<?xml version="1.0" encoding="UTF-8"?><rss version="2.0" xmlns:content="http://purl.org/rss/1.0/modules/content/" xmlns:wfw="http://wellformedweb.org/CommentAPI/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:sy="http://purl.org/rss/1.0/modules/syndication/" xmlns:slash="http://purl.org/rss/1.0/modules/slash/" xmlns:georss="http://www.georss.org/georss" xmlns:geo="http://www.w3.org/2003/01/geo/wgs84_pos#" > <channel> <title>IoT Archives - CDInsights</title> <atom:link href="https://www.clouddatainsights.com/tag/iot/feed/" rel="self" type="application/rss+xml" /> <link>https://www.clouddatainsights.com/tag/iot/</link> <description>Trsanform Your Business in a Cloud Data World</description> <lastBuildDate>Thu, 14 Dec 2023 20:50:13 +0000</lastBuildDate> <language>en-US</language> <sy:updatePeriod> hourly </sy:updatePeriod> <sy:updateFrequency> 1 </sy:updateFrequency> <generator>https://wordpress.org/?v=6.6.1</generator> <image> <url>https://www.clouddatainsights.com/wp-content/uploads/2022/05/CDI-Favicon-2-45x45.jpg</url> <title>IoT Archives - CDInsights</title> <link>https://www.clouddatainsights.com/tag/iot/</link> <width>32</width> <height>32</height> </image> <site xmlns="com-wordpress:feed-additions:1">207802051</site> <item> <title>Digital Twins: The IoT-Powered Sandboxes Behind Smart Manufacturing</title> <link>https://www.clouddatainsights.com/digital-twins-the-iot-powered-sandboxes-behind-smart-manufacturing/</link> <comments>https://www.clouddatainsights.com/digital-twins-the-iot-powered-sandboxes-behind-smart-manufacturing/#respond</comments> <dc:creator><![CDATA[Jason Hehman]]></dc:creator> <pubDate>Thu, 14 Dec 2023 20:50:07 +0000</pubDate> <category><![CDATA[AI/ML]]></category> <category><![CDATA[digital twins]]></category> <category><![CDATA[IoT]]></category> <category><![CDATA[smart manufacturing]]></category> <guid isPermaLink="false">https://www.clouddatainsights.com/?p=4726</guid> <description><![CDATA[Digital twins technology is a core enabler for better development and cost optimization in smart manufacturing and industry 4.0.]]></description> <content:encoded><![CDATA[<div class="wp-block-image"> <figure class="aligncenter size-full"><img fetchpriority="high" decoding="async" width="1000" height="667" src="https://www.clouddatainsights.com/wp-content/uploads/2023/11/Depositphotos_664846762_S.jpg" alt="" class="wp-image-4737" srcset="https://www.clouddatainsights.com/wp-content/uploads/2023/11/Depositphotos_664846762_S.jpg 1000w, https://www.clouddatainsights.com/wp-content/uploads/2023/11/Depositphotos_664846762_S-300x200.jpg 300w, https://www.clouddatainsights.com/wp-content/uploads/2023/11/Depositphotos_664846762_S-768x512.jpg 768w, https://www.clouddatainsights.com/wp-content/uploads/2023/11/Depositphotos_664846762_S-930x620.jpg 930w" sizes="(max-width: 1000px) 100vw, 1000px" /><figcaption class="wp-element-caption"><em>Digital twins technology is a core enabler for better development and cost optimization in smart manufacturing and industry 4.0.</em></figcaption></figure></div> <p>Industry 4.0 – the emerging future of manufacturing – combines software and IoT devices to support faster, smoother, and more cost-effective operations. Getting there, though, depends on tools that provide accurate, real-time insights about floor-level machinery. Also critical: a low-risk way to continually optimize performance and equipment health.</p> <p>Enter digital twins. They use IoT data to replicate physical machinery (like a robot or conveyor belt) in a virtual environment. And they’re the key to <a href="https://www.rtinsights.com/2024-automotive-smart-manufacturing-trends/">smart manufacturing</a>.</p> <p>Here, I’ll explain how digital twins work and how manufacturers can use them to improve efficiency and protect the bottom line.</p> <h3 class="wp-block-heading">How Real-Time IoT Data Powers Digital Twins</h3> <p>True <a href="https://www.rtinsights.com/manufacturers-embrace-digital-twins-for-enhanced-production/">digital twins</a> are more than static 3D simulations. They rely on real-time data that continuously flows between IoT-monitored equipment and engineers. Here’s what the process looks like:</p> <ul class="wp-block-list"> <li><strong>IoT sensors collect real-time data about equipment</strong>. This could include data about a machine’s position, electrical currents, rotational speed, noise emission, temperature, etc.</li> <li><strong>The data feeds into the manufacturer’s cloud storage system</strong>. Depending on the sensor hardware, data transmission can occur via WiFi, Bluetooth, cellular, or wide access network (WAN).</li> <li><strong>Engineers use cloud data to virtually replicate the monitored equipment</strong>. From here, engineers can manipulate this digital twin without actually being on the facility floor. If any changes need to occur to the physical equipment, they can remotely communicate that to nearby workers. </li> </ul> <p>As IoT sensors continue to improve their connectivity with cutting-edge technology (like 5G), they’ll be able to relay larger volumes of data at higher speeds than ever. That means manufacturers can maximize the accuracy and relevancy of every digital twin.</p> <p>It’s worth noting that although digital twins are often used to simulate equipment or machinery, they can also replicate whole manufacturing environments. For example, if a manufacturer is using connected data loggers to monitor a climate-controlled warehouse, workers can create a digital twin to simulate how sensitive products react to sudden fluctuations in temperature, pressure, or humidity.</p> <p>In the following sections, we’ll take a look at a few specific ways manufacturers can use digital twins to transform their facility operations.</p> <p><strong>See also:</strong> <a href="https://www.clouddatainsights.com/surging-growth-in-the-global-cloud-database-and-dbaas-market/">Surging Growth in the Global Cloud Database and DBaaS Market</a></p> <h3 class="wp-block-heading">Digital Twins Enable Smarter Decision Making</h3> <p>One of the biggest advantages to a digital twin is the ability to remotely track a machine’s operation in real time. And with the right software, AI can recommend adjustments that could extend a machine’s lifetime or improve its performance. </p> <p>Those two use cases – predictive maintenance and performance optimization – can pave the way for smart manufacturing that protects the bottom line. To illustrate, I’ll walk you through a scenario for each.</p> <h4 class="wp-block-heading">Use Case #1: Predictive Maintenance</h4> <p>Imagine an auto manufacturer that’s struggling to keep its automated conveyor belts from breaking down without warning. Engineers can use a series of digital twins to monitor the full conveyor belt system. Thanks to real-time data, they can pinpoint the moment a foreign object gets caught in a machine or a motor starts slowing down – and alert a worker on the floor before the system comes to a halt.</p> <p>What’s more, AI can use equipment data to, say, notify engineers when a motor has worn down past a certain threshold. It can even suggest an optimal maintenance schedule by comparing real-time and historical data.</p> <p>The bottom line: with the help of digital twins, manufacturers can slash equipment downtime and keep revenue flowing.</p> <h4 class="wp-block-heading">Use Case #2: Performance Optimization</h4> <p>Picture a food and beverage manufacturer that wants to improve the efficiency of its packaging robots. With digital twins, engineers can track each robot’s performance and identify specific components that could be slowing them down. </p> <p>Then, they can virtually test individual tweaks (like a motor upgrade) and simulate their effectiveness in custom production scenarios. They can even adjust environmental factors, like heat or humidity, to gauge the upgraded robot’s structural resilience. Once the engineers have identified the right changes, they can relay next steps to the right team.</p> <p>The benefit: digital twins let manufacturers explore ways to create new efficiencies without disrupting equipment on the floor.</p> <h3 class="wp-block-heading">Digital Twins Can Be a Product Development Sandbox</h3> <p>So far, we’ve looked at ways digital twins can help manufacturers optimize equipment within their facilities. But they’re also powerful tools for intelligently improving devices in production. </p> <p>Think of digital twins here as a product development sandbox: engineers can model a near-infinite number of product changes and operational scenarios in a matter of seconds. Most importantly, these changes happen without the need for costly physical rework.</p> <p>The simulation and testing process is similar to the ones we’ve already explored. The main difference: each model relies on a recent snapshot of IoT data about prototypes versus a true real-time stream. Even so, engineers get a highly accurate prototype simulation that they can virtually manipulate to boost its performance. It’s a low-risk way to test, fail, learn, and create better products.</p> <h3 class="wp-block-heading">Don’t Wait to Invest in Digital Twins</h3> <p>Digital twins are a core enabler for Industry 4.0. But the truth is that many firms are already watching this space. The proof: digital twin investments <a href="https://www.mckinsey.com/industries/industrials-and-electronics/our-insights/digital-twins-the-key-to-smart-product-development">are set to reach $73.5 billion by 2027</a>.</p> <p>It’s clear that manufacturers using digital twins will gain a leg up in this next phase of industrial innovation. Invest now or risk falling behind. </p> <div class="saboxplugin-wrap" itemtype="http://schema.org/Person" itemscope itemprop="author"><div class="saboxplugin-tab"><div class="saboxplugin-gravatar"><img alt='Jason Hehman' src='https://secure.gravatar.com/avatar/8260f2dda367a977ff4db8d5054f256d?s=100&d=mm&r=g' srcset='https://secure.gravatar.com/avatar/8260f2dda367a977ff4db8d5054f256d?s=200&d=mm&r=g 2x' class='avatar avatar-100 photo' height='100' width='100' itemprop="image"/></div><div class="saboxplugin-authorname"><a href="https://www.clouddatainsights.com/author/jason-hehman/" class="vcard author" rel="author"><span class="fn">Jason Hehman</span></a></div><div class="saboxplugin-desc"><div itemprop="description"><p><em>Jason Hehman is a Client Partner at </em><strong><em><a href="https://txidigital.com/">TXI</a></em></strong><em> and the Vertical Lead for Industrial Innovation and Industry 4.0 at TXI. He helps clients come up with innovative solutions to their most difficult challenges by building integrated teams of their own, transforming company cultures, and supporting new products and services that optimize the ways they and their customers do business.</em></p> </div></div><div class="clearfix"></div></div></div>]]></content:encoded> <wfw:commentRss>https://www.clouddatainsights.com/digital-twins-the-iot-powered-sandboxes-behind-smart-manufacturing/feed/</wfw:commentRss> <slash:comments>0</slash:comments> <post-id xmlns="com-wordpress:feed-additions:1">4726</post-id> </item> <item> <title>How Data Masking Helps Ensure IoT Data Security</title> <link>https://www.clouddatainsights.com/how-data-masking-helps-ensure-iot-data-security/</link> <comments>https://www.clouddatainsights.com/how-data-masking-helps-ensure-iot-data-security/#respond</comments> <dc:creator><![CDATA[Elizabeth Wallace]]></dc:creator> <pubDate>Wed, 08 Nov 2023 13:22:41 +0000</pubDate> <category><![CDATA[Security]]></category> <category><![CDATA[data masking]]></category> <category><![CDATA[IoT]]></category> <guid isPermaLink="false">https://www.clouddatainsights.com/?p=4217</guid> <description><![CDATA[Data masking is one technique companies can use to balance IoT security needs with data usability. Read more to find out where it fits.]]></description> <content:encoded><![CDATA[<div class="wp-block-image"> <figure class="aligncenter size-full"><img decoding="async" width="1000" height="667" src="https://www.clouddatainsights.com/wp-content/uploads/2023/08/Depositphotos_175513896_S.jpg" alt="Data masking in IoT" class="wp-image-4218" srcset="https://www.clouddatainsights.com/wp-content/uploads/2023/08/Depositphotos_175513896_S.jpg 1000w, https://www.clouddatainsights.com/wp-content/uploads/2023/08/Depositphotos_175513896_S-300x200.jpg 300w, https://www.clouddatainsights.com/wp-content/uploads/2023/08/Depositphotos_175513896_S-768x512.jpg 768w, https://www.clouddatainsights.com/wp-content/uploads/2023/08/Depositphotos_175513896_S-930x620.jpg 930w" sizes="(max-width: 1000px) 100vw, 1000px" /><figcaption class="wp-element-caption"><em>Data masking is one technique companies can use to balance IoT security needs with data usability. Read more to find out where it fits.</em></figcaption></figure></div> <p>The adoption of the Internet of Things has created an interconnected digital landscape. This is great for companies because everything from delivering seamless customer experiences to managing vast distributed systems is easier. One thing that’s also easier? Getting access to sensitive data. Profound interconnectedness means devices continuously collect, transmit, and store data, much of which needs to remain confidential. Enter data masking — a technique that camouflages sensitive information while retaining its authenticity.</p> <p>Let’s explore the critical role of data masking in the world of IoT, its significance, and the promise it holds in safeguarding a connected future.</p> <p>See also: <a href="https://www.clouddatainsights.com/take-control-how-to-make-the-invisible-serverless-threat-landscape-visible/">How to Make the Invisible Serverless Threat Landscape Visible</a></p> <h3 class="wp-block-heading">Why is data masking an essential part of IoT?</h3> <p><a href="https://www.clouddatainsights.com/data-masking-methods-for-data-centric-security/">Data masking</a> is the process of disguising original data to protect sensitive information while maintaining the data’s authenticity and usability. This differs from other techniques.</p> <h4 class="wp-block-heading">Synthetic data versus data masking</h4> <p>Synthetic data is generated to simulate the patterns of real-world data without corresponding to any actual event or individual. Data masking alters real data to create a sanitized version for non-secure environments. Synthetic data can be excellent for preserving privacy, especially in situations like GDPR or HIPAA, because the data does not correspond to any real source; there’s nothing to steal. However, it is resource intensive to generate and requires sophisticated models and domain knowledge. </p> <h4 class="wp-block-heading">Data encryption versus data masking</h4> <p>Data encryption converts data into unreadable code, offering strong protection during data transmission. Data masking may not inherently secure the data during transmission and could be reverse engineered. However, data encryption comes with challenges, including performance overheads and potential inefficiencies on low-power devices. The IoT domain may trend towards a hybrid approach to safeguard across the data lifecycle.</p> <h4 class="wp-block-heading">Data anonymization versus data masking</h4> <p>Data anonymization removes any classified, sensitive, or personal information from datasets. However, this transformation can sometimes strip away critical insights and may hint at patterns if not done correctly. Data masking provides a realistic but non-sensitive environment by adjusting specific data entries. </p> <h3 class="wp-block-heading">Data masking is one solution to managing IoT data security</h3> <p>Data masking in the IoT realm is important in several key ways.</p> <ul class="wp-block-list"> <li><strong>Protecting sensitive information:</strong> IoT devices collect a lot of data. While some of this is data from shop floors or environment monitoring, some of it is also embedded into products used by the public. Masking this data ensures that leaked information remains non-identifiable and non-compromising even in a data breach.</li> <li><strong>Regulatory compliance:</strong> Companies using IoT as part of a product must comply with privacy laws and regulations like GDPR and CCPA. Masking is one strategy that helps companies remain compliant and avoid the legal ramifications of a breach.</li> <li><strong>Reducing data misuse:</strong> Even if there isn’t a data breach, not everyone in the organization should have access to raw, unmasked data. Masking can help organizations ensure that only the right internal users can only the data they need to perform their tasks.</li> <li><strong>Protecting device integrity:</strong> IoT devices are a popular target for cybersecurity breaches. By masking data, the attacker may not be able to obtain meaningful or exploitable information even if the device is compromised. </li> <li><strong>Maintaining utility:</strong> Unlike removal, encryption, or even anonymization, data-making can maintain the structure and usability of the data. Developers, testers, and analysts can still work with it effectively without exposing sensitive information.</li> </ul> <h3 class="wp-block-heading">How data masking can help address IoT security challenges</h3> <p>Data masking is one viable solution to help address several challenges inherent in IoT ecosystems. </p> <ul class="nv-cv-m wp-block-list"> <li><strong>Diversity and fragmentation:</strong> Thanks to data diversity, a one-size-fits-all security solution is infeasible for IoT. However, data masking could offer a <a href="https://www.clouddatainsights.com/reduce-risks-how-to-protect-data-from-the-edge-to-the-cloud/">broader approach</a> to protect sensitive data across various devices regardless of the specific use case.</li> <li><strong>Limited resources:</strong> Masking modifies the data before it’s even stored or processed. It could serve as one lightweight security layer that demands less computational power than some other strategies.</li> <li><strong>Long device lifetimes:</strong> Masked data offers a long-term protection approach. Even if vulnerabilities arise in the future due to a lack of updates or security patches, data itself is obscured and less useful to potential threat actors.</li> <li><strong>Supply chain and development risks:</strong> Masking data at the source (as it enters the device) reduces the risk of exposure, even if other vulnerabilities exist in the device’s components.</li> </ul> <h3 class="wp-block-heading">Some data masking best practices</h3> <p>Implementing data masking effectively requires a combination of strategies and best practices. Here are some data masking best practices to ensure optimal data security:</p> <ul class="nv-cv-m wp-block-list"> <li><strong>Understand sensitive data:</strong> Before implementation, conduct a thorough assessment to identify which data is sensitive. Understand the regulations related to your industry specifically.</li> <li><strong>Apply consistent masking across systems:</strong> Ensure the same rules are applied consistently across your databases and systems. This helps ensure relationships between datasets remain intact.</li> <li><strong>Preserve the format:</strong> Keeping masked data in a similar form to the original data ensures that future processes can interact with the masked data correctly. Focusing on usability ensures developers can still work with it for future projects.</li> <li><strong>Irreversibility:</strong> Ensure that masking is impossible to reverse to prevent threat actors from accessing information after a breach.</li> <li><strong>Audit and monitor:</strong> Regularly determine who accessed the data (both masked and raw) and for what purpose. Ensure your masking techniques are current best practices and remain effective.</li> <li><strong>Automate where possible:</strong> Automation reduces human error and helps companies apply masking principles consistently across the IoT ecosystem.</li> <li><strong>Document the process:</strong> comprehensive documentation of data masking procedures and rules aids in transparency, future adjustments, and troubleshooting.</li> </ul> <h3 class="wp-block-heading">Balance IoT data utility and security with data masking</h3> <p>Data masking can bridge the gap between the need for data protection and the requirement for usability. By adhering to data masking best practices, organizations can bolster defenses against potential breaches and misuse but still harness the full utility of their data sets. </p> <div class="saboxplugin-wrap" itemtype="http://schema.org/Person" itemscope itemprop="author"><div class="saboxplugin-tab"><div class="saboxplugin-gravatar"><img loading="lazy" decoding="async" src="https://www.clouddatainsights.com/wp-content/uploads/2022/05/Elizabeth-Wallace-RTInsights-141x150-1.jpg" width="100" height="100" alt="" itemprop="image"></div><div class="saboxplugin-authorname"><a href="https://www.clouddatainsights.com/author/elizabeth-wallace/" class="vcard author" rel="author"><span class="fn">Elizabeth Wallace</span></a></div><div class="saboxplugin-desc"><div itemprop="description"><p>Elizabeth Wallace is a Nashville-based freelance writer with a soft spot for data science and AI and a background in linguistics. She spent 13 years teaching language in higher ed and now helps startups and other organizations explain – clearly – what it is they do.</p> </div></div><div class="clearfix"></div></div></div>]]></content:encoded> <wfw:commentRss>https://www.clouddatainsights.com/how-data-masking-helps-ensure-iot-data-security/feed/</wfw:commentRss> <slash:comments>0</slash:comments> <post-id xmlns="com-wordpress:feed-additions:1">4217</post-id> </item> <item> <title>Reduce Risks: How to Protect Data from the Edge to the Cloud</title> <link>https://www.clouddatainsights.com/reduce-risks-how-to-protect-data-from-the-edge-to-the-cloud/</link> <comments>https://www.clouddatainsights.com/reduce-risks-how-to-protect-data-from-the-edge-to-the-cloud/#respond</comments> <dc:creator><![CDATA[Julian Durand]]></dc:creator> <pubDate>Tue, 06 Sep 2022 13:29:57 +0000</pubDate> <category><![CDATA[Security]]></category> <category><![CDATA[edge]]></category> <category><![CDATA[IoT]]></category> <guid isPermaLink="false">https://www.clouddatainsights.com/?p=1751</guid> <description><![CDATA[Data needs to be persistently protected as it goes from edge devices to a number of interim devices, then to the cloud and back. ]]></description> <content:encoded><![CDATA[<div class="wp-block-image"> <figure class="aligncenter size-full is-resized"><img loading="lazy" decoding="async" src="https://www.clouddatainsights.com/wp-content/uploads/2022/09/edge-security-Depositphotos_324846782_S.jpg" alt="" class="wp-image-1752" width="750" height="500" srcset="https://www.clouddatainsights.com/wp-content/uploads/2022/09/edge-security-Depositphotos_324846782_S.jpg 1000w, https://www.clouddatainsights.com/wp-content/uploads/2022/09/edge-security-Depositphotos_324846782_S-300x200.jpg 300w, https://www.clouddatainsights.com/wp-content/uploads/2022/09/edge-security-Depositphotos_324846782_S-768x512.jpg 768w, https://www.clouddatainsights.com/wp-content/uploads/2022/09/edge-security-Depositphotos_324846782_S-930x620.jpg 930w" sizes="(max-width: 750px) 100vw, 750px" /><figcaption>Data needs to be persistently protected as it goes from edge devices to a number of interim devices, then to the cloud and back.</figcaption></figure></div> <p>A combination of increasingly powerful semiconductor technology and networking innovation led by 5G promises an upcoming renaissance in AI and edge computing. These technologies will allow companies to process an increasing amount of their data in devices at the edge of their networks. That being said, the cloud is not going away. For trust and security professionals, this trend means they need to deal with an even more complicated environment as they will have to ensure that data remains trusted and secure throughout a greatly expanded data ecosystem. And, just to add to the pressure, with IoT and data becoming more and more intertwined with critical infrastructure, their job is more important than ever.</p> <p>Given the expected exponential growth in the number of edge devices where sensitive data can reside and be processed, maintaining the flow of trusted data is not a simple process. As an example, let’s look at a refrigerated delivery truck. The truck can have a number of systems that can collect and process data, such as the battery and electric drivetrain, the refrigeration system, the delivery tracking system, and apps on the driver’s personal device. In addition to the vehicle-mounted sensors, the refrigeration system itself will typically have multiple temperature and humidity sensors positioned throughout the trailer. All of these will gather and transmit large amounts of data, quite often in real time. </p> <p>As the truck travels through its route, each of these systems will be sending and receiving data through any number of networks, ranging from the Bluetooth connection on the driver’s phone, to the self-organizing temperature sensor mesh, to the Wi-Fi network at the truck yard, to the cellular and satellite networks the truck connects to on the road. As Vehicle-to-Vehicle and Vehicle-to-Infrastructure (V2X) communications become more common, our truck will be connecting to these for more critical safety functions.</p> <p>Each of these devices and network configurations has its own characteristics that a security architect needs to take into consideration. In addition, the data needs to be secured when it reaches and is processed in the cloud. Let’s also not forget about protecting the data as it is sent back to the systems in the truck from the cloud. Multiply this by the number of trucks in a fleet, and you can see why edge security is a conundrum.</p> <p><strong>See also: </strong><a href="https://www.clouddatainsights.com/cloud-security-a-primer/" target="_blank" rel="noreferrer noopener">Cloud Security: A Primer</a></p> <p>You also need to consider the speed at which cyber attacks can affect something. In the past, the effects of many attacks may not have been immediately felt. An attack in our refrigerated truck example could put the life of the driver and those around her in immediate danger.</p> <p>While the edge is now a very exciting place for computing technology, not all edge networks are equal when it comes to security. In many cases, network security currently in place is inadequate. And since the Internet is a network of networks, the security supporting trusted data flows is only as strong as the weakest network link. The threat is very real. Weak network security often has gaps in it that can be exploited, and devices can be compromised with disastrous consequences. For example, hackers were able to obtain sensitive data from a Las Vegas casino through <a href="https://www.washingtonpost.com/news/innovations/wp/2017/07/21/how-a-fish-tank-helped-hack-a-casino/">an Internet-connected fish tank</a>. Also, hackers can take advantage of leaked data to imitate a trusted connection and issue spoofed orders to devices.</p> <p>One major issue with widely used edge network security protocols such as <a href="https://en.wikipedia.org/wiki/Transport_Layer_Security">TLS</a> or <a href="https://en.wikipedia.org/wiki/Virtual_private_network">VPNs</a> is that they only protect data from one network endpoint to another. Once the data leaves the network endpoint, it is dependent on the security of the device it’s now traversing. This could be an IoT gateway many of which are known to have inadequate security and could be yet another weak point for a hacker to exploit.</p> <p>Adopting a <a href="https://en.wikipedia.org/wiki/Zero_trust_security_model">zero-trust network architecture</a> is a well-regarded approach to this issue, but the architecture needs to be designed to persistently protect the data throughout its lifecycle. This means not only from a device to the cloud but also from the cloud to the device. It also means protecting data while at rest and in use, as data can be exposed to a hostile environment as it exits a TLS or VPN tunnel. Companies need to invest in security systems that persistently protect data across any and all gaps that they may encounter in their journey in order to make the next generation of IoT technology safe, scalable, and reliable.</p> <p>Taking these sorts of measures is even more important since companies now rely more than ever on data to do such things as reduce operational costs, enhance user experience, support their customers, and create new products and services. A successful cyberattack can be very costly to the corporate bottom line. According to a 2022 Ponemon Institute study, between March 2021 and March 2022, the average total cost of a data breach was $4.35 million, an all-time high.</p> <p>Nevertheless, this just reflects the direct costs of a data breach, such as response costs, data migration, and regulatory fines. When data is stolen or hacked, and systems are made to malfunction, the impacts to a company can be enormous. It could mean loss of productivity as well as revenue from day-to-do day operations as well as short, medium, or long-term productivity losses. Other implications include loss of competitive advantage, reputational damage, and reduction in the value of assets. If the cyberattack affects OT systems, such as those involved in energy infrastructure, it could cause catastrophic failures that would rise to the level of a national security issue.</p> <p>As mentioned before, data needs to be persistently protected as it goes from edge devices to a number of interim devices, then to the cloud and back. One promising approach doesn’t involve new, untested technologies. Instead, it relies on technologies with a sound track record that is well understood in the IT industry. One of these is <a href="https://en.wikipedia.org/wiki/Public_key_infrastructure">PKI</a>-based digital signatures. Data packets can be digitally signed (and optionally encrypted) at the device when they are created. When doing this though, it is important that the device itself includes a protected processing environment to protect the security of the device’s software stack. The digital signature would not only indicate the makeup of the data packet, but it would also include metadata such as the known secure state of the device and the time the data was created.</p> <p>The data packet then makes its way over networks and devices – both trusted and untrusted – to a cloud server. This cloud server would have knowledge of the digital signatures of signed packets. When the server receives the packet, it would then compare the digital signature of the packet it received with the digital signature of the packet when it was created. The server would then confirm that the data can be trusted and (if encrypted), decrypt it and direct it to the proper cloud data repository.</p> <p>The data still needs to be protected on the cloud, though. Modern cloud data systems are usually well maintained by security experts, but the algorithms that companies use are often licensed from third-party developers. While most can be trusted, since in many ways the algorithms represent a “black box,” it’s prudent to run algorithms in cloud “sandboxes” where the input and output of algorithms are strictly controlled to avoid unauthorized access to data.</p> <p>Maintaining trust in data through its journey is critical to corporate operations in today’s digital world. With the judicious use of well-understood data security technologies and techniques, it’s not an impossible goal.</p> <div class="saboxplugin-wrap" itemtype="http://schema.org/Person" itemscope itemprop="author"><div class="saboxplugin-tab"><div class="saboxplugin-gravatar"><img loading="lazy" decoding="async" src="https://www.clouddatainsights.com/wp-content/uploads/2022/09/Julian-Durand.png" width="100" height="100" alt="" itemprop="image"></div><div class="saboxplugin-authorname"><a href="https://www.clouddatainsights.com/author/julian-durand/" class="vcard author" rel="author"><span class="fn">Julian Durand</span></a></div><div class="saboxplugin-desc"><div itemprop="description"><p><strong><a href="https://www.linkedin.com/in/juliandurand/">Julian Durand</a></strong> is Vice President of Product Management at <strong><a href="https://www.intertrust.com/">Intertrust</a></strong>. Julian is an accomplished product owner, team leader, and creative inventor with more than 25 years of success in bringing breakthrough products to market at a massive scale. He is a named inventor in Digital Rights Management (DRM), Internet of Things (IoT), and virtual SIM technologies, was the technical lead for the first music phone, and pioneered vSIM and IoT businesses at Qualcomm. Julian has also productized SaaS and PaaS offerings in construction telematics, real-time child tracking, and cyber risk data analytics and is currently a CISSP (Certified Information System Security Professional). He is also a contributing member of the Forbes Council.</p> </div></div><div class="clearfix"></div></div></div>]]></content:encoded> <wfw:commentRss>https://www.clouddatainsights.com/reduce-risks-how-to-protect-data-from-the-edge-to-the-cloud/feed/</wfw:commentRss> <slash:comments>0</slash:comments> <post-id xmlns="com-wordpress:feed-additions:1">1751</post-id> </item> </channel> </rss>