Watch the full episode below

In the fifth episode of Prompt & Response, the Inetum podcast about AI in practice, host Piotr Mechliński (Data & GenAI Manager EEMEA, Inetum Polska) talks with Przemysław Saniak (Business Development Director) and Andrzej Gumieniak (IoT Practice Leader) about what changed, what still blocks scale, and where AIoT, Artificial Intelligence of Things, is heading next.

TL;DR. IoT is no longer the buzzword, it is the quiet hero running under the floor of modern enterprises. AI is the brain that finally makes that nervous system pay back, as our expert sums up. The winners in 2026 are the ones who combine edge AI, a Unified Namespace for their device data, and serious data governance, and who start from a business case instead of a shopping list of platforms.

Key takeaways from the episode

• IoT is mature: over 20 billion devices were connected by the end of 2025, a number expected to double by 2030.
• Predictive maintenance can cut unplanned stops by up to 70%. Smart buildings can save 20 to 30% on energy and water (U.S. Department of Energy).
• The next step is AIoT: intelligence moves to the edge, the nervous system gets a brain.
• The closed loop is the real shift: not only reading data, but sending decisions back to devices in real time.
• The biggest blockers are legal (Cyber Resilience Act, NIS2), organisational (who owns IoT inside the company) and technical (20 to 30 year old PLCs, vendor lock-in).
• Unified Namespace is becoming the default architecture to unify device data and feed AI agents with context.
• Data governance is not optional anymore. Without a catalogued context, LLMs and agents cannot act reliably on operational data.

From IoT to AIoT: why the name changed

The term AIoT, Artificial Intelligence of Things, is not marketing polish. It captures a real shift: intelligence no longer lives only in the cloud.

Three forces push in the same direction:

1. Smaller devices, bigger compute. Miniaturisation lets companies deploy far more sensors and actuators without expanding footprint.
2. Edge inference. Predictive models are now small and efficient enough to run directly on the device. Less round-tripping to the cloud, faster action, lower cost per decision.
3. Enterprise context for AI. Real-time sensor streams combine with vector databases, retrieval augmented systems and company documentation so that AI agents can reason over operations, not just transactions.

If IoT is the nervous system of the enterprise, AI is the brain. You just combine them to get value out of the whole body.

Andrzej Gumieniak, IoT Practice Leader, Inetum Polska

Where AIoT actually creates value

There is no shortage of sensors. There is a shortage of good answers to one question: what changes in cost, uptime, revenue or speed because of this deployment?

Przemysław groups the real payback into three streams.

Cost optimisation

• Predictive maintenance. Reduction of unplanned stops by up to 70%, with the matching cut in downtime cost.
• Smart buildings. According to the U.S. Department of Energy, smart buildings can save up to 30% on energy and 20 to 30% on water consumption.
• Smart cities. Harder to quantify, but quality of living and operational efficiency improve.

New business models

Connected products make “as a service” models practical: equipment as a service, usage based contracts, proactive SLA management. The trade-off is real: vendors take on more operational responsibility, but they gain deeper, longer customer relationships.

The closed loop

Andrzej adds the stream that separates mature players from beginners. The data flow is no longer one way, bottom-up. Once you trust the models, you let the system send commands back to the devices and steer production in near real time. That is the step where IoT stops being a dashboard and starts being an operating system.

The value is not in knowing faster. It is in reacting faster, and that is where AI earns its keep.

Przemysław Saniak, Business Development Director, Inetum Polska

Edge AI and custom models: why manufacturing is different

Manufacturing rarely fits a generic AI template. Every plant is different, every process has its quirks, every shop floor has its own mix of old and new equipment. That is why AIoT in factories leans toward custom models and edge AI.

Two practical consequences:

• Not all data is gold. Edge devices filter and summarise at the source, so only relevant signals reach the central systems.
• End-to-end streams. Decisions happen where they belong: at the edge for safety and latency, at the core for cross-plant optimisation and learning.

The integration challenges nobody warns you about

Scaling AIoT is worth it, but the path is rarely clean. Inetum IoT experts name three categories of friction.

Legal and regulatory

The EU keeps raising the bar: Cyber Resilience Act, NIS2, sectoral rules on top. If connected devices touch critical infrastructure, compliance is not a backlog item, it is a design constraint.

Organisational

IoT often lands between departments. Is it IT? Is it production engineering? Who is the product owner? Scaling without a clear owner guarantees pilot limbo. A defined operating model is as important as any platform choice.

Technical and legacy

Factories run on equipment that is 20 or even 30 years old. Old PLCs are already wired into SCADA systems, so connectivity exists, but it is brittle and locked to specific vendors. Inetum has seen sites where assets were linked only by SMS, because that was the only usable channel. That is enough to start, but it is not an architecture.

Unified Namespace: the foundation for agentic IoT

The emerging answer on the shop floor is the Unified Namespace (UNS): a single, standardised place where every device publishes its state in real time, in a format everyone in the organisation can understand.

Two ideas underpin it:

Pub-sub over polling

Instead of legacy systems constantly querying PLC registers, devices publish events to a central message bus. Any consumer, SCADA, MES, analytics, AI agents, picks up what it needs.

Shared naming and semantics

Every signal is named and described consistently, so meaning does not get lost as data propagates.

UNS is not only a networking pattern. It is a data governance decision applied to operational technology. That is exactly what agentic AI needs in order to act safely on the factory floor.

Data governance is the new battleground

LLMs and agents cannot reason well on unlabelled streams. For AIoT to deliver, companies need more than telemetry. They need context:

• catalogued data with clear definitions,
• historical records linked to outcomes,
• documentation, runbooks and maintenance history,
• the tribal knowledge that usually lives in senior technicians’ heads.

Feed that into the “brain” of the enterprise and the agent can make informed decisions in seconds, not after a postmortem. Skip it, and you get confident-sounding suggestions that miss the point.

Data governance, in other words, is now a prerequisite for operational AI, not a sideshow for the data team.

What comes next: agentic IoT and Industry 5.0

Looking a step ahead, the guests see two overlapping horizons.

Fully agentic IoT

Once UNS and governance are in place, AI agents can plan maintenance routes, reschedule production, adjust set points and trigger safety interventions, all while keeping a human in the loop where it matters. Inetum is already seeing early versions of this pattern in manufacturing and utilities.

Industry 5.0 and human safety

The collaboration between IoT, robotics, AI and people is getting tighter. Edge AI is a critical enabler for worker safety in harsh environments: detecting risks and stopping machines before a person can be harmed.

Przemysław references Stanley Kubrick: the HAL 9000-style, AI-enhanced operational control centre is no longer science fiction. With mature data foundations and strong cybersecurity, these centres already exist in advanced industrial sites, taking routine load off operators and letting them focus on exceptions.

Where to start if you are beginning today

If you are just starting your IoT (or AIoT) journey, both guests converge on the same playbook.

1. Start with the business case, not the platform. Name the outcome: less downtime, lower energy cost, new service revenue, safer workers. Then work backwards to technology.
2. Make the value measurable from day one. Run short iterations, define what “worth keeping” looks like, and be ready to kill a use case that does not deliver.
3. Think governance in parallel, not afterwards. Data governance, AI governance and a clear operating model need to grow alongside the first MVPs, not after the third one.
4. Do not try to climb Everest in one jump. Connect devices first. Learn to react on their data. Then add AI on top. Big picture in mind, small moves on the ground.
5. Build vs buy is not the first question. With 600+ IoT platforms by 2021 (a number that has since consolidated, including vendors like Google stepping out of the space), and with GenAI now accelerating custom builds, the choice is easier once you have proven value. Not before.

Start with the business case. Otherwise you will build a compressed solution that connects your freezer to your TV, and not much else.

Przemysław Saniak, Business Development Director, Inetum Polska

FAQ

Ready to take the next step?

Your AI journey starts with a conversation. Share your challenge, idea, or question, and our experts will respond with insights and practical guidance. Talk to us

About the speakers

• Piotr Mechliński, Data & GenAI Manager EEMEA, Inetum Polska, host of Prompt & Response.
• Przemysław Saniak, Business Development Director, Inetum Polska.
• Andrzej Gumieniak, IoT Practice Leader, Inetum Polska.

Previous episodes of Prompt & Response

• All episodes
• AI compliance marketing agent
• AI strategy and use case discovery
• Voice of the customer in the age of GenAI
• AI in manufacturing

The key difference does not start with algorithms or platforms. It starts with the nature of data itself.

This episode is especially relevant for leaders and architects who want to move beyond Industry 4.0 dashboards and understand how modern data platforms and generative AI can support real manufacturing processes, not just proofs of concept.

Watch the full episode below

Prompt & Response Webcast #4 – Transcript & Key Insights

Data does not start digital in manufacturing industry

In digital-native industries, data is born digital. Transactions, clicks, and interactions are structured from the very beginning. Manufacturing works in the opposite direction.

Here, data originates in the physical world. Pressure, vibration, temperature, sound, movement – all of these signals must first be measured and translated into digital form. Only then can analytics or AI be applied.

This is why working with manufacturing data is never just about dashboards. It requires engineering understanding and domain knowledge. You need to know how machines behave, how materials age, and how small physical deviations can signal much larger problems.

The challenge is amplified by legacy environments. Many factories still rely on machines and PLC controllers designed decades ago. AI systems must integrate with technology that was never meant to be connected, scalable, or data-driven.

In manufacturing, data doesn’t come from clicks or transactions. It comes from physics. From pressure, vibration, temperature.
If you don’t understand the physical process behind the data, no AI model will save you.

Marek Czachorowski
(AI & Data Practice Leader, Inetum)

Repeatable processes create real analytical potential for manufacturing companies

Despite these constraints, manufacturing has a powerful advantage: repeatability. Production lines, assembly steps, quality checks, and logistics flows run every day, often thousands of times.

Where repetition exists, patterns emerge. Deviations become visible. This is why manufacturing, when approached correctly, is such a strong candidate for analytics and AI.

The value goes beyond the production line itself. Supporting processes like supply chain management or logistics generate equally important signals. When analyzed together, they reveal how a factory truly operates – not just how it was designed to operate.

Products continue to generate data after leaving the factory

Modern manufacturing no longer ends at production. Products themselves become continuous sources of data once they are in use.

Predictive maintenance is often called the holy grail of manufacturing AI. Not because it’s technically impressive, but because even small improvements can save millions when downtime is critical.

Sebastian Stefanowski
(Chief Architect)

Sensors embedded in machines, vehicles, or equipment allow manufacturers to understand how products behave in real conditions. This enables a shift from reactive maintenance to predictive maintenance – identifying issues before failures occur.

In highly complex environments, such as aviation, even small improvements have a massive impact. Unplanned maintenance is expensive, downtime is critical, and better prediction translates directly into safety improvements and measurable cost savings.

What distinguishes successful cases is rarely the sophistication of the model. It is the maturity of the data foundations behind it.

Quality control moves from inspection to prevention

AI also changes how quality is managed inside the factory. Instead of discovering defects at the end of production, machine learning models can identify early signals that something is going wrong.

Subtle anomalies appear long before a defect becomes visible. This allows production to stop earlier, avoid unnecessary cost, and focus resources where they actually matter.

The result is not just better quality, but higher throughput and lower waste at the same time – a combination that traditional approaches struggle to deliver.

Use AI to your advantage: from collecting data to understanding it

For many organizations, Industry 4.0 initiatives focused on connectivity and dashboards. Sensors were installed, data was collected, charts were built. What is changing now is the role of generative AI.

The focus shifts from collecting data to understanding it. GenAI acts as a bridge between humans and complex technical environments, helping engineers navigate documentation, maintenance history, sensor logs, and operational data more efficiently.

This mirrors patterns discussed earlier in Prompt & Response, including voice of the customer in the age of GenAI, where GenAI serves as a translator between raw data and human decision-making.

The impact is especially visible in knowledge retention. Experienced engineers leave, taking years of tacit knowledge with them. AI does not replace expertise, but it makes existing knowledge accessible and usable at scale.

Start with business reality, not with ai

Most AI initiatives in manufacturing don’t fail because of algorithms. They fail because data foundations and business context were never fixed first

Piotr Mechliński
(Host, Head of AI & Data, EEMEA)

One message remains consistent throughout this episode and the entire series. Successful AI in manufacturing does not start with AI.

It starts with business reality. Clear goals, well-understood processes, and reliable data foundations come first. Only then does it make sense to decide whether machine learning, predictive models, or Generative AI are the right tools — and where they will actually deliver value.

This is the natural continuation of the journey discussed in From AI strategy to execution, where strategy only matters if it can survive contact with reality.

Manufacturing is not harder than other industries when it comes to AI. It is simply different. Organizations that respect that difference are the ones that turn AI from hype into measurable results.

To access the video, please fill in the form

Speakers

ANDRZEJ GUMIENIAK
Head of Practice IoT and Distributed Solutions at Inetum

PRZEMYSŁAW SANIAK
Business Development Manager at Inetum

Understanding IoT connectivity

The connectivity level is a ground zero for IoT solutions. The first step in the IoT journey is connecting devices and ensuring data flow to your systems.  At this point, you must ensure that all devices you plan on gathering data from are connected to your data platform and that the data flow is recognized.

The journey that starts at the point of connectivity aligns with the stages of the Industrial Revolution. 

Types of IoT connectivity technologies

Connectivity is key for data gathering, but it’s complex. It can be divided into wired and wireless technologies, each with different protocols and applications. Factors to consider include power consumption, range, data rate, scalability, interoperability, and security. 

Do you need an IoT connectivity management platform?

Connectivity management platforms allow you to oversee your devices and infrastructure from one place, Such platforms are secure and allow you to tailor the solution to your needs accordingly.

Read also: 5 IoT challenges in 2024 and a guide to overcoming them.

Choosing the right IoT connectivity solution

Connectivity choice is crucial when it comes to using the right IoT connectivity solution. With the rise of IoT technology, there are various connectivity options available, each with its benefits and drawbacks. 

IoT Connectivity Comparison 

There are different types of connections in the context of IoT devices. Some of the examples: 

Wired connectivity

• Ethernet
• Serial
• Fieldbus
• USB
• Power Line Communication

Wireless connectivity

• RF 433 MhZ
• WiFi (IEEE 802.11)
• NFC
• LoRA
• Cellular (GPRS 3G / 4G, Nb-IoT, LTE-M, 5G
• Zigbee

IoT Application Protocols

• CoAp
• MQTT
• DDS
• XMPP 
• AMQP
• HTTP/REST
• FTP 
• LwM2M

Factors to Consider When Selecting an IoT Connectivity Solution

When comparing different connectivity options, you have to consider such factors as: 

• range and related costs

Specific connectivity solutions cover smaller or larger areas. Mobile solutions cover large areas and cellular IoT is a rapidly growing technology. Satellite solutions, in turn, have global coverage. The choice of solution will affect costs (e.g., related to data transfer and range).  Cellular connectivity is one of the most important areas and IoT trends,.

• bandwidth

Data transfer between devices will also incur costs. Bandwidth speed is crucial in many situations, as latency can directly affect your business. 

• power consumption

Taking into account the aspect of data transfer speed, we must remember that this affects energy consumption. The faster the transfer, the higher the consumption. The choice between high-latency solutions and low-latency solutions will therefore depend on the company’s needs in terms of connecting devices (e.g., do we need to work on real-time data).

Connectivity – exploring IoT use cases

Inetum cooperated with an industry leader in producing steel wires and ropes. Our client needed to measure steel rope parameters involving various data types, including numeric and image data, transferred using an internet connection. 

Their goals and challenges:   

• Centralized round-checks resources to manage the product lifecycle 

• Easily compare results of consecutive checks 

• Scattered data of different types (numeric, images, MRT results) 

How did they prepare for the IoT journey: 

• Custom optical and MRT solution for steel ropes monitoring 

• Measurement data gathered in a local database 

• Ethernet-based communication for the first stage 

IoT connectivity solutions in various industries

We cited an example of a company in the manufacturing area. However, connected devices are common in almost every industry, and in many of them – they are critical. Industries that are successfully using IoT-connected devices include healthcare, automotive, logistics, retail, and agriculture, for example.

Other interesting examples of IIoT solutions from projects implemented by Inetum: 

• Our client from the energy sector dealt with thousands of devices using wireless and cellular connections, standardizing data for central IoT solutions.
• The other client of Inetum, used a combination of wired and satellite links for real-time data transfer from their jet engines. 

Security and efficiency in IoT connectivity

The more connected devices and the greater the number of operating systems a company has, the greater the risk of cybersecurity incidents. Here is why it is important to include secure IoT strategies. 

Possible threats include the use of security breaches, malware attacks and, also, the human factor due to a lack of awareness of threats.

Ensuring IoT security in connectivity solutions

• get to know the risks and undertake protective measures.
• upgrade company knowledge in terms of IoT security solutions
• use proven safety standards and solutions (firewalls, extending endpoint security). 
• set up processes for dealing with security incidents, bearing in might that “anything that can go wrong will go wrong”. 

Optimizing power consumption in IoT devices

When it comes to optimizing power consumption in IoT devices, the choice of IoT connectivity option plays a crucial role. Choosing an IoT connectivity that is tailored to the specific requirements of the device can significantly impact its energy efficiency. It is important to evaluate the power consumption. 

Here are the steps you can take: 

• Optimize network 
  – Ensure proper IoT device’s transmission frequency 
  – Comply with bandwidth standards
  – Transmit only useful data to lower energy consumption
  
• Optimize software 
  –  Use low-power sleep states appropriately 
  – Take advantage of monitoring capabilities and apply predictive maintenance
  – Ensure your applications are not performing unnecessary tasks 
  
• Optimize hardware
  – Select appropriate microcontrollers (MCU) with various speeds and clock times to adjust the battery usage. 
  – Disable power to unused RAM, whenever it is an option.
  – Restrict the number of peripherals and get rid of unused ones.

How to choose the right connectivity technologies? 

Choosing the right connectivity technology can be troublesome.

Especially if we must balance price, data transfer speed, and power consumption. What you need to consider and questions you need to ask yourself: 

• the type and size of devices and their scalability – bear in mind not only individual devices and their size, but the entire ecosystem – the number of devices in the IoT network, security solutions, and network capabilities. 
• Environmental aspects – will your devices be spread over a smaller or larger area?  In cities, rural areas, or remote locations? All this will affect the type of connectivity, as you need to consider the appropriate coverage of the area and the power consumption of the devices.  
• Needs in terms of data transmission – are you operating on real-time data (live streaming, or batch-processing data? What are the data sizes and what speed do you need for your operations?  

Technologies for IoT – summary 

The role of IoT solutions is growing in areas such as remote asset monitoring, IoT-based process automation, vehicle fleet management, location tracking, or plant asset performance optimization. Connecting IoT devices is just the first step leading to understanding data and making value out of it. To advance, you need a plan, skills, knowledge of use cases, and partnerships. All these factors will contribute to the success of an organization’s IoT deployment.

When deciding on a particular IoT platform, it is worth making sure what the capabilities are within the connectivity management framework. At Inetum, we are a partner of Cumulocity IoT, which has a network of partners providing platform-compatible devices with substantial connectivity capabilities.

Streamline Your IoT Operations Andrzej Gumieniak, our Head of Practice IoT, is here to help you navigate the complexities of IoT solutions. Book a consultation to discuss your case. Schedule a meeting

AI-enabled IoT. Key statistics on AI implementations

IoT – this technology is not just about smart cities, smart homes, and wearable gadgets. It is a transformative industrial advantage. In recent years, IoT has been quite hype, and we know the hype curve is high right now. IoT and AI increasingly often join forces to collect data and enable IoT devices to use their maximum potential. According to IoT Analytics Research, in 2022, only 17% of Artificial Intelligence solutions were implemented in IoT.

Future of AI-powered IoT applications

However, this state is expected to change by 2027 when nearly 50% of IoT solutions are predicted to have an AI component, with 13% being AI-based and 43% AI-augmented. That shows the real power of AI within Internet of Things solutions.   

Integration of AI and IoT for operational efficiency

Why do companies decide to integrate AI into the Internet of Things?  

So where does the huge popularity of combining AI and IoT take its roots?   

First, it is all about convenience and seeking ways to expedite operations. Humans naturally seek efficiency and convenience, often preferring to avoid performing repetitive and laborious tasks ourselves.  

Consequently, we delegate these tasks to computers, leveraging automation to handle them. By enabling automated systems to manage and control various processes, we can ensure that once we detect an issue or an opportunity, it can be addressed promptly and effectively by these systems.   

Now it is time to use AI  

Once we can control our connected devices, why not leave it to the computer to – using AI and Machine Learning techniques – analyze data generated by IoT devices and assess if something wrong is going on with those smart devices? This allows us to take appropriate actions. This way, IoT and AI-enabled data analysis ensure operational excellence.   

Real-world examples of AI and IoT implementation

Many companies that have decided to integrate AI into their IoT environment can already boast interesting solutions. Our client is a leader in green energy and wind turbine manufacturing.   They faced the challenge of operating thousands of wind turbines worldwide with only a few operators.  

Their goals and challenges   

• Centralized maintenance of worldwide wind fleet   
• Different technologies (some 25-years old)   
• Operator learning period and rotation    
• Number of devices and alarms to handle   

How did they prepare for their AI with IoT journey:

• Up to 300 built-in sensors   
• Different communication options – wired and wireless (mostly cellular)    
• Data delivery from periodical SMTP to real-time OPC-UA   

Artificial Intelligence in IoT solution  

For the client, the AI solution turned out to be the best one. It was based on sophisticated conditional rules working on a streaming analytics engine where the incoming data was compared with the enterprise data. This resulted in many alerts. So, to address that with a limited workforce, streaming analytics and integration with the enterprise systems (SAP and ServiceNow) were used. The simpler alerts of non-critical severity were managed automatically. By creating the proper work orders for the proper maintenance, human operators were relieved and could handle critical issues.   

Once the problem is resolved in the field, the status is automatically synchronized back from SAP systems to the IoT platform and the dashboard. This way, the operators know that the problems are resolved. With an autonomous approach, we reach very high productivity of the maintenance and operations teams.

Streamline Your IoT Operations Andrzej Gumieniak, our Head of Practice IoT, is here to help you navigate the complexities of IoT solutions. Book a consultation to discuss your case. Schedule a meeting

How does AI improve connectivity and decision-making in IoT systems?

For that, we can take advantage of Artificial Intelligence model assistance, pre-trained for specific use cases. This way we do not need to build up sophisticated conditional rules based on many parameters inside our business logic as AI supports us with certain routine tasks. That leads to an especially important and useful application: prescriptive maintenance. This maintenance strategy takes advantage of machine data to outline maintenance-related tasks.   

Also read: Predictive maintenance   

Machine Learning in IoT

Machine learning (ML) is playing a significant role in the Internet of Things (IoT) ecosystem. By leveraging AI and ML algorithms, companies can extract valuable insights from the vast amount of data generated by IoT sensors. Nowadays you can use IoT platforms (e.g. Cumulocity IoT) that have built-in ML components that make Machine Learning model training and implementation easier and quicker.   

AI in IoT streamlines data analytics

Using the incoming data, you can process it through analytics and predictive Machine Learning models. You no longer need to create alerts or work orders manually. Instead, you can utilize an AI advisor or a more sophisticated rules engine, taking advantage of contextual data to support issue resolution.  

IoT and AI for better alarm monitoring  

AI-enabled automation eliminates the need for operators to sift through thousands of alarms. Operators can then focus on the most challenging cases, while simple and obvious ones can be managed automatically by the central IoT platform.   

What are the benefits of AI and IoT?

• Faster issue resolution – data collected from the IoT sensors is analyzed in the instance by AI, which allows for quicker issue detection and resolution.  
• Improved security – IoT security is considered one of the biggest challenges and concerns. AI algorithms can monitor access to IoT networks and detect suspected activities.   
• Predictive & prescriptive maintenance capabilities for preventing downtime – by employing cognitive algorithms used in IoT, you can prescribe actions and instruct the maintenance staff on what to do or even issue the appropriate commands to the team automatically.   

Also read: The top challenges in IoT 

AI in IoT or Artificial Intelligence of Things?

Artificial intelligence in itself is a powerful tool. When it becomes the heart of an organism such as a network of connected IoT devices, you get an intelligent system capable of learning and making decisions. In this context, there is increasing talk of AIoT (Artificial Intelligence of Things) technology, in which IoT devices talk to each other and act independently of human intelligence.  

IoT and AI trends will become increasingly important

IoT applications increasingly leverage AI algorithms to analyze the vast amounts of data generated by industrial IoT devices. The combination of AI and IoT technologies allows for real-time data processing and the ability to make decisions based on the insights gathered in many industries now, including logistics, automotive, and manufacturing. Overall, the benefits of AI in IoT are vast and continue to expand as AI and IoT work together. We can expect this trend to gather pace, as the number of connected devices worldwide in 2025 is forecasted to hit 25 bn, according to IoT Analytics.

Understanding Preventive Maintenance vs. Predictive Maintenance

The reactive approach assumes that equipment is expected to operate until it fails and only then is the failure fixed. In the reactive approach, we try to mitigate these unexpected failures through regular, time-based service intervals, where the unit is stopped, and regular inspection and replacement of operating components is performed. This is supposed to prevent unexpected failures, but it often turns out that such a service stop was not necessary because the components were not yet worn out. 

Before we go any further, it’s important to explain two terms that often appear in the context of equipment maintenance: preventive maintenance and predictive maintenance. How do they differ?  

Predictive maintenance undertakings are referred to as proactive maintenance and are planned and scheduled according to equipment conditions based on sensor data analytics.  Preventive maintenance is a subset of “proactive” but is less advanced than either “condition-based” or “predictive”. 

Also read: Guide to predictive maintenance  

What is condition-based maintenance?

The goal of Condition-Based Maintenance (CBM) is to monitor the actual condition of equipment. Its backbone lies in checking a machine for very specific indicators. The main benefit is that analysis is performed while the machine is working; thus, there is no need to schedule operational disruptions.  

The Benefits of Predictive Maintenance Software

According to a study by Deloitte, more than 85% of manufacturing executives surveyed expect smart factory solutions to be the main factor driving competitiveness in the years ahead.  

While innovation gives companies a competitive advantage, supply chain disruptions and costly unplanned equipment maintenance works are still major concerns. This is why companies are moving towards disruptive technologies, investing in the Industrial Internet of Things, Digital Twins, and real-time monitoring solutions that enable predictive maintenance. This proactive approach to equipment maintenance management brings significant benefits.  

Analytics for Enhanced Decision-Making 

According to research, 70% of operators are not aware of when to replace, upgrade, or schedule equipment maintenance.  

Having data on asset performance and past failures is not yet the basis for drawing accurate conclusions about future incidents. Therefore, analytics and predictive models trained on historical data equip you with powerful tools to make better decisions when planning maintenance work.  

Improving Asset Performance with Predictive Analytics 

Industries for which assets are critical need to have solutions that help them drive revenue growth rather than struggle with asset maintenance costs.  

According to data cited by Forbes, classical preventive maintenance addresses about 20% of failures. More than 80% of machine failures, in turn, happen with no pattern and are referred to as so-called “random failures”. Predictive analytics solutions based on condition monitoring allow you to increase the detection of such failures, repair important parts, and improve asset performance.  

Reducing Downtime through Real-time Insights 

If we accept the hourly median of downtime cost quoted by IoT Analytics, every 60 seconds of unpredicted asset outage means losses of over $1,600. 

Condition monitoring analytical solutions can reduce these costs by 30 percent and minimize machine downtime by 50 percent, says a McKinsey report. A SoftwareAG report gives even more optimistic figures for the Cumulocity IoT platform: a 50 percent reduction in outage costs.  

Streamline Your IoT Operations Andrzej Gumieniak, our Head of Practice IoT, is here to help you navigate the complexities of IoT solutions. Book a consultation to discuss your case. Schedule a meeting

Examples of the Best Predictive Maintenance Software  

1. SAP Asset Performance 
   An advanced predictive analytics platform integrated with SAP’s enterprise applications (SAP ERP). The system enables real-time monitoring with Machine Learning algorithms.  

2. Cumulocity IoT 
   The Cumulocity IoT platform provides all the capabilities you need for building condition-based maintenance solutions and integrates with 3rd party solutions for Machine Learning model training capabilities. The platform has been acclaimed as a Leader in the 2024 Gartner Magic Quadrant for Global Industrial IoT Platforms. The provider reports that the estimated time to implement the Proof of Concept based on their IoT platform is less than 1.5 months. 

3. Azure IoT  
   Azure IoT is a cloud-based service by Microsoft. The platform offers robust capabilities for predictive maintenance by leveraging the Internet of Things alongside advanced analytics, Machine Learning, and AI solutions. Azure IoT has been acclaimed as a Leader in the 2024 Gartner Magic Quadrant for Global Industrial IoT Platforms.  

4. PTC ThingWorx  
   PTC ThingWorx  is a recognized platform that integrates IoT, AR, and AI to enhance equipment maintenance processes. PTC’s solution allows for quick application development and AR-IoT integration.  

5. Siemens Insights Hub  
   An IoT system with advanced predictive maintenance capabilities. The software allows real-time data processing, analytics, and integration with Siemens PLM software solutions. 

Implementing an Effective Predictive Maintenance Strategy

When it comes to ensuring the smooth operation of machinery, implementing a strong predictive maintenance program is crucial. Follow these steps to gain optimal results. 

1. Leveraging Sensors and Real-time Data for Proactive Maintenance 

In the proactive approach to maintenance, sensors play a crucial role in equipment monitoring. IoT sensors monitor the health and performance of equipment in real-time. They gather valuable data on aspects such as: 

• temperature,  
• vibration, 
• pressure, 
• speed, 
• humidity, 
• usage hours, 
• fuel levels, 
• air quality, etc. 

In this way, machine degradation can be detected far in advance. Automated alerts allow you to gain ongoing insight into the health of your devices and undertake maintenance activities when needed. 

2. Choosing the Best Predictive Maintenance Software 

Finding the best predictive maintenance software is not an easy undertaking, yet is a crucial part of implementing a predictive maintenance strategy. 

One important factor you will need to consider is whether the software uses a CMMS (Computerized Maintenance Management Software) system. This integrated approach can streamline maintenance operations and improve efficiency. 

It is also important to evaluate the features and capabilities of different options to ensure that you choose the right predictive maintenance software, aligned with the needs and goals of your organization. 

3. Ensuring You Have the Right Features in Predictive Maintenance Solutions 

There are many solutions available on the market, and the above are only selected examples. There is no single answer to the question “Which software is the best?”, as the decision, as in many situations, is an individual matter.  

Some aspects you may need to consider while searching for the best software:  

• data analysis capabilities  
• real-time monitoring capabilities 
• predictive modeling functionalities 
• intuitiveness of the interface  
• compatibility with your existing systems  
• costs and licensing models 
• time to market 
• customization possibilities 

Summary  

By using predictive maintenance solutions, companies not only prevent equipment failures but also reduce downtime and maintenance costs by 30-50%.  

The available software tools allow you to increase productivity thanks to advanced analytics supported with AI and ML algorithms. You can wisely plan maintenance schedules to ensure that your maintenance teams are equipped with reliable monitoring tools. Predictive maintenance enables better decision-making, which is now a must-have in industries where the cost of each hour of downtime goes into the hundreds of thousands of dollars.  

Addressing any challenges of the IoT, especially those related to development and security risks in business applications, is one of the most important issues for companies planning to implement the Industrial Internet of Things. Whether your company is taking advantage of the benefits of the IoT or is already planning to do so, you need to know how to address the issues effectively! Read our guide to learn more about IoT challenges and connectivity solutions that every IoT enthusiast should know. 

Challenges in the IoT  

The IoT is one of the fastest-growing areas of the market. Although 2023 was the year of Generative Artificial Intelligence, the breakthrough that has taken place in this realm will have an impact on other areas – including the further development and potential of IoT. 
 
To start with some relevant facts: 

• Two-thirds of industrial organizations stated that they are currently implementing an IIoT strategy, according to the IoT Analytics report. 
• 50% of businesses do not have the necessary in-house skills in security, data science, and connectivity to create, implement, and oversee IoT projects, according to Inmarsat research.  

Challenges in IoT development  

Developing IoT solutions comes with its own set of challenges. Addressing them is crucial as the IoT industry simply snowballs and more devices are deployed with the potential for security breaches.  

• Skills shortage – according to research conducted by Inmarsat, close to 50% of businesses do not have the necessary in-house skills in security, data science, and connectivity to create, implement, and oversee IoT projects. Implementing and maintaining IoT systems requires competence in many different areas. The key to success is the right blend of powers from the business and technology worlds. 
• Lack of a clear, phased strategic plan – IoT projects are not speedy implementations. They are time-consuming. The median time for IIoT projects in 2023 was approximately 20–25 months. Not everyone has the right strategists at home to divide the project into the required phases and plan for its long-term development. 
• No access to IoT proof points – not all companies are eager to share knowledge about their ongoing IoT projects and IoT technologies. Disclosing data, the technologies used or security features can open the way to security breaches in applications or on devices. Therefore, when you are looking for a third-party company to look after your IoT solutions, you may not get information about their previous projects.  
• Data and cybersecurity concerns – data and device connectivity are at the heart of the IoT. Security breaches and tactics keep on evolving; hence, IoT issues and sensitive data protection are the number one priority. For example, a malicious device can be introduced into the network by an attacker, or malware can be installed. Based on the security reports available, IoT router attacks are currently behind almost 75% of successful IoT attacks.  
• Need for large-scale, rapid investment – in an era of economic uncertainty, it seems every company is carefully watching their pennies. For IoT deployments, large-scale solutions that require changes during the project are sometimes a challenge. This creates uncertainty in terms of budget planning. ISG’s information shows that 90% of respondents admit that rapid project development forces them to change the solution, half of which is vitally important.  
• Lack of culture of collaboration – in the context of agile methodologies, the role of collaboration and good communication is undisputed. An agile mindset is just as important, if not more so, when implementing Internet of Things projects. With several years of work on an undertaking, it is easy to get discouraged and move away from ongoing communication when positive results are not immediately seen.  
• Resistance to outsourcing – last but not least, companies implementing IoT / IIoT solutions in-house are reluctant to consider the possibility of outsourcing this undertaking. According to the reports, only 30% of all respondents are searching for a business partner who offers comprehensive IoT services. The challenge here is especially in allocating access to data, which can become a risk in the wrong hands, instead of a competitive advantage. 
   

Ensuring connectivity in IoT  

Connectivity, meaning the capability of IoT devices or appliances to communicate with one another or with systems, is the foundation of IoT. Software AG research showed that almost 90% lack the expertise to choose and procure the device connectivity approach.  

Connectivity problems can be of various natures. A sensor device located in an inaccessible, remote location, poor device access to the network, or many dispersed units can cause poor connectivity. 

 Some common solutions to connectivity issues include: 

• using dedicated networks for IoT devices, 
• implementing mesh networking to extend the range of coverage, 
• using repeaters or signal boosters to improve the signal, 
• ensuring the proper configuration of devices and networks.  

Additionally, the regular maintenance and monitoring of IoT devices can help prevent and quickly address any issues related to connectivity. 

Protocols for seamless IoT integration 

The IoT revolution is made possible by, among other things, great integration between devices and platforms. At a time when more and more companies are relying on data processed in real time, such integration plays a key role. This is ensured by having the right protocols in place. Among others: 

1. MQTT (Message Queuing Telemetry Transport) – unreliable networks with low data capacity, significant delays, or disruptions. 

2. CoAP (Constrained Application Protocol) – built with M2M (Machine-to-Machine communication) applications such as smart energy and building automation.  

3. HTTP/HTTPS – for general requests/responses used for web services in general. 

4. WebSocket – efficient for real-time communication. Best for low-latency two-way communication. 

Managing IoT deployments: buy or build?  

One of the most important challenges is deciding whether to buy or build. 

Buying a ready-made solution may save you time and resources in the short term but building a custom solution can provide you with a competitive edge and better align with your specific business goals and requirements. 

When deciding whether to buy or build, consider the following factors: 

• Budget: Buying a pre-built solution may be more cost-effective initially, as custom development can require significant upfront investment. However, building a custom solution may provide long-term cost savings and a higher Return on Investment thanks to improved efficiency and better functionality. 
• Competitiveness: Consider how a custom solution can give you a unique advantage over your competitors. Off-the-shelf products may be limited in terms of customization and may not fully meet your business needs. 
• Return on Investment (ROI): Evaluate the potential Return on Investment for both buying and building a solution. Consider factors such as increased productivity, cost savings, improved customer experience, and better scalability. 

Build 

By choosing to develop IoT applications yourself, you gain the freedom to build a custom solution tailored to your needs. You have all integrations and metrics under control. The disadvantage, however, is the slower time to market, and the wide scope of the project at every stage. Also, it’s troublesome to hire in-house developers with the set of competencies you need.  

Buy 

Using an off-the-shelf solution, you’ll see value faster, avoid the hassle of recruiting experts, and gain the provider’s built-in authentication mechanisms for data security to protect your data. Unfortunately, such a solution also has its drawbacks – you are limited to one platform, and if you don’t think this choice through well, it will bring you more trouble than it’s worth in the long run (including vendor lock-in). 

Buy and Build 

Both Microsoft and Cumulocity, leaders in IoT platform solutions, recommend taking advantage of the benefits of both approaches. With a solution known as Buy & Build, you get an IoT platform as a foundation and can customize it as needed. At the same, you minimize ownership and use proven secure solutions. This approach makes swift IoT adoption possible without companies having to build everything from scratch, saving time and resources.  

Also read: Legacy systems modernization  

Understanding IoT security challenges for devices 

One of the biggest IoT challenges is addressing these challenges when it comes to managing IoT deployments. Many IoT devices often lack standardization and may have weak IoT security solutions, making them vulnerable to privacy issues. IoT adoption means connecting the world of hardware and software, using the best available practices and knowledge. IoT consultants’ services may prove indispensable in this regard. 

Security issues of connected devices  

Security issues are an area that companies are paying close attention to, as malfunctioning and unauthorized access to devices can have dire consequences – for production, logistics, or the safety and lives of people.  

Take, for example, medical devices that dispense drugs. As the IoT revolution goes on, the healthcare sector is more and more reliant on connected devices. According to a 2022 Juniper Research study, by 2026, hospitals worldwide will deploy nearly 7.5 million connected medical devices. 

The most common risks pertain to IoT exploits, malware and unauthorized access, and breaches associated with human factors.  

We wrote about potential scenarios and risks in the article: Security of IoT devices.  

Exploring 5 major vulnerabilities in IoT devices 

Getting to know potential vulnerabilities in IoT devices is becoming increasingly important as IoT appliances continue to evolve.  

1. Weak authentication methods – passwords that are easy to guess pose the risk of unauthorized access to industrial devices. 

2. Insufficient network security – so-called Man-in-The-Middle Attacks (MITM) are aimed at compromising vulnerabilities between the communication protocol and services running on IoT devices.  

3. Insecure ecosystem – components such as web, backend API, cloud, or mobile interfaces should be secured with trusted and reliable encrypting methods.  

4. Not keeping components up to date – software dependencies or legacy systems may pose a risk to the entire IoT ecosystem.  

5. Privacy concerns – data stored should comply with privacy regulations (GDPR or CCPA) to avoid legal consequences.  

The importance of encryption in securing IoT data 

Encryption plays a crucial role in securing IoT data due to the privacy issues in IoT and the fact that IoT devices can be exploited without proper security and privacy measures in place. 

To secure data, IoT developers use synchronous (singular cryptographic key) and asynchronous algorithms (multiple keys). The best-known methods are:  

1. Advanced Encryption Standard (AES) – an encryption algorithm used by security agencies and organizations worldwide. It is considered easy to implement and apply in many IoT security areas.  
2. Triple DES – successor to the DES algorithm (no longer in use). Often used for payment systems and FinTech solutions. 
3. RSA – commonly applied in SSL/TLS certifications, encrypting emails, and cryptocurrencies. 
4. DSA (Digital Signature Algorithm) – introduced in the 1990s by the National Institute of Standards and Technology (NIST). In simple terms, it allows you to generate 2 digital signatures that enable the receiver to authenticate the sender. 
5. Blowfish – freely available, used for safeguarding online payments and user password protection.  

Authentication methods for IoT devices 

Authentication methods for IoT devices are vitally important for ensuring the security of the entire IoT ecosystem. With billions of connected devices transmitting and receiving data, with weak authentication methods, network security and personal data are at stake. 

The secret to securing connected devices is access control. Device authentication means ensuring that any device willing to connect to the network must fulfill a set of strict criteria. Possible authentication methods include: 

• Single/one-factor authentication 
• Two-factor authentication 
• Three-factor/multi-factor authentication  
• Cryptographic authentication  

Summary: Overcoming Industrial IoT challenges 

The landscape of Industrial IoT is rapidly evolving, with billions of IoT devices being deployed in various industrial sectors, from logistics, to healthcare, to manufacturing. As IoT solutions become more widespread, new challenges arise in security practices and security audits to ensure IoT devices are protected. IoT manufacturers must prioritize safety and implement adequate solutions to secure their existing and new IoT devices.  

This can be achieved by using proven solutions and partners who have a track record of successful projects. The advantages are access to talent, shortening time to market and the ability to consult in the context of IoT software implementation. The proper planning of all activities and market research is a step that cannot be overlooked for long-term IoT undertakings where you need to carefully think through the entire IoT infrastructure and its vulnerabilities.  

Elevate Your Application Development

Our tailored Application Development services meet your unique business needs. Consult with Marek Czachorowski, Head of Data and AI Solutions, for expert guidance.

Schedule a meeting

But IoT continues to become more intelligent, evolving from devices that simply collect data to those that capture, analyze, and action it from any part of your network. In this article, we look at IoT Edge computing, including what it is, the benefits it can drive, and how to get started immediately by using Microsoft Azure.

Let’s get into it!

What is IoT Edge Computing?

The Internet of Things (IoT) isn’t a new concept. We’ve been connecting a physical IoT device to our networks for ages to capture and transfer data. Once that data is captured, it’s transferred to a central computing device for processing and actioning, either by Artificial Intelligence or by humans. 

Where IoT Edge computing makes a leap forward is moving that processing power to the edge of the network, putting it closer to the IoT device that’s capturing the data. While a central system manages the modules deployed to the edge of the network, the clever work happens closer to the Internet of Things devices.

This helps speed up the end-to-end process, de-risk your architecture, reduce latency, and provide a better user experience. This is especially important when processing data at scale, as a distributed set of resources that execute locally on those devices spreads the load and reduces the risk of failure. 

Also read: Cloud computing trends 2023-2025

Edge Devices vs Standard IoT: Connectivity, Analytics & More

We know the high-level differences between a standard IoT device and an IoT edge device, but let’s get into a more detailed capabilities comparison to truly understand the difference. 

• Connectivity – Standard and Edge IoT devices largely operate in the same way, sitting as part of your wider network, connecting through an IoT gateway. The key difference is that because services for processing run on each IoT Edge device, many of them can also operate offline providing stronger analytics at the edge of your network.
  
• Performance – While it’s hard to compare the performance like-for-like, IoT devices collect and send data incredibly efficiently and pass it back through an IoT hub for processing. When you deploy an Edge device, you deploy processing capability with it, meaning it can analyze and execute data-driven business logic at the edge of the network. 
  
• Cost – Again, it’s tricky to compare the like-for-like cost of IoT and Edge IoT as they do slightly different things. When using standard IoT devices, the processing power is centralized in your IT network, creating large volumes of central hardware. Edge systems with processing capabilities mean more hardware is placed evenly across the network. While this may be more costly in the short term, if you expand your Edge IoT capability, you can achieve economies of scale and store data in your edge locations rather than in more expensive central data centers.
  
• Resilience – Because IoT Edge supports local, offline processing, it offers greater resilience than standard online-only IoT devices. This is especially useful if your mobile IoT devices connectivity can’t be guaranteed, such as with self-driving cars. 

The Benefits of Intelligent Edge Devices and IoT Architecture

If you’re looking for a competitive edge in your IoT strategy, edge computing brings many benefits that can help you set yourself apart. Here are some of the most common benefits those who run IoT edge devices see in their organizations.

1. IoT Edge Agents sit on the network edge, putting the devices closer to the end user and their requirements. This means you are more likely to meet their needs and keep your teams and customers happy.
2. Because the processing power is local to the device, IoT Edge runtime is often faster than a standard IoT device, leading to faster decision-making and data outputs. 
3. IoT Edge architecture reduces network latency as less data has to be sent across the network to be processed.
4. Closer processing power enables modern technologies like Machine Learning and Artificial Intelligence to be deployed more widely than in a traditional cloud computing environment.
5. IoT Edge devices actually increase your broader IoT security as they store data locally, meaning less IoT data has to be sent across your wider network.
6. Because Edge data can operate offline, IoT applications have a higher uptime, ensuring IoT data is constantly being captured, analyzed, and actioned.

How Does Azure IoT Edge Work? Leverage Microsoft IoT Hub to Create an Intelligent Edge

Microsoft is one of the leading suppliers of Edge IoT technology, leveraging its Azure IoT hub to help customers generate and use more data than ever before. Here’s how it works: 

• Microsoft allows you to use IoT Edge devices as containers that run Azure services in any part of the IoT network. That means you can deploy critical third-party services, such as Azure stream analytics, exactly where your organization needs them. 
• But this doesn’t have to be a blanket approach. Instead, you can deploy different IoT edge modules exactly where you need them, applying the right business logic to each business department. Each device may need different requirements, whether that additional processing power, the ability to hold large amounts of data, or any number of Azure IoT Edge security enhancements – with Azure IoT, no matter the individual requirement, each device can be tailored to meet your needs.
• But, all of that distributed hardware needs management. All of your Azure IoT edge devices connect to a central IoT hub allowing them to be managed in one place. With Microsoft’s Azure Monitor, you can even be automatically alerted to issues within any application deployed to IoT devices. 
• For Microsoft’s customers, this means a simplified development environment, reduced IoT costs, and more reliable service in areas with intermittent coverage!

Getting Started With Your IoT Solution: Deploying, Managing, and Securing IoT Edge Devices

If you like what you’ve read about IoT edge devices, you’re probably wondering how to turn your new-found ambitions into a reality. Here are the first three steps we recommend to help you get started. 

1. Consider your current IT position. Before you start thinking ahead into the future, you need to have a clear picture of where you are today. Are you operating on-premises or utilizing cloud services? Do you have a lot of technical debt? Are you failing to meet the service demands of your business? Whatever your situation, understanding your strengths, weaknesses, and areas for improvement will help you plan your IoT edge journey.
   
2. Identify where IoT edge can help. Whether it’s helping you to capture more data, send data to the cloud more efficiently, build better network resilience, or simply futureproof your computing model, as we’ve seen in this article, there are many benefits IoT edge brings over typical IoT sensors. Once you know your own business and have identified your areas for improvement, look for where edge applications can help improve your IT capabilities. 
   
3. Partner with an expert. Companies that see the best results collaborate with experts to help guide them along the journey. As a Microsoft Global Gold Partner, at Inetum, we’re perfectly placed to help you take the first steps via Azure IoT Edge. Through our consulting offering, we’ve helped hundreds of companies across the world build an IoT strategy that better connects them with their data at every point across their network. 

So, if you want to take your data strategy to the next level, contact our Consulting teams today to hear how we can help you maximize the power of IoT Edge. 

Jump to:

1. 1. Smart devices everywhere
2. 2. Industrial IoT (IIoT)
3. 3. Where is the IoT market heading today?
4. 4. Internet of Things and automation
5. 5. IoT and the data
6. 6. Important questions on IoT and security
7. 7. How do we improve security?
8. 8. Are companies a target of IoT attacks?
9. 9. How will the IoT world evolve in the future, together with its security?
10. 10. Examples of EDGE IoT
11. 11. Summary

Smart devices everywhere 

Smart devices have already transformed our daily life. This is not only the case of consumer electronics, but the global digital trend followed by industry leaders and enterprises. The statistical consumer is familiar with smart house systems, wearables tracking our fitness activity and overall condition as well as specially designed solutions for precise measurement of glucose or oxygen levels in our blood. 

Industrial IoT (IIoT) 

The industrial sector gained a significant boost of awareness towards automation of processes and development of innovations, both heavily depending on IoT combined with e.g., Artificial Intelligence or Cloud. As an example of such innovation, we can use an autonomous vehicle, which is a great example of multi-purpose applications taking advantage of AI and IoT. 

Let’s take a look at electric cars, which can be spotted way more often than self-driving cars. Since they need to be charged, drivers will be able to use a growing network of publicly available charging points. After authorization, IoT will allow using smart and secured services like e.g payment services. 

We can name other cases and examples like: 

• intelligent manufacturing floors for the production sector, 
• smart metering solutions for the utility providers,  
• or this could be a way to improve the efficiency of green energy generation with predictive maintenance for wind turbines.  

The list of IoT-driven business ideas is never-ending. 

Understand (and overcome!) the challenges of building smart products

Download our whitepaper and learn how to shorten development cycles and deliver better customer experiences

Get it now!

Where is the IoT market heading today? 

The Corona crisis in 2020 turned our way of living and working upside down. On one hand, we had to deal with lockdown, on the other, we experienced the acceleration of digitalization like never before. New needs have arisen: 

• Monitoring of the coronavirus outbreak 
• Preventive actions 
• Drawing conclusions based on massive amounts of data from various sources e.g. our mobiles, which say a lot about our habits, places we visit, people we meet, and the way we live. The data were collected on a scale never experienced before. 

Internet of Things and automation 

From the perspective of industry, the reasonable way was to use technology like IoT to automate processes and work on the development of self-organizing business not depending on human interaction, especially during the lockdown. 

IoT vendors responded to the increasing demand with extended offerings not only focusing on hardware but also software to make the best use of the data gathered or exchanged by the devices. However, before we can reach this point the collected data need to be stored, explored, aggregated, and optimized. This has been the main challenge of the digital transformation, we are currently witnessing. 

IoT and the data 

IoT devices collect enormous amount of data about us. You may wonder: Who can access such data? Is it dangerous for us, as the users of these devices? 

From everlasting, human lives in compromise between his life comfort zone and safety. Thanks to cloud solutions we have started sharing more data about ourselves. For example, we have been sharing the information, where we are, what we buy, who we meet with, etc. It is because, the cloud and its applications simplify our daily life. 

Read also: Introduction to the world of Azure IoT 

 Important questions on IoT and security  

Let’s put another example here: keyless car opening. Many of us simply cannot live without this functionality nowadays. Slowly, we do forget what the car-key is, although we can hear about potential danger caused by similar “remote” solutions. 

All in all, correlated data simplifies our life, however it is dangerous from the potential cyberattack point of view. That is why, it is so important to be aware of whom and what data is shared. By the way, it is enough to realize our common sense and self-awareness of which data we share and which not. Because we do not realize the fact that most often trivial, uncorrelated data, in combination with other data (e.g. taken from social-network channels), can generate a fairly accurate profile of our behavior, preferences and habits. This in turn helps a potential criminal to prepare an attack against us. That is why it is so important to constantly work on mechanisms that allow us to secure information about us in a proper way. 

How do we improve security? 

First of all, we should be careful with whom and what data we share. It is also important if a given organization is able to maintain transparency in relation to: what data it collects, for how long, or does it allow us to clean this data at any time upon our request? 

Another question is, whether this data is personalized or anonymized. For example, Google map does not need to store the personal data on Mr. Kowalski (his personal profile and his account details), when taking measurements on the traffic jam on the highway. 

Read also: IoT Security: The Complete Guide to the Internet of Things (IoT) Security For Your Business

Are companies a target of IoT attacks? 

Single devices in use by are not any more the target for the cyberattacks. The hackers focus their activities more often on entire organizations collecting this data or big companies. A data leak from such organization destroys its social reliability, which in turn may even cause shutting down its business at all. 

This is already a better target for entire hackers’ groups, because it triggers a wish for larger amount of money to extort. 

Therefore, such companies that are also IoT data aggregators have to continuously look for security solutions. Decentralized systems (such as blockchain) are one of the alternatives to secure the data. The decentralization of data collection points, any kind of protection against data modification (without leaving a sign of hackers’ activity) seem to be the right direction this IoT world should evolve. Is it the only one? Probably not. 

How will the IoT world evolve in the future, together with its security? 

First of all, let’s take a note what is the difference between the IoT we have used so far and the IoT we have been developing for the future perspective. The trends we have been developing drive towards dynamic decentralization, where IoT devices communicate directly with each other without any intermediate point (human, DataCenter). The devices inform each other about certain local events. Such events can affect the inter-operation of the other devices in given geographic location. 

Naturally, they will generate more traffic among one another, and they will also analyze sensitive data within single device. Based on their own analysis, the devices will autonomously take the decisions and trigger appropriate automatic actions. It will all take place in the device itself. It does not have to go to a central server or cloud. So we will evolve towards so-called EDGE Computing, or EDGE IoT. 

Examples of EDGE IoT 

One of the examples could be a wind turbine (one of the projects we are currently working on). Such a turbine measures in parallel the weather conditions and autonomously can stop itself if the weather gets down (hurricane)

Another example, Tesla can detect holes in the road or local icing and store this information not only for itself (EDGE), but to share this information with others (cloud). And such a hybrid cooperation between EDGE and CLOUD will develop very dynamically in the future. 

This is where enormous computing power is needed inside a car and enormous bandwidth of telecommunications networks for the connectivity purposes. So that the entire layer of Artificial Intelligence can operate safely in the car itself. 

What is more, in this business and technological domain security is a very extensive topic. 

Summary 

The examples we quoted above show that the IoT world will rather develop itself towards decentralized ecosystems. There will be introduced dedicated mechanisms to allowing for confirmation or authorization whether the data is actually sent from the device that we expect this data to come from. It will be obligatory to encrypt the data and verify its integrity, whether it has been modified anywhere on the way. The data will be sent only over the secure channels, and definitely not as a plain text, as it still happens. 

But while the benefits of commercial and consumer IoT are fantastic, for businesses across the globe, commercial IoT also comes with a lot of risk. Since the dawn of time, people have tried to steal what matters most to us, and cybercriminals now have many new and dangerous ways to exploit your business unless your IoT security is solid and secure. 

In this article, we’ll look at all things IoT security, including what IoT devices may live in your organization, why securing them is so important, the most common vulnerabilities, and how to secure them. Then, to finish, we’ll share how an IT partner like Inetum can help you build a top-notch IoT security strategy.

Let’s get started!

What Is An Internet of Things (IoT) Device For Your Business?

An IoT device is any network-based, physical item that isn’t a computer, mobile phone, or tablet. In the enterprise space, IoT devices can be deployed anywhere, helping to capture data that improves operational efficiency, drives new products or services, and enhances management decision-making.

Common commercial IoT systems include:

• Printers
• IP cameras
• Sensors
• Lighting
• HVAC systems
• Access control systems.

Why Is IoT Security Important? What’s The Cybersecurity Risk To Your Business?

While most organizations have strong IT controls and protocols, the security best practices of many IoT devices are unknown to many organizations. This presents a problem because when these devices are connected to your network, you expose your entire infrastructure. 

Expanding to an IoT network creates a whole host of IoT security risks. This is because you generate more visibility on your network, build more entry points, and creates new areas of vulnerability on top of your existing network security requirements. 

Put simply, the more connected devices and the greater the number of operating systems you have, the greater your risk of intrusion. That’s why your cybersecurity strategy needs to extend to include secure IoT tactics.

Understand (and overcome!) the challenges of building smart products

Download our whitepaper and learn how to shorten development cycles and deliver better customer experiences

Get it now!

The Top 3 IoT Security Issues to Be Aware Of

Like with your traditional IT infrastructure, cybercriminals across the globe are taking advantage of IoT vulnerabilities in organizations. While many of the attack paths are the same, they are some slight nuances to be aware of when it comes to Internet of Things security. 

Here are the three main areas threat actors looks to take advantage of: 

• IoT Exploits. With the rate of change and emerging nature of IoT, devices are riddled with unknown exploits just waiting to be taken advantage of. Classics such as network scanning, SQL injection, and buffer overload are still present with IoT devices, so be on the lookout for unknown security breaches.
  
• Malware. As soon as IoT devices became commonplace, criminals were writing malicious malware code to crack them. Unfortunately, Trojans, Ransomware, and Worms are still the main threat with IoT devices, with specific ‘IoT Worms’ one of the most common routes of attack for those wishing to gain unauthorized access. 
  
• Human Factors. As has always been the case, in many instances, humans are one of the most significant risks associated with IoT cybersecurity. Because many IoT devices seem small and insignificant, many organizations don’t encourage people to heighten their awareness, leading to phishing attacks and weak password breaches. 

Read also: Introduction to the world of Azure IoT  

IoT Security Best Practices: A 4-Step Approach To Protect Your Business

While the threats of an IoT attack are very real, the good news is, it’s not impossible to put controls in place to manage your risk against cyberattacks. 

Here’s a high-level approach for building out your IoT strategy and overcoming the majority of security challenges. 

1. Upskill your team. Even though IoT is a new technology, it’s not entirely unknown. Like all areas of IT, knowledge is power, so invest some time, energy, and money into upgrading your organization’s IoT cyber knowledge.  
   
2. Identify your IoT risks. This all starts by identifying your organization’s size, scale, and distribution of IoT devices. Understand where your attackers may try to gain access, and you can begin implementing measures to mitigate key risks to your organization. 
   
3. Take mitigating action. There’s no point knowing about risks if you don’t manage them. IoT devices aren’t particularly special, so tactics such as attack surface rules, enhanced encryption, extending your endpoint security and firewall systems, and regular firmware updates will really help. Again, there’s no need to reinvent the wheel, just mirror your existing network security approach into the IoT space. 
   
4. Detect and respond. As we all know, a cyberattack is a matter of when not if. Plus, with the growing number of IoT devices available on the market, your chances of an attack are only going to increase. Put processes and controls in place to detect when an attack, such as DDOS, is inbound, and ensure you have suitable ways to respond to protect your organization. 

Implement some of these security measures, and you’ll be operating secure IoT devices that allow you to leverage the benefits of new technology without exposing yourself to unnecessary security and privacy risks. 

How Inetum Can Help With IoT Security Solutions

While many of us are used to IoT devices in our homes, many organizations haven’t yet taken to leap to bring them into business operations. Or, if they have, they aren’t using them to their fullest potential. 

That’s where a technology partner, such as Inetum, can help. Access to enhanced data sets from IoT devices can help you: 

• Gain additional insights into customer behavior and preferences
• Unlock new products and services, driving increased revenue and profit
• Keep important assets safe and secure all year round
• Boost operational efficiencies by identifying waste and being alert to issues.

But as we’ve seen, you can only reap the rewards if you keep your business safe. Our team of IoT experts is on hand to help you keep your business safe by leveraging techniques such as micro-segmentation, network access control, secure gateway management, team awareness training, and many, many more. 

If you’d like to learn more about IoT deployment, implementation, or security, you can check out our upcoming 3-part IoT webinar series hosted by a collection of Inetum’s IoT experts and Digital Transformation consultants. 

Get yourself signed up by clicking the link on our dedicated webinar page where we will keep you updated you on upcoming online events: https://webinars.nearshore-it.eu/.  

We’re looking forward to seeing you there!