. However, the old maxim, “ there’s no such thing as a free lunch ,” rings true here. Because microservices can also lead to significant operational overhead and complexity. Microservices sprawl, unclear component dependencies, service duplication, and anti-patterns like circular dependencies are all part of the chaos that comes hand-in-hand with microservices. there’s no such thing as a free lunch Perhaps you’ve seen some of the common symptoms of this microservices chaos: Engineering velocity drastically slows down Onboarding new developers grows more difficult Mean time to recovery (MTTR) for outages or performance issues increases Overall system resiliency diminishes, putting business outcomes at risk Engineering velocity drastically slows down Onboarding new developers grows more difficult Mean time to recovery (MTTR) for outages or performance issues increases Overall system resiliency diminishes, putting business outcomes at risk While it’s still best practice to modernize monolithic applications by breaking them into microservices, it’s also best practice to proactively guard against the issues that microservices introduce . it’s also best practice to proactively guard against the issues that microservices introduce Architecture Governance to Control the Chaos The best way to proactively guard against these problems is by using architecture governance — a collection of rules, processes, and practices that manage and control your software architecture. architecture governance With proper software architecture governance, you can reduce microservices complexity, ramp up developers faster, reduce MTTR, and improve the resiliency of your system, all while building a culture of intentionality. (Note that we’re talking about software architecture governance in this article, not enterprise architecture governance, which has been around for some time and is concerned with the policies, processes, and procedures of enterprise architecture strategies.) But exactly how do you build architecture governance? Governance, whether we’re talking about access, data, or architecture, is often just establishing a set of rules. But for architecture governance, you need more than just rules. You must start with architectural observability. Architectural Observability: The Foundation of Good Governance To implement effective governance, you first need comprehensive visibility into your software architecture . comprehensive visibility into your software architecture Architectural observability gives you a continuous, real-time view of your architecture. It provides your team with the necessary insights to manage the architecture and interconnectedness of distributed applications. Architectural observability Architectural observability provides documentation from a working system, it shows you systems flows, dependencies, what’s changing, and how your systems interact. It gives your team a thorough understanding of how the architecture works, how it drifts from release to release, and how its changes impact dependent services and resources. Without this visibility, you’re in trouble. If you’ve built a microservices project, you’ve probably encountered the results of a lack of visibility: Microservices that are difficult to manage Unclear component dependencies Service duplication Anti-patterns such as circular dependencies that increase the risk of outages Performance issues Microservices that are difficult to manage Unclear component dependencies Service duplication Anti-patterns such as circular dependencies that increase the risk of outages Performance issues Your overall application resiliency, scalability, and engineering velocity are at high risk. And it only gets worse — the longer you let this lack of visibility continue, the more complex and unmanageable your architecture becomes. Good Observability Equals Good Governance However, with good architectural observability, you can visualize your architecture layer in real-time, gain an understanding of how your system works, and monitor changes as your architecture evolves. with With observability, your team can proactively address issues, such as service sprawl and dependency mismanagement — before they lead to technical debt or system failures. And once you have that observability in place — once you know what’s happening and what’s changing — you can now create and enforce your rules of governance. You can implement a governance program that sits over your architecture, monitors, and controls changes, and allows you to build and manage microservices effectively by proactively applying standards and rules to your architecture and codebase as it evolves. Sounds great! But how do you actually build this? Let’s look at an example of how to build this in the real world. Implementing Architectural Observability and Governance First, you need to implement your architectural observability. The domain of architectural observability is new and rapidly evolving. For our example, we’ll use one of the platforms at the forefront — vFunction . vFunction is an AI-driven platform that provides visualizations of software architecture. vFunction vFunction leverages AI and OpenTelemetry tracing to enable teams to track every aspect of their architecture and monitor changes between releases. It can quickly identify complex flows, architectural, technical debt, and architectural drift. OpenTelemetry For example, vFunction allows you to better understand your distributed applications: Visualize the architecture and automatically create exportable sequence diagrams of all system and data flows. Identify drift, such as new services or dependencies. Identify resource exclusivity changes (such as multiple new services accessing the same database table) versus previous releases. Identify circular dependencies between services that could impact resiliency. Identify duplicate functionality/services that may be merged to remove complexity. Maintain an up-to-date, real-time view of the global microservices ecosystem. Visualize the architecture and automatically create exportable sequence diagrams of all system and data flows. Identify drift, such as new services or dependencies. Identify resource exclusivity changes (such as multiple new services accessing the same database table) versus previous releases. Identify circular dependencies between services that could impact resiliency. Identify duplicate functionality/services that may be merged to remove complexity. Maintain an up-to-date, real-time view of the global microservices ecosystem. vFunction uses the same data that APM tools use (so it’s straightforward to install) but uses a different layer of intelligence to address the architectural root cause of issues. This is especially important for managing the sprawl and complexity that result from the rapid and frequent deployment of microservices. Implementing Rules to Maintain Standards After implementing your observability, you’re ready to create and enforce your rules of governance. vFunction allows you to create architecture rules and tags and then monitor your architecture in real time against those rules, enforcing your critical governance policies. Any time a deployment or merge request breaks these rules, you can block that action. What type of rules might your governance enforce ? You could enforce rules such as: What type of rules might your governance enforce Dependency requirements. For example, certain services should or should not communicate with other services. Restricted resources. Multiple services should not depend on the same database table. Changes should not increase service interdependencies. Restrictions on new multi-hop flows that could impact performance and resiliency. Dependency requirements. For example, certain services should or should not communicate with other services. Restricted resources. Multiple services should not depend on the same database table. Changes should not increase service interdependencies. Restrictions on new multi-hop flows that could impact performance and resiliency. vFunction can watch and send alerts for all of the above. vFunction can watch and send alerts By implementing a focused set of rules and tags, you can create adaptive governance that guides developers in maintaining an optimal, clean, and efficient architecture. You can build architecture governance into your code and your culture. Conclusion Microservices chaos can threaten to derail development velocity, system resiliency, and operational efficiency. Architecture governance offers a way out of this chaos — a way to regain control over your architecture and steer it toward greater scalability and stability. Whether your organization is just starting to scale with microservices or is already in the depths of microservices sprawl, it’s the right time to introduce architecture governance to ensure that your architecture is efficient and resilient — and ready to support your next phase of growth.

Mastering Microservices: Building Effective Architecture Governance for Scalability and Stability

Sujeet Kashid is a Robotics Software Engineer, currently based in the San Francisco Bay Area working with Woven by Toyota. His expertise is integrating and improving the Human Machine Interface (HMI) systems of autonomous vehicles, and he completed his Masters in Science from University of Cincinnati with a specialization in Robotics and Intelligent Systems. Kashid’s research could help increase the safety of operation of Unmanned Aerial Vehicles (UAVs) in GPS-denied indoor areas, and provides a way for multiple robots to cooperate and distribute tasks. Kashid sat down with us and broke down the evolution of the autonomous vehicles, and more importantly, what the next steps are for the automobile industry. Imagine a world of driverless cars–it seems somewhat Orwellian, doesn’t it? Like a scene out of your favorite sci-fi movie, where cars zoom around at breakneck speeds, you never fear they will collide, or in the 1990 film, Total Recall, when we see a driverless taxi. With autonomous vehicles already deployed through major cities like San Francisco and Los Angeles, that sci-fi future is (actually) now. But the world of autonomous vehicles is not limited to this sci-fi vision, it has implications much more far reaching. To gain a better understanding of how autonomous vehicles will continue to evolve, I was lucky enough to sit down with robotics software engineer Sujeet Kashid, who currently works with Woven, which is leading next-generation cars at Toyota. Woven by Toyota is leading Toyota’s “transformation into a mobility company,” according to their website. They’re trying to innovate with one of the most staunch and traditional of all Japanese car brands, which has a vision for the future. If you’re planning on saving up to buy a Toyota car anytime soon, they’re working on incorporating autonomous driving and advanced driver assistance systems. Some of the things they’re working with include OS platform Arene, Woven City, and Toyota's new “test course” for mobility. He is situated at the crossroads of the industry, and gives us a clear picture of not only where we are today, but what the future holds. The Road to Automated Vehicles is Paved with Good Intentions According to Forbes Advisor, in 2022, a total of 42,514 fatal car accidents happened on roads across the country. Meanwhile, 1.6 million crashes resulted in injuries and an additional 4.2 million accidents caused property damages. The main cause of car crashes is drunk driving, lack of seatbelt use, speeding and distracted drivers. Having autonomous vehicles on the roads will go beyond just aiding human error, we’re entering a world where the vehicles themselves can communicate with each other. “In a world where autonomous vehicles can talk to each other, and work cooperatively, they will be able to quickly clear out a way for the emergency vehicles to move faster and uninterrupted to their destination,” said Kashid. Because let's face it: Autonomous vehicles have the potential to reduce road collisions. They also provide a variety of other societal and environmental benefits. But how much of this is realistic? “The intention of robotics is to help people and make the world a better place,” says Kashid, when talking about the safety effects of the robotics industry. The industry has made drastic efforts to help improve the wellbeing of individuals both on the roads and at work–it’s safety focused. “Most road accidents can be attributed to driver error,” says Kashid. “My hope is that autonomous vehicles become ubiquitous making the road a safer place.” In fact, the implications stretch further than the road, entering into workplace safety and time-saving measures. “My research provides a method to operate Unmanned Aerial Vehicles in indoor, GPS-denied environments safely,” says Kashid, diving into another future avenue of exploration. The ability for warehouse vehicles to operate independently will only serve to speed up operations and lower costs. Having autonomous vehicles communicate with each other can help guard our roads with safety. “They can communicate with each other to reach road help faster and save more lives,” he said. “In a world where autonomous vehicles can talk to each other and work cooperatively, this is totally possible.” Pedal To The Metal: The Speedbumps Autonomous Vehicles Face Putting autonomous vehicles on the road is no cheap task. Just look at Waymo, formerly known as the Google self-driving car project, who are trying to make it safe and easy for people to get around with driverless taxis. Waymo has raised over $5.5 billion to get their vehicles on the road in Phoenix alone. While autonomous vehicles are significantly safer than their manned alternatives, a couple of accidents have set the project back. Meanwhile, Tesla switches gears for their customers. Most advanced autonomous features available to many consumers, like Full Self-Driving with Tesla, are only Level 2 autonomous vehicles,” says Kashid. Toyota offers Level 2 autonomous vehicle systems as well, one that can control both steering and acceleration, but Level 2 means it still requires a driver. Right now the issue is twofold before according to the next level. “The next stage in autonomous vehicles is making the functions ubiquitous and affordable,” said Kashid. The potential speed bumps in the development of autonomous vehicles is simple. “I think it's money and time,” said Kashid. “The first cars invented were battery electric vehicles, but it needed huge technological advancements in batteries to make them hold more energy, last longer and make them affordable.” Autonomous cars need to become more affordable, too. “We need technological advancements to make autonomous vehicles affordable, and that research and development takes a lot of money now to enjoy its benefits in the near future,” he adds. Forecasting The Future: Creating Jobs While Innovating With Autonomous Vehicles The intention of robotics is to help people and make the world a better place, notes Kashid. But some tasks may get phased out as technology evolves, and people might lose jobs. “I believe the biggest area of need is that free and accessible skills education be provided to these work forces, so they can be moved to other kinds of jobs,” said Kashid. For example, an Uber driver can be taught to be an autonomous vehicle operator for testing and gathering data.” This might not only mitigate job losses in certain areas, but also help the growth of development of autonomous vehicles if that work force is taught how to help, thus achieving a symbiotic relationship,” he adds. Who doesn’t want to live in a safer world? In order for this vision to be played out there needs to be a massive effort made. This falls in large part on the automobile industry to create affordable autonomous vehicles, but also on the consumers to have faith in the product. Change is scary and often met by resistance. In this case, change should be met with open arms. The future is not far away and thanks to the efforts of those like Kashid, it might be here sooner than we think. Sujeet Kashid is a Robotics Software Engineer, currently based in the San Francisco Bay Area working with Woven by Toyota. His expertise is integrating and improving the Human Machine Interface (HMI) systems of autonomous vehicles, and he completed his Masters in Science from University of Cincinnati with a specialization in Robotics and Intelligent Systems. Kashid’s research could help increase the safety of operation of Unmanned Aerial Vehicles (UAVs) in GPS-denied indoor areas, and provides a way for multiple robots to cooperate and distribute tasks. Kashid sat down with us and broke down the evolution of the autonomous vehicles, and more importantly, what the next steps are for the automobile industry. Sujeet Kashid is a Robotics Software Engineer, currently based in the San Francisco Bay Area working with Woven by Toyota. His expertise is integrating and improving the Human Machine Interface (HMI) systems of autonomous vehicles, and he completed his Masters in Science from University of Cincinnati with a specialization in Robotics and Intelligent Systems. Kashid’s research could help increase the safety of operation of Unmanned Aerial Vehicles (UAVs) in GPS-denied indoor areas, and provides a way for multiple robots to cooperate and distribute tasks. Kashid sat down with us and broke down the evolution of the autonomous vehicles, and more importantly, what the next steps are for the automobile industry. Sujeet Kashid is a Robotics Software Engineer, currently based in the San Francisco Bay Area working with Woven by Toyota. His expertise is integrating and improving the Human Machine Interface (HMI) systems of autonomous vehicles, and he completed his Masters in Science from University of Cincinnati with a specialization in Robotics and Intelligent Systems. Kashid’s research could help increase the safety of operation of Unmanned Aerial Vehicles (UAVs) in GPS-denied indoor areas, and provides a way for multiple robots to cooperate and distribute tasks. Kashid sat down with us and broke down the evolution of the autonomous vehicles, and more importantly, what the next steps are for the automobile industry. Imagine a world of driverless cars–it seems somewhat Orwellian, doesn’t it? Like a scene out of your favorite sci-fi movie, where cars zoom around at breakneck speeds, you never fear they will collide, or in the 1990 film, Total Recall, when we see a driverless taxi. With autonomous vehicles already deployed through major cities like San Francisco and Los Angeles, that sci-fi future is (actually) now. Total Recall, But the world of autonomous vehicles is not limited to this sci-fi vision, it has implications much more far reaching. To gain a better understanding of how autonomous vehicles will continue to evolve, I was lucky enough to sit down with robotics software engineer Sujeet Kashid, who currently works with Woven, which is leading next-generation cars at Toyota. Woven by Toyota is leading Toyota’s “transformation into a mobility company,” according to their website. They’re trying to innovate with one of the most staunch and traditional of all Japanese car brands, which has a vision for the future. If you’re planning on saving up to buy a Toyota car anytime soon, they’re working on incorporating autonomous driving and advanced driver assistance systems. Some of the things they’re working with include OS platform Arene, Woven City, and Toyota's new “test course” for mobility. He is situated at the crossroads of the industry, and gives us a clear picture of not only where we are today, but what the future holds. The Road to Automated Vehicles is Paved with Good Intentions According to Forbes Advisor , in 2022, a total of 42,514 fatal car accidents happened on roads across the country. Meanwhile, 1.6 million crashes resulted in injuries and an additional 4.2 million accidents caused property damages. The main cause of car crashes is drunk driving, lack of seatbelt use, speeding and distracted drivers. Forbes Advisor Forbes Advisor Having autonomous vehicles on the roads will go beyond just aiding human error, we’re entering a world where the vehicles themselves can communicate with each other. “In a world where autonomous vehicles can talk to each other, and work cooperatively, they will be able to quickly clear out a way for the emergency vehicles to move faster and uninterrupted to their destination,” said Kashid. Because let's face it: Autonomous vehicles have the potential to reduce road collisions. They also provide a variety of other societal and environmental benefits. But how much of this is realistic? “The intention of robotics is to help people and make the world a better place,” says Kashid, when talking about the safety effects of the robotics industry. The industry has made drastic efforts to help improve the wellbeing of individuals both on the roads and at work–it’s safety focused. “Most road accidents can be attributed to driver error,” says Kashid. “My hope is that autonomous vehicles become ubiquitous making the road a safer place.” In fact, the implications stretch further than the road, entering into workplace safety and time-saving measures. “My research provides a method to operate Unmanned Aerial Vehicles in indoor, GPS-denied environments safely,” says Kashid, diving into another future avenue of exploration. The ability for warehouse vehicles to operate independently will only serve to speed up operations and lower costs. Having autonomous vehicles communicate with each other can help guard our roads with safety. “They can communicate with each other to reach road help faster and save more lives,” he said. “In a world where autonomous vehicles can talk to each other and work cooperatively, this is totally possible.” Pedal To The Metal: The Speedbumps Autonomous Vehicles Face Putting autonomous vehicles on the road is no cheap task. Just look at Waymo, formerly known as the Google self-driving car project, who are trying to make it safe and easy for people to get around with driverless taxis. Waymo has raised over $5.5 billion to get their vehicles on the road in Phoenix alone. While autonomous vehicles are significantly safer than their manned alternatives, a couple of accidents have set the project back. Meanwhile, Tesla switches gears for their customers. Most advanced autonomous features available to many consumers, like Full Self-Driving with Tesla, are only Level 2 autonomous vehicles,” says Kashid. Toyota offers Level 2 autonomous vehicle systems as well, one that can control both steering and acceleration, but Level 2 means it still requires a driver. Right now the issue is twofold before according to the next level. “The next stage in autonomous vehicles is making the functions ubiquitous and affordable,” said Kashid. The potential speed bumps in the development of autonomous vehicles is simple. “I think it's money and time,” said Kashid. “The first cars invented were battery electric vehicles, but it needed huge technological advancements in batteries to make them hold more energy, last longer and make them affordable.” Autonomous cars need to become more affordable, too. “We need technological advancements to make autonomous vehicles affordable, and that research and development takes a lot of money now to enjoy its benefits in the near future,” he adds. Forecasting The Future: Creating Jobs While Innovating With Autonomous Vehicles The intention of robotics is to help people and make the world a better place, notes Kashid. But some tasks may get phased out as technology evolves, and people might lose jobs. “I believe the biggest area of need is that free and accessible skills education be provided to these work forces, so they can be moved to other kinds of jobs,” said Kashid. For example, an Uber driver can be taught to be an autonomous vehicle operator for testing and gathering data.” This might not only mitigate job losses in certain areas, but also help the growth of development of autonomous vehicles if that work force is taught how to help, thus achieving a symbiotic relationship,” he adds. Who doesn’t want to live in a safer world? In order for this vision to be played out there needs to be a massive effort made. This falls in large part on the automobile industry to create affordable autonomous vehicles, but also on the consumers to have faith in the product. Change is scary and often met by resistance. In this case, change should be met with open arms. The future is not far away and thanks to the efforts of those like Kashid, it might be here sooner than we think.

The Next Step Of Autonomous Cars: Safety Concerns And Affordability, Says Expert

**SINGAPORE, Singapore, October 3rd, 2024/CyberNewsWire/--**At DEF CON 32, the SquareX research team delivered a hard-hitting presentation titled Sneaky Extensions: The MV3 Escape Artists where they shared their findings on how malicious browser extensions are bypassing Google’s latest standard for building chrome extensions: Manifest V3 (MV3)’s security features, putting millions of users and businesses at risk. SquareX’s research team publicly demonstrated rogue extensions built on MV3. The key findings include: Extensions can steal live video streams, such as those from Google Meet and Zoom Web, without requiring special permissions.
The rogue extensions can act on a user’s behalf to add collaborators to private GitHub repositories.
The extensions are capable of hooking into login events to redirect users to a page disguised as a password manager login.
Extensions built on MV3 can steal site cookies, browsing history, bookmarks, and download history with ease, like their MV2 counterparts.
The rogue extensions can add pop-ups to the active webpage, such as fake software update prompts, tricking users into downloading malware. Browser extensions have long been a target for malicious actors — a Stanford University report estimates that 280 million malicious Chrome extensions were installed in recent years. Google has struggled to address this issue, often relying on independent researchers to identify malicious extensions. In some cases, Google has had to manually remove them, such as the 32 extensions taken down in June last year. By the time they were removed, these extensions had already been installed 75 million times. Most of these issues arose because the Chrome extension standard, Manifest Version 2 (MV2), was riddled with loopholes that granted extensions excessive permissions, and allowed scripts to be injected on the fly, often without users' knowledge. This allowed malicious actors to easily exploit these vulnerabilities to steal data, inject malware, and access sensitive information. MV3 was introduced to address these problems by tightening security, limiting permissions, and requiring extensions to declare their scripts beforehand. However, SquareX’s research shows that MV3 falls short in many critical areas, demonstrating how attackers are still able to exploit minimal permissions to carry out malicious activity. Both individual users and enterprises are exposed, even under the newer MV3 framework. Today's security solutions, such as endpoint security, SASE/SSE, and Secure Web Gateways (SWG), lack visibility into installed browser extensions. There is currently no mature tool or platform capable of dynamically instrumenting these extensions, leaving enterprises without the ability to accurately assess whether an extension is safe or malicious. SquareX is committed to the highest level of cybersecurity protection for enterprises and has built key innovative features to solve this problem, which include; Fine grained policies to decide which extensions to allow / block and parameters include extension permissions, creation date, last update, reviews, ratings, user count, author attributes etc
SquareX blocks network requests sent by extensions at run time – based on policies, heuristics and machine learning insights
SquareX is also experimenting with dynamic analysis of Chrome Extensions using a modified Chromium browser in its cloud server These are part of SquareX’s Browser Detection and Response solution which is being deployed at medium-large enterprises and is effectively blocking these attacks. Vivek Ramachandran, Founder & CEO of SquareX, warned about the mounting risks: “Browser extensions are a blind spot for EDR/XDR and SWGs have no way to infer their presence. This has made browser extensions a very effective and potent technique to silently be installed and monitor enterprise users, and attackers are leveraging them to monitor communication over web calls, act on the victim’s behalf to give permissions to external parties, steal cookies and other site data and so on.” “Our research proves that without dynamic analysis and the ability for enterprises to apply stringent policies, it will not be possible to identify and block these attacks. Google MV3, though well intended, is still far away from enforcing security at both a design and implementation phase,” said Vivek Ramachandran. About SquareX SquareX helps organizations detect, mitigate and threat-hunt client-side web attacks happening against their users in real time. SquareX's industry-first Browser Detection and Response (BDR) solution, takes an attack-focused approach to browser security, ensuring enterprise users are protected against advanced threats like malicious QR Codes, Browser-in-the-Browser phishing, macro-based malware, malicious extensions and other web attacks encompassing malicious files, websites, scripts, and compromised networks. With SquareX, enterprises can also provide contractors and remote workers with secure access to internal applications, enterprise SaaS, and convert the browsers on BYOD / unmanaged devices into trusted browsing sessions. Contact Head of PR Junice Liew SquareX junice@sqrx.com This story was distributed as a release by CyberNewsWire under HackerNoon’s Business Blogging Program. Learn more about the program here **SINGAPORE, Singapore, October 3rd, 2024/CyberNewsWire/--**At DEF CON 32, the SquareX research team delivered a hard-hitting presentation titled Sneaky Extensions: The MV3 Escape Artists where they shared their findings on how malicious browser extensions are bypassing Google’s latest standard for building chrome extensions: Manifest V3 (MV3)’s security features, putting millions of users and businesses at risk. SquareX SquareX SquareX’s research team publicly demonstrated rogue extensions built on MV3. The key findings include: Extensions can steal live video streams, such as those from Google Meet and Zoom Web, without requiring special permissions. The rogue extensions can act on a user’s behalf to add collaborators to private GitHub repositories. The extensions are capable of hooking into login events to redirect users to a page disguised as a password manager login. Extensions built on MV3 can steal site cookies, browsing history, bookmarks, and download history with ease, like their MV2 counterparts. The rogue extensions can add pop-ups to the active webpage, such as fake software update prompts, tricking users into downloading malware. Extensions can steal live video streams, such as those from Google Meet and Zoom Web, without requiring special permissions. The rogue extensions can act on a user’s behalf to add collaborators to private GitHub repositories. The extensions are capable of hooking into login events to redirect users to a page disguised as a password manager login. Extensions built on MV3 can steal site cookies, browsing history, bookmarks, and download history with ease, like their MV2 counterparts. The rogue extensions can add pop-ups to the active webpage, such as fake software update prompts, tricking users into downloading malware. Browser extensions have long been a target for malicious actors — a Stanford University report estimates that 280 million malicious Chrome extensions were installed in recent years. Google has struggled to address this issue, often relying on independent researchers to identify malicious extensions. report report In some cases, Google has had to manually remove them, such as the 32 extensions taken down in June last year. By the time they were removed, these extensions had already been installed 75 million times. Most of these issues arose because the Chrome extension standard, Manifest Version 2 (MV2), was riddled with loopholes that granted extensions excessive permissions, and allowed scripts to be injected on the fly, often without users' knowledge. 32 extensions 32 extensions This allowed malicious actors to easily exploit these vulnerabilities to steal data, inject malware, and access sensitive information. MV3 was introduced to address these problems by tightening security, limiting permissions, and requiring extensions to declare their scripts beforehand. However, SquareX’s research shows that MV3 falls short in many critical areas, demonstrating how attackers are still able to exploit minimal permissions to carry out malicious activity. Both individual users and enterprises are exposed, even under the newer MV3 framework. Today's security solutions, such as endpoint security, SASE/SSE, and Secure Web Gateways (SWG), lack visibility into installed browser extensions. There is currently no mature tool or platform capable of dynamically instrumenting these extensions, leaving enterprises without the ability to accurately assess whether an extension is safe or malicious. SquareX is committed to the highest level of cybersecurity protection for enterprises and has built key innovative features to solve this problem, which include; Fine grained policies to decide which extensions to allow / block and parameters include extension permissions, creation date, last update, reviews, ratings, user count, author attributes etc SquareX blocks network requests sent by extensions at run time – based on policies, heuristics and machine learning insights SquareX is also experimenting with dynamic analysis of Chrome Extensions using a modified Chromium browser in its cloud server Fine grained policies to decide which extensions to allow / block and parameters include extension permissions, creation date, last update, reviews, ratings, user count, author attributes etc SquareX blocks network requests sent by extensions at run time – based on policies, heuristics and machine learning insights SquareX is also experimenting with dynamic analysis of Chrome Extensions using a modified Chromium browser in its cloud server These are part of SquareX’s Browser Detection and Response solution which is being deployed at medium-large enterprises and is effectively blocking these attacks. Browser Detection and Response Browser Detection and Response Vivek Ramachandran, Founder & CEO of SquareX, warned about the mounting risks: “Browser extensions are a blind spot for EDR/XDR and SWGs have no way to infer their presence. This has made browser extensions a very effective and potent technique to silently be installed and monitor enterprise users, and attackers are leveraging them to monitor communication over web calls, act on the victim’s behalf to give permissions to external parties, steal cookies and other site data and so on.” Vivek Ramachandran , Founder & CEO of SquareX , warned about the mounting risks: “Browser extensions are a blind spot for EDR/XDR and SWGs have no way to infer their presence. This has made browser extensions a very effective and potent technique to silently be installed and monitor enterprise users, and attackers are leveraging them to monitor communication over web calls, act on the victim’s behalf to give permissions to external parties, steal cookies and other site data and so on.” Vivek Ramachandran Vivek Ramachandran SquareX SquareX “Our research proves that without dynamic analysis and the ability for enterprises to apply stringent policies, it will not be possible to identify and block these attacks. Google MV3, though well intended, is still far away from enforcing security at both a design and implementation phase,” said Vivek Ramachandran. “Our research proves that without dynamic analysis and the ability for enterprises to apply stringent policies, it will not be possible to identify and block these attacks. Google MV3, though well intended, is still far away from enforcing security at both a design and implementation phase,” said Vivek Ramachandran. About SquareX SquareX helps organizations detect, mitigate and threat-hunt client-side web attacks happening against their users in real time. SquareX SquareX SquareX's industry-first Browser Detection and Response (BDR) solution, takes an attack-focused approach to browser security, ensuring enterprise users are protected against advanced threats like malicious QR Codes, Browser-in-the-Browser phishing, macro-based malware, malicious extensions and other web attacks encompassing malicious files, websites, scripts, and compromised networks. With SquareX, enterprises can also provide contractors and remote workers with secure access to internal applications, enterprise SaaS, and convert the browsers on BYOD / unmanaged devices into trusted browsing sessions. Contact Head of PR Junice Liew SquareX junice@sqrx.com This story was distributed as a release by CyberNewsWire under HackerNoon’s Business Blogging Program. Learn more about the program here This story was distributed as a release by CyberNewsWire under HackerNoon’s Business Blogging Program. Learn more about the program here here here

SquareX Shows How Malicious Extensions Bypass Google’s MV3 Restrictions

SquareX يوضح كيف تتجاوز الإضافات الضارة قيود MV3 الخاصة بشركة Google

SquareX показва как злонамерените разширения заобикалят MV3 ограниченията на Google

SquareX muestra cómo las extensiones maliciosas eluden las restricciones MV3 de Google

SquareX montre comment les extensions malveillantes contournent les restrictions MV3 de Google

A SquareX megmutatja, hogy a rosszindulatú bővítmények hogyan kerülik meg a Google MV3 korlátozásait

SquareX mostra come le estensioni dannose aggirano le restrizioni MV3 di Google

SquareX、悪質な拡張機能がGoogleのMV3制限を回避する方法を紹介

SquareX აჩვენებს, თუ როგორ აცილებენ მავნე გაფართოებები Google-ის MV3 შეზღუდვებს

SquareX покажува како злонамерните екстензии ги заобиколуваат ограничувањата за MV3 на Google

SquareX laat zien hoe kwaadaardige extensies de MV3-beperkingen van Google omzeilen

SquareX mostra como extensões maliciosas contornam as restrições MV3 do Google

SquareX waxay tusinaysaa sida Fidinta xaasidnimada leh ay uga gudubto Google's MV3

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