IoT Security
Article | June 27, 2023
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.
Read More
IoT Security
Article | July 5, 2023
Artificial intelligence (AI) has already made headway into becoming a general-purpose technology vastly impacting economies. Yet, the interpretation and estimated trajectory for something remotely close to what we call AI now was first explored in the 1950s.
Until this very day, AI keeps on evolving further. Though let’s face it, AI would have been useless without data. With around 2.5 quintillion bytes of data being generated every day, the numbers will shoot up as the Internet of Things (IoT) enters the game.
Let’s see what this is all about and where and how exactly IoT crosses paths with AI applications.
IoT fundamentals: Where does IoT meet AI
The benefits of IoT in AI
Challenges of IoT in AI
Why implement machine learning in IoT
IoT applications for AI
Key takeaways
IoT fundamentals: Where does IoT meet AI?
What is meant by the term internet of things (IoT) is essentially a system of correlated digital and mechanical appliances, computing devices, and sensors embedded often into everyday objects that transfer data over a network. IoT connects the internet to any and every physical thing or place in the world.
Modern IoT has advanced from the mere merging of microelectromechanical systems to wireless technologies, and faster data transfer through the internet. This resulted in a confluence of information technology and artificial intelligence, allowing unstructured machine-generated data to be evaluated for insights that could lead to new developments.
More and more industries are now referring to IoT to function more proficiently, provide better customer service, escalate the significance of their business, and implement robust decision-making.
Machine learning for IoT can be used to identify anomalies, predict emerging trends, and expand intelligence through the consumption of audio, videos, and images. The implication of machine learning in IoT can substitute manual processes and offer automated systems using statistically backed up actions in critical processes.
The benefits of IoT in AI and real life
IoT offers the following benefits to AI applications:
IoT data for business purposes
Cost and time savings
Task automation and reduction of human intervention
Higher quality of life
IoT data for business purposes
IoT can also be viewed as a data pool. That means by aggregating IoT data, one can extract useful data-driven feedback, which in turn (used properly) may foster effective decision-making. Businesses can also identify new market opportunities, not because of IoT itself but by using the data IoT provides. And since IoT offers companies access to more data, and hence advanced analytics of that data, its usage can eventually result in improved customer outcomes and enhanced service delivery.
Cost and time savings
When devices get connected, cost reductions come along with it. The gathering of different data allows for advances in efficiency, and it leads to money surplus and low-cost materials.
Task automation and reduction of human intervention
Nowadays, devices that are internet-connected can be found in every aspect of our lives, and it is safe to say that they make tasks easier. These automation features range from real-time AI-powered chatbots to home automation control systems, and all of it usually takes a click of a button.
For businesses offering AI-enabled solutions, similar advancements can be achieved with pipeline automation too. That includes significant cuts in annotation and QA time. By leveraging SuperAnnotate’s platform, hundreds of companies recorded faster task completion and more accuracy in prediction results.
Higher quality of life
IoT is not only beneficial in the business aspects but it also creates better living circumstances for us. Smart cities and agriculture, intelligent homes, and food waste solutions are some of the most common ways of IoT providing better, more sustainable living conditions for people.
Challenges of IoT in AI
Despite the numerous benefits and advancements that IoT brings to the table, there have been a few limitations with it. Some of them are listed below:
Privacy issues
Data overflow
Bug issues
Compatibility issues
Privacy issues
With the increased connection between multiple devices or their coexistence for model development purposes, more information is shared between them, which poses vulnerability to your data and makes room for caution. Added layers of protection are needed to prevent risks of data leaks and other threats.
Data overflow
Eventually, organizations will have to find a way to deal with the large numbers of IoT devices, and that will include the collection and systematic management of all the data from those IoT devices. The proper use of data lakes and warehouses, close governance, and intuitive arrangement of datasets will become an utmost priority.
Join hundreds of leading companies who build super high-quality training data up to 5x faster using SuperAnnotate’s intuitive data curation and robust project management features.
Bug issues
If one IoT device has a bug in its system, there is a large chance that every other connected device will also have it.
Compatibility issues
Because there are no international standards of compatibility for IoT, it's harder for different devices to communicate with one another.
Why implement machine learning in IoT
More and more companies are combining IoT with machine learning projects so they can achieve analytical skills on a large variety of use cases which allows their businesses to have access to fresh insights and adopt innovative automation. By implementing machine learning for IoT, they can leverage the following:
Convert data into a coherent format
Arrange the machine learning model on device, edge, and cloud
Enable use of data on edge devices directly for complex decision making
IoT applications for AI
Although we have covered the basics of IoT, its implications for AI are not as simple. Many corporations are adopting IoT which allows them to have an advanced approach to growing and advancing their business. Novel IoT applications are offering organizations the ability to plan and implement more vigorous risk management strategies. Some of the more common uses of IoT in AI encompass the following:
Transport logistics
Not only does IoT expand the material flow systems in transport logistics, but it also improves the automatic identification and global positioning of freight. It also increases energy efficiency and consequently declines the consumption of energy.
Smart cities
Although the term smart city is still incomplete, it mainly refers to an urban area that endorses sustainable enlargement and high quality of life. Giffinger et al.’s model explains the features of a smart city, including the people, the government, the economy, and lifestyle.
E-health control
The two main objectives of future health care are e-health control and prevention. People nowadays can choose to be monitored by physicians even if they do not live in the same country or place. Tracing and monitoring peoples’ health history makes IoT-assisted e-health extremely useful. IoT healthcare solutions could also benefit the specialists, as they can collect information to advance their medical calculations.
Key takeaways
Ever since its development, IoT, especially AI-enabled IoT, as discussed, has been enhancing our daily lives and directing us to work smarter while having complete control over the process. Besides having smart appliances to elevate homes, IoT devices can also be essential for providing insights and an actual look for businesses into their systems. Heading forward, IoT will continue to develop as more organizations get to understand its potential usage and tangible benefits.
Read More
IoT Security
Article | June 28, 2023
Explore the IoT tools for security and maintenance. These IoT monitoring tools addresses cyber security and privacy issues, catering to a various users including industrialists & individuals.
With the proliferation of interconnected devices in the Internet of Things ecosystem, ensuring robust security measures has become crucial to protect against cyber threats. The complexity and diversity of IoT systems pose unique challenges, making thorough security testing an essential practice. To address these challenges, various IoT development tools have emerged that enable organizations to assess and mitigate vulnerabilities in their IoT deployments. In this article, explore the top ten tools to secure IoT and IoT testing, equipping professionals and organizations with the means to identify and address potential security weaknesses, thus bolstering the overall security posture of their IoT infrastructure.
1. AWS IoT Device Defender
AWS IoT Device Defender is one of the security IoT management tools, designed to protect and manage IoT devices and fleets. Its auditing capabilities and continuous monitoring enable users to assess their IoT resources' security posture, identify vulnerabilities, and address potential gaps. By leveraging machine learning models or defining custom device behaviors, it can monitor and detect malicious activities, such as traffic from suspicious IP addresses or unusual connection attempts. The tool provides security alerts for failed audits or behavior anomalies, allowing users to mitigate potential risks quickly. Built-in actions facilitate security issue resolution, including device certificate updates, quarantine, and policy replacements. AWS IoT Device Defender offers automation for security assessments, identification of attack vectors, analysis of historical device behavior, and alarm notifications through various AWS interfaces.
2. Dynamic Application Security Testing
Appknox offers two robust mobile application security solutions: Automated Dynamic Application Security Testing (DAST) and Penetration Testing (PT). With Automated DAST, users can assess the security of their mobile apps in real time while running in their operational environment. The solution provides access to real devices, allowing users to replicate real-life interactions and identify security vulnerabilities. On the other hand, Appknox's Penetration Testing solution delivers reliable and thorough security assessments by expert security researchers. Users can request a manual pentest effortlessly, and the skilled team analyzes apps to identify and eliminate potential threats. The process includes identifying the tech stack, analyzing the threat landscape, setting up breakpoints, testing responses, detecting bugs, and performing advanced threat exploits.
3. Enterprise IoT Security
Enterprise IoT Security is a comprehensive Zero Trust solution designed to address IoT devices' security challenges in modern enterprises. It helps eliminate implicit trust and enforces zero-trust principles through least privilege access, continuous trust verification, and continuous security inspection. With this solution, organizations can quickly discover and assess every IoT device, easily segment and enforce the least privileged access, and protect against known and unknown threats. By simplifying operations, Enterprise IoT Security enables faster deployment, with a 15-time faster deployment than other solutions. The solution offers better and faster protection for IoT devices, delivering 70 times more security efficiency and 20 times speedier policy creation.
4. Azure Sphere
Azure Sphere is a secure IoT platform offered by Microsoft that allows businesses to create, connect, and maintain intelligent IoT devices. It provides end-to-end security, from the silicon level to the operating system (OS) to the cloud. With Azure Sphere, organizations can securely connect, manage, and protect existing and new IoT devices. The platform offers over-the-air updates, integration with IoT platform services, and continuous security improvements. It helps businesses deploy real-time security patches, maintain device operations, and accelerate time to market. Azure Sphere incorporates essential security properties and offers comprehensive security and compliance.
5. Microsoft Defender for IoT
Microsoft Defender for IoT is a comprehensive security solution that provides real-time asset discovery, vulnerability management, and threat protection for the Internet of Things and industrial infrastructure, including ICS/OT environments. It offers context-aware visibility into IoT and OT assets, enabling organizations to manage their security posture and reduce attack surfaces based on risk prioritization. With behavioral analytics, it detects and responds to attacks across IT and OT networks. Integrated with SIEM/SOAR and XDR tools, it delivers unified security and leverages threat intelligence for automatic response. Microsoft Defender for IoT is designed to meet the unique security needs of various industries and supports complete endpoint protection when combined with Defender for Endpoint.
6. IoT Security
Forescout offers an IoT security solution that automates security measures and provides visibility for every device connected to the network. Their zero-trust approach ensures complete device visibility, proactive network segmentation, and least-privilege access control for IoT, OT, IoMT, and IT devices. The platform classifies and monitors devices in real time, identifies weak credentials, and enforces strong passwords. It also enables dynamic network segmentation and automates zero trust policy orchestration across multi-vendor environments. Forescout's solution efficiently manages asset inventory and device lifecycle and has been proven to scale for enterprise-level deployments.
7. ThingSpace
The ThingSpace Platform for IoT offers a comprehensive set of iot tools and devices for developing and managing the lifecycle of IoT devices. It enables connectivity management at scale, allowing secure activation on the Verizon network and providing features to troubleshoot, locate, and manage IoT devices. Whether at the prototype stage or ready to scale for enterprise-level deployment, ThingSpace provides the necessary resources for IoT solution development and management. As a Magic Quadrant Leader for IoT Connectivity Services, ThingSpace offers solutions for software management, device readiness, and overall device lifecycle management. Businesses can collaborate with technology leaders through their Executive Briefing Program to achieve their specific goals and gain a competitive edge.
8. Verimatrix
The Verimatrix Secure Delivery Platform offers a unified user experience by combining cybersecurity and anti-piracy services into a comprehensive cloud ecosystem. It provides media companies, content owners, streaming providers, and broadcast operators with a single pane of glass experience for securing content, applications, and devices. Key offerings include Streamkeeper Multi-DRM for cloud-based digital rights management, Verimatrix App Shield for zero code hardening of mobile applications, Verimatrix Video Content Authority System (VCAS) for real-time monitoring, and Streamkeeper Counterspy for cybersecurity and anti-piracy solutions. The platform also facilitates partner integrations, enabling seamless onboarding and revenue preservation.
9. Trustwave
Trustwave's Managed IoT Security provides comprehensive solutions to secure the Internet of Things (IoT) and minimize the risk of compromise. With expertise from Trustwave SpiderLabs, it offers knowledge about network assets, identifies weaknesses in applications, servers, APIs, and cloud clusters, and enables secure IoT deployment with quick validation of fixes. This reduces the risk of compromised devices, which can lead to various threats, including DoS attacks, privacy violations, and data theft. Trustwave's services cater to IoT developers/manufacturers, offering product security reviews, testing, and incident readiness services. For IoT implementers, it provides managed security services and testing to safeguard deployments and associated data.
10. ARMIS Agentless Device Security Platform
The ARMIS Agentless Device Security Platform supports implementing the Critical Security Controls(CIS) framework. Developed by the Center for Internet Security (CIS), these controls are periodically updated by a global community of experts. ARMIS aligns with the CIS Controls and provides a comprehensive set of security controls to address the framework's requirements. The platform caters to enterprises of all sizes and offers different implementation groups based on risk profile and available resources. With ARMIS, organizations can enhance their cybersecurity posture and implement the CIS Controls effectively.
Final Thoughts
Security is a major concern in IoT tools and software due to the proliferation of connected devices, the diverse and complex nature of IoT ecosystems, the need to protect data privacy and confidentiality, the lack of standardization, the long lifecycles of devices, and the distributed and scalable nature of IoT deployments. Addressing these concerns is crucial to prevent unauthorized access, data breaches, and ensure the integrity and privacy of IoT data.
The IoT tools and technologies discussed in this article represent some of the top options for conducting comprehensive IoT security testing. By leveraging these tools, professionals and organizations can proactively identify and address vulnerabilities in their IoT systems, ensuring their data and devices' confidentiality, integrity, and availability. By incorporating these tools into their security practices, organizations can bolster their IoT security strategy and enhance their ability to protect against emerging threats in the dynamic IoT landscape.
Read More
Industrial IoT, Theory and Strategy
Article | May 17, 2023
Explore the emerging complexities of IoT data governance with 7 key challenges to tackle. Address data privacy, security, and ethical concerns, empowering your business for success in 2023 and beyond.
Contents
1 The Case for Maintaining IoT Data Governance
2 Challenges of IoT Data Governance
2.1 Lack of Organizational Commitment
2.2 Data Privacy Concerns
2.3 Lack of Endpoint Security for IoT Devices
2.4 Issues with IoT Device Authentication
2.5 Increasing Volume of Unstructured Data
2.6 Unethical Use of IoT Data
2.7 Inadequate Data Governance Protocols
3 Addressing IoT Data Governance Challenges
3.1 Security by Design
3.2 Awareness Initiatives
3.3 Standardized Data Governance Policies
4 Conclusion
1 The Case for Maintaining IoT Data Governance
The growing use of IoT devices across various industries has caused a surge in data volume. Most of these devices store sensitive company data, which plays a crucial role in business operations but can have dire consequences if it falls into the wrong hands. Thus, companies need to understand what is IoT governance and its implementation to safeguard sensitive data from unauthorized access and malicious exploitation.
2 Top Challenges in IoT Data Governance for Businesses
2.1 Lack of Organizational Commitment
Organizational commitment is essential for effective IoT data governance. There needs to be a clear purpose and goals regarding data governance that are communicated to all stakeholders. Not focusing on organizational commitment can result in a lack of alignment between the organization's goals and the IoT data governance strategy, as well as uncertainty about ownership and accountability for data governance across the organization.
2.2 Data Privacy Concerns
Ensuring data privacy is a significant concern when implementing IoT data management to maintain IoT data governance security. With the vast amount of data generated by IoT devices, there is an increased risk of personal and sensitive data being compromised. Therefore, it is crucial to identify potential vulnerabilities, mitigate the risk of data privacy breaches in IoT environments, and anonymize user data for consumer devices.
2.3 Lack of Endpoint Security for IoT Devices
IoT devices are often designed with limited processing power and memory, and as such, many connected devices do not have built-in security features. This makes them attractive targets for hackers seeking to access confidential data or disrupt operations. Without proper endpoint security measures, IoT devices can be compromised, leading to data breaches, network downtime, and other security incidents that can compromise the entire system's integrity.
2.4 Issues with IoT Device Authentication
When IoT devices are designed without proper authentication mechanisms, it can be challenging to verify their identities. This results in possible unauthorized access, data breaches, and other security incidents. To supplement IoT data management practices, companies must implement secure authentication protocols specifically designed for IoT environments, such as device certificates, digital signatures, and multi-factor authentication, to maintain IoT data governance.
2.5 Increasing Volume of Unstructured Data
IoT devices generate vast amounts of data in various formats and structures, including text, images, audio, and video, which can be difficult to process, manage, and analyze. This data is often stored in different locations and formats, making it challenging to ensure quality and consistency. Moreover, this flood of unstructured data can contain sensitive information that must be protected to comply with regulations and standards. For effective IoT data governance, it is necessary to implement data classification, metadata management, and data quality management to make sense of unstructured data.
2.6 Unethical Use of IoT Data
IoT devices collect data that can be sensitive and personal, and misuse can lead to various negative consequences. Data from IoT devices can be used to develop insights, but it must be handled carefully to avoid privacy violations, discrimination, or other negative consequences. Ensuring data ethics requires organizations to consider the potential impacts of their data collection and use practices on various stakeholders. This involves addressing issues such as data privacy, data ownership, transparency, and bias in IoT data analytics.
2.7 Inadequate Data Governance Protocols
Without proper data governance protocols, IoT data may be inaccurate, incomplete, or difficult to access or analyze, reducing the effectiveness of IoT systems and limiting the potential benefits they can provide. Additionally, inadequate data governance protocols can lead to security and privacy vulnerabilities, potentially exposing sensitive data to unauthorized access or theft. This can result in legal and regulatory penalties, reputational damage, and a loss of customer trust.
3 Addressing IoT Data Governance Challenges
3.1 Security by Design
This approach involves integrating security and governance considerations into the design and development of IoT systems from the outset. This helps minimize vulnerabilities, prevent breaches that may compromise the confidentiality, integrity, and availability of IoT data, and help maintain IoT data governance. In addition, by prioritizing security in the design phase, organizations can implement security controls and features tailored to their IoT systems' specific needs, which can help prevent unauthorized access, manipulation, or theft of IoT data.
3.2 Awareness Initiatives
IoT data governance challenges can arise due to an improperly trained workforce that may not recognize the purpose and benefits of data governance practices. Awareness initiatives can help organizations develop a culture of security and privacy. These initiatives can educate employees and stakeholders about the risks and best practices associated with IoT data governance, including the importance of data security, privacy, and ethical considerations. By raising awareness of these issues, organizations can promote a culture of responsible data management, encourage stakeholders to adhere to data governance policies and procedures, and reduce the risk of human error or intentional misconduct that could compromise IoT data.
3.3 Standardized Data Governance Policies
Collaboration between local, regional, and federal governments and businesses is essential to establishing frameworks for implementing IoT and related technologies within their jurisdictions. Cooperation between governments and enterprises is crucial for implementing a standardized IoT data governance policy. This will protect end-users by mandating basic standards in procurement processes and creating regulations and guidelines that promote responsible data governance.
4 IoT Data Governance: Future Outlook
Data is one of the most valuable resources for organizations today, and addressing the problem of IoT data governance will ensure that the IoT of enterprises is used effectively and responsibly. Straits Research reported that the worldwide data governance market had a worth of USD 2.1 billion in 2021 and is projected to reach an estimated USD 11.68 billion by 2030. IoT devices are a key driving factor behind the growth of the data governance market, and as the amount of data generated and the number of devices grows, so will the complexity of data governance. By maintaining strong data governance policies and tracking changes in policies and best practices, businesses can ensure compliance and maintain trust in the long run.
Read More