IoT Security
Article | October 11, 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.
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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.
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IoT Security
Article | July 17, 2023
The concept of "never trust, always verify" is the foundation of the relatively new security architecture known as "zero trust." Zero trust requires that all users and devices be verified every time they connect, even from inside the "moat," in contrast to the conventional castle-and-moat security architecture, which automatically trusts users and devices located within a network's perimeter.
Companies are being forced to reconsider how they safeguard their networks by the internet of things (IoT). Unmanaged smart gadgets connected to the internet expand the number of potential access points for hackers to compromise your security when they are added to a network.
Zero Trust Security Expansion for IoT
After establishing it for users and their devices, organizations must extend zero-trust security to cover unmanaged, non-user devices too. To do this, they require zero trust identity management technologies that automatically register devices, issue credentials, and offer password-less authentication.
Device Visibility
A device may be infected with malware or have a security breach if performance problems or bugs start to appear frequently. In addition, a malfunctioning device may be more vulnerable to attack. Therefore, organizations require device health monitoring that can automatically identify problems and flag them for remedy in order to establish and maintain zero trust security for IoT. Some cutting-edge solutions can also automatically prevent an impacted device from making further connection attempts or carrying out corrective actions without requiring human participation.
The Principle of Least Privilege (PoLP)
The principle of least privilege (PoLP), which argues that any user or device should only obtain the bare minimum access privileges necessary to perform their job functions, is widely used in conjunction with zero trust security. Therefore, organizations must establish the minimal level of network access required for each device to carry out its functions before limiting its potential privileges in order to deploy PoLP for IoT. Implementing identity and access management (IAM) tools and guidelines that support zero trust and PoLP for devices is one approach to accomplishing this.
Security Monitoring
There are other zero-trust security monitoring programs created especially for IoT, such as Palo Alto Networks' IoT Security, which was previously discussed. Businesses can also utilize tools to monitor devices and network traffic, such as next-generation firewalls and intrusion detection and prevention systems (IDS/IPS). The zero trust security solution for IoT must include monitoring in addition to as much automation as possible so that threats can be identified, contained, and remedied even when no one is there to press a button or disconnect a device manually.
One of the leading causes of zero trust security projects failing over time is that people stop adhering to them once they get complicated. This is especially true for IoT security that operates on zero trust. In addition, it can be logistically challenging to keep remote, unmanaged devices at zero trust.
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IoT Security
Article | June 27, 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.
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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.
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