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Industrial IoT, IoT Security

Article | July 11, 2023

Manufacturers were already digitizing their processes before March 2020. The COVID-19 pandemic gave IT and operational professionals in the manufacturing space reasons to want to move faster. Teams that can’t work on the factory floor (pandemic, weather, closed roads, etc.) need a way to monitor and control processes over the network. Supply chain woes—like wildly fluctuating demand and the container ship that blocked the Suez Canal—highlighted the need for agility. A skilled labor shortage has further accelerated plans for automation. Digitization brings visibility and agility The fourth industrial revolution, also known as Industry 4.0, lays the foundation of modern digital manufacturing. It brings together cyber and physical systems, automation, industrial IoT, and better vertical and horizontal integration. The network has a starring role in digital manufacturing, connecting people and applications in any location to factory-floor assets like sensors, actuators, cameras, and industrial automation and control systems (IACS). Benefits of digitization include improved overall equipment effectiveness (OEE) uptime, product quality, worker safety, cybersecurity, 24/7 asset monitoring and faster new product introduction and accelerating plant buildouts. Four essentials for manufacturing networks As IT and operational professionals work to innovate traditional manufacturing facilities and operations, we must consider that digital manufacturing requires more networks. Here are guidelines for making sure your manufacturing network is up to the task. Use network devices specifically designed for industrial environments like factories In addition to high performance and reliability, industrial routers, switches, and firewalls need to withstand harsh environmental conditions like extreme temperatures, shock, vibration, and humidity. They also need to be able to control access, have support for real-time industrial protocols, and enable the flow of key operational data to move across applications in the cloud. Further, the operational networks they build need to be scalable and highly resilient. We designed our industrial routers and switches to meet these requirements. Give IT and OT visibility and control into what they care about The manufacturing network is a joint project of the IT and OT teams. If you’re on the IT team, you want a solution that works with your existing network management and security applications, and doesn’t require significant training or disruption. You want to automate network maintenance and quickly identify and solve performance issues, especially in this business-critical space. If you’re on the OT team, you’re probably not an IT expert. You want visibility of issues that impact availability, product quality, workforce effectiveness and straightforward recommendations to resolve them. Cisco DNA Center – proven in the largest IT networks – meets all these needs. It automates time-consuming manual tasks, continuously monitors network health, and provides reports and controls on an easy-to-use dashboard. Cisco Cyber Vision gives you visibility into assets and processes. For agile manufacturing, look for “plug-and-play” deployment Manufacturers are simultaneously expanding production, hyper-customizing products, improving operations, and launching new products and services. To achieve these goals, you need the agility to scale product capacity, change product mix, and reallocate resources as needed. Quickly shift networking and production resources where you need them using Cisco DNA Center’s plug-and-play onboarding and provisioning. Pay careful attention to cybersecurity Cybersecurity starts with knowing everything that is connected to your industrial network, who’s talking to each other and what they are saying. Cisco Cyber Vision automatically takes a complete inventory. OT teams use a graphical interface to create production zones (aka network segments) containing all assets that need to communicate. (The painting controller doesn’t need to talk to the assembly-line controller.) Cisco Identity Services Engine (ISE) deploys polices that block unintended communications between segments to keep malware infections from spreading. Cisco Cyber Vision also takes a baseline of each asset’s usual communications patterns, alerting OT and IT teams to unusual behavior that could be a sign of a security breach. Prepare to do more with less The manufacturing skills shortage has widened the skills gap, with fewer experts left on the plant floor to prevent mistakes and solve crises. Connecting your plant floor helps you do more with less. A resilient network with the four qualities I’ve described—rugged devices, IT and OT collaboration, simpler and agile network management, and cybersecurity—helps you proactively identify potential problems, discover the cause, and resolve them before they affect production or quality.

IoT Security

Article | June 28, 2023

5G trends are shaping the future of various technologies, from the Internet of Things to virtual reality. Learn more about the top trends in 5G to stay ahead of the competition in this sector. Contents 1 The Current State of IoT Data Security 2 Top Trends in IoT Data Security in 2023 2.1 Emergence of AI-powered Security Solutions 2.2 Potential of Blockchain Technology 2.3 Growing Use of Zero-trust Security Frameworks 2.4 Greater Emphasis on End-to-end Encryption 2.5 Industry and Government Collaboration 3 Conclusion As the Internet of Things (IoT) continues to rapidly expand, data security has become a critical concern for businesses and consumers alike. With recent high-profile breaches and cyberattacks, the latest trends in IoT data security focus on implementing stronger encryption and authentication protocols, as well as enhancing device-level security measures to protect sensitive data from potential threats. 1 The Current State of IoT Data Security The growing adoption of IoT has led to a digital transformation in the way businesses operate. IoT technology has enabled organizations to collect and analyze vast amounts of data in real-time, allowing for improved decision-making, increased operational efficiency, and enhanced customer experiences. Despite these benefits, organizations are currently facing significant IoT data security challenges that must be addressed to fully realize the potential of this technology. Companies recognize unauthorized access (43%), data privacy (38%), and data integrity (31%) as top IoT security challenges. (Source: Statista) Businesses are actively addressing these security challenges by investing in IoT and data security solutions. The global market for IoT data security continues to grow, and companies are increasingly investing in strategies for data security in the IoT. To secure access to mission-critical connected devices and sensitive data, it is imperative for businesses to keep track of IoT trends in data security. 2 Top Trends in IoT Data Security in 2023 2.1 Emergence of AI-powered Security Solutions AI-powered security systems can rapidly detect and respond to attacks, reducing the likelihood of significant damage to IoT devices or networks. In particular, its ability to analyze vast amounts of data in real-time and identify anomalies or potential security threats makes AI a vital component of an IoT data security strategy. Detecting an IoT security breach in progress is possible with AI security systems, which identifies unusual behavior by analyzing data patterns from IoT devices. AI can also be used to diagnose potential vulnerabilities in IoT devices and networks, allowing organizations to take proactive measures to address them before they are exploited. The pattern recognition capabilities of AI also help secure IoT technology through predictive analytics. By analyzing past data breaches and attacks, AI systems detect potential cyberattacks and develop predictive models to detect and respond to them proactively. AI-driven security systems have the potential to streamline incident response by lessening the load on cybersecurity teams and reducing response time. The ability to adapt and learn from a previous cyberattack allows machine learning (ML) algorithms to create novel strategies that prevent similar attacks in the future. AI represents a significant development in addressing IoT security concerns since it provides sophisticated capabilities to protect IoT networks and devices that conventional security measures cannot provide. AI-enabled security systems deliver immediate identification, reaction, and deterrence of possible threats, which is why they will be critical in ensuring data security in the IoT. 2.2 Potential of Blockchain Technology Blockchain's unique features, such as decentralization, immutability, and cryptographic security, provide a robust framework for secure communication and data sharing among IoT devices. By leveraging blockchain technology, businesses can ensure their IoT data's integrity, confidentiality, and authenticity. One of the key advantages of using blockchain for IoT data security is its decentralized nature. Blockchain networks are distributed and run on a peer-to-peer basis, making it difficult for attackers to compromise the network. This also makes it an ideal solution for recording and securing data from multiple access points, such as IIoT systems. Additionally, blockchain networks are designed to be immutable, making them an ideal solution for IoT data security and providing a tamper-proof and transparent ledger for recording data flow. This can help enterprises identify and mitigate security threats more quickly and efficiently, reducing the risk of cybersecurity incidents. A research paper published in Wireless Networks highlights the advantage of using a Blowfish Blockchain Model to enable IoT data sharing security, particularly for multimedia content. Blockchain technology is a promising solution for securing IoT data. Its unique features, including decentralization, immutability, and cryptographic security, make it an ideal candidate for many IoT use cases. This technology can potentially transform data security for IoT devices by offering the IoT sector the solution it requires. 2.3 Growing Use of Zero-trust Security Frameworks Zero-trust frameworks ensure that only authorized devices and users can access sensitive data and systems, protecting against insider threats and external attacks. This is especially important in IoT environments, where devices may lack traditional security measures like firewalls and antivirus software. Device identity management is a critical component of zero-trust security for IoT data. Only recognized devices are allowed access to a network or data by leveraging processes and technologies that authenticate device identity. With Zero Trust, any connected device must be authorized before accessing any resources, including data. By closely monitoring and managing access, businesses can maintain the security of the IoT. This protects against threats that exploit weak device identity management. Overall, zero-trust security frameworks are essential for safeguarding IoT data from malicious actors and protecting the integrity of IoT ecosystems. 2.4 Greater Emphasis on End-to-end Encryption IoT poses a threat to data security when users do not take proper measures to protect the data generated. End-to-end encryption provides a strong layer of protection against unauthorized access, interception, and other cyber threats by encrypting data at the source, during transmission, and at rest. IoT devices collect and process a wide range of sensitive data, from personal information and financial data to critical infrastructure and medical records. This data is often transmitted over networks and shared with cloud services, and the risk of cyberattacks during transmission cannot be ignored. End-to-end encryption can provide a strong layer of protection by encrypting data at the source, working to improve the limited data security of the IoT. As the use of IoT devices continues to grow, implementing end-to-end encryption will become increasingly important for ensuring the security and privacy of sensitive IoT data. 2.5 Industry and Government Collaboration In late 2021, the UK and Singapore governments became the first to announce obligatory security requirements for specific categories of IoT devices. Due to IoT data security risks, other countries have also defined guidelines, best practices, certifications, or labeling efforts for IoT devices. However, adoption among IoT device makers and vendors has been slow. The National Institute of Standards and Technology (NIST) has been working on establishing cybersecurity guidelines for IoT devices. In June 2022, NIST incorporated consumer IoT cybersecurity criteria into its family of IoT cybersecurity guidance. NIST is also working with the IoT industry to design, standardize, and test solutions for IoT security controls. By discussing IoT device security concepts and establishing guidelines in collaboration, the industry and the government can foster adoption of general methods to protect IoT devices from cybersecurity breaches. Such cooperation can be crucial in ensuring that IoT devices are secure from cyber threats and that IoT device makers and vendors adopt best practices for IoT device security. 3 Conclusion The trends in IoT data security showcase several proactive measures that can be taken to protect sensitive data in a rapidly evolving technological landscape. In addition, organizations are moving towards a more comprehensive approach to IoT data security with the emergence of AI-powered security solutions, blockchain technology, and the shift to zero-trust security frameworks. As IoT devices continue to proliferate, organizations must prioritize security and data protection to prevent data breaches and cyberattacks. This emphasizes the need for collaboration between industry and government to strengthen security measures and improve IoT device security by building with a ‘secure by design’ approach.

Enterprise Iot

Article | May 11, 2023

IoT has undeniably become the massive growth propellant for modern-day business. Enterprises employ intelligent systems to improve production in factories, and reduce costs, build industrial automation systems to replace human assignments, monitor and reduce energy; and develop autonomous transportation to enhance driver safety. Inside these embedded systems are sensors that rapidly transmit data that must be immediately captured, processed, and acted upon. Traditional embedded database solutions don't understand and meet the complex needs of IoT devices when it comes to processing and managing data. IoT edge database solutions that can understand the constant data stream from sensors enable devices to make crucial decisions in milliseconds. Real-time Edge Data Processing Enterprisers and business owners prefer scalable edge data management solutions to deploy hundreds of IoT devices so that each device can manage, collect, and analyze the massive amounts of data these IoT sensors produce without losing performance. These devices must capture and store critical information so that the IoT node can make independent decisions and trigger appropriate reactions. Database queries allow device apps to get the information they need to make intelligent decisions in real-time, quickly and without wasting time. To be successful in the IoT, you need the right data management software and the ability to quickly collect and connect device data rapidly to get low latency. IoT Data Processing and Management Standard data management solutions do not fully address the complexity of architecting software for IoT data processing. Despite being the primary data source, sensors are often constrained by their limitations and fail to provide sophisticated analysis. The focus of IoT data analysis and management is to harvest real-time information and make sense of it quickly. A good solution uses technologies that many developers are already familiar with, like SQL, to solve the new problem of analyzing IoT sensors directly on edge devices. Conclusion While building a device application, at every stage, developers must make tough calls to select the best data management and database software to launch their edge-centric IoT systems. Such costly decisions consume significant development and validation time as well. Using existing IoT data management platforms is a better way to deal with scaling, security, and the weight of data. Businesses can set up, connect, and grow their IoT infrastructure with these platforms. Organizations don't have to build their own IoT infrastructure from scratch. Instead, they can use IoT platforms that give them access to IoT devices, cloud infrastructure, and networks worldwide. Small and medium-sized businesses may find this method saves money.

Article | April 14, 2020

The survey data I’m referring to comes from a study conducted by the Eclipse Foundation about the adoption of commercial Internet of Things (IoT) technology. The aim of the study was to get a better understanding of the IoT industry landscape by identifying the requirements, priorities, and challenges faced by organizations deploying and using commercial IoT technologies. More than 350 respondents from multiple industries responded, with about a quarter of respondents coming from industrial production businesses. While this survey was not solely focused on the manufacturing and processing industries, its results reflect the general business community’s IoT adoption at the end of 2019. As such, it is a pre-COVID-19 snapshot of IoT adoption.