Tiny Tech, Big Impact : The Future of Safer IoT Innovations

(By Harish Kumar GS, Head of Sales, India and SAARC, Check Point Software Technologies)

As IoT devices proliferate and integrate deeply into our everyday lives, the demand for advanced, scalable security solutions across all organizations and industries has become critical. According to Market Research, The India Internet of Things (IoT) market was valued at USD 34.3 billion in 2022 and is expected to witness substantial growth. Projections indicate that the market will increase from USD 41 billion in 2023 to USD 171.7 billion by 2032, achieving a compound annual growth rate (CAGR) of 19.60% during the forecast period (2024–2032). This growth is largely driven by the rising demand for data-driven decision-making in the information technology (IT) sector, supported by increased government investments in advancing the country's IoT infrastructure.

Traditional security approaches often struggle with IoT devices' limited resources, which restrict their capacity to run comprehensive security controls. This challenge has paved the way for Embedded Machine Learning (Embedded ML), or TinyML, as a game-changing solution uniquely suited to address IoT’s security demands.

Embedded ML transforms IoT and embedded systems by enabling devices to perform data analysis and decision-making directly on the device. This local processing significantly reduces latency and enhances data privacy since information doesn’t need to be transmitted to the cloud. Beyond the benefits of smarter and more adaptive IoT devices, Embedded ML addresses the security limitations of resource-constrained systems by providing a more tailored, device-level intelligence that operates independently.

However, as IoT devices grow more “intelligent,” they also become more complex and potentially vulnerable to sophisticated cyber threats. Cybercriminals are now exploiting adversarial ML techniques to subtly manipulate input data, causing IoT devices to misclassify or malfunction without raising alarms. In addition, this could lead to IoT incorrect actions, think of misinterpreting readings or worse shut down.  Especially dangerous is OT environments such as Critical Infrastructures. Were downtime means service disruption.

Embedded ML as secret and invisible security weapon

Embedded Machine Learning (ML) harnesses the power of machine learning directly within small, low-power IoT devices, enabling them to detect and prevent threats locally on the device. Via embedding intelligence directly into IoT assets, Embedded ML addresses key security challenges and offers significant advantages across a wide range of industries.

One of the most compelling features of Embedded ML is its ability to create an “invisible security” layer, where IoT devices can autonomously “self-monitor” and protect themselves against new and emerging threats without human intervention. This invisible approach means that security measures operate quietly in the background, without the need for visible cameras or intrusive hardware, making it ideal for sensitive settings like hospitals, critical infrastructure environments where obvious security devices may be impractical or even disruptive.

For industries and organizations, this self-monitoring, low-maintenance defense architecture provides a powerful advantage, reducing the need for frequent manual updates or active oversight. Embedded ML’s ability to remain unseen is rooted in its seamless integration with device operations, quietly analyzing data and adjusting to threats as they emerge, thus creating an “invisible” but highly effective security layer.

Practical Example: Imagine a hospital equipped with IoT-enabled patient monitoring systems that use Embedded ML to detect anomalies in real-time, flagging potential issues without additional hardware. Unlike traditional solutions, which might require visible security cameras or external sensors, Embedded ML enables these devices to “self-monitor,” automatically adjusting to threats and safeguarding patient data without drawing attention. This invisible security capability allows IoT assets to function cyber resilient and as intended while providing discreet, real-time protection that integrates seamlessly into high-sensitivity environments.

Privacy at the Edge | How Embedded ML enables IoT Compliance

Regulations like the Cyber Resilience Act (CRA) in the EU and many others around the world mandate that sensitive data be processed securely and with strict privacy protections. Embedded ML allows for local processing, ensuring that data doesn’t need to be transmitted to centralized cloud servers for analysis. In the event of a data breach, regulations like GDPR impose strict penalties based on how an organization has handled security. Embedded ML enhances localized detection and prevention, meaning it can identify a breach or suspicious activity before sensitive data is transmitted or compromised. This proactive security measure reduces the risk of a breach, helping organizations stay compliant and avoid fines. Compliance in IoT environments can be complex, especially as the number of connected devices scales. Embedded ML lightweight footprint makes it easy to integrate into a large number of devices without significant overhead, allowing organizations to manage compliance across vast IoT networks efficiently. It ensures that security protocols are uniformly applied across all devices, making large-scale compliance efforts more manageable.

IoT Security 2.0 | Key Advantages of Embedded Machine Learning

● Local processing for immediate Threat Detection: Embedded ML models can detect threats in real-time by running directly on devices, reducing the delay in identifying and responding to potential attacks. This is critical for applications requiring quick threat detection and response, like smart home security and industrial monitoring, where latency can be a security risk.

● Cost-effective way to scale IoT security across legacy devices: Many industries are heavily invested in legacy devices that are outdated, lack strong security protections and are challenging to update. Embedded ML minimal processing and memory requirements mean that even older IoT devices can have a layer of intelligence added, without needing full hardware upgrades. This reduces costs while enhancing network-wide security, an especially valuable point for CISOs facing budget constraints or scaling challenges across large IoT ecosystems.

● Reduced Cloud dependency: Due to its ability to perform tasks locally, Embedded ML minimizes reliance on the cloud, which reduces bandwidth and power consumption. This localized approach is beneficial in scenarios with connectivity constraints. Offering this “off-the-grid” setup is ideal for monitoring in agriculture or wildlife preservation, autonomous vehicles or underground mining as normally many of these areas are unprotected. It also enhances data privacy, as sensitive information doesn’t need to leave the device.

● Reduced bandwidth usage: Processing data locally reduces the amount of data transmitted over the network, saving bandwidth, making Embedded ML suitable for network-constrained environments.

● Sustainability and energy efficiency: Embedded ML models are optimized to consume minimal energy, ensuring that battery-operated IoT devices maintain a long lifespan even while performing security tasks. This is essential in sectors like environmental monitoring, where devices are expected to operate for months or years without human intervention. This supports sustainability goals by conserving the IoT asset lifespan and lowering energy requirements.

● Autonomous Operation and resilience: In critical applications like industrial IoT (IIoT), Embedded ML allows devices to operate autonomously, identifying and handling irregularities without external input. This self-sufficiency is vital for remote or hazardous environments where human intervention is limited, enabling IoT devices to continue functioning even if disconnected from central systems.

● Facilitates adaptive learning: Embedded ML models can be trained and fine-tuned on-device, allowing edge IoT devices to adapt to changing environmental conditions. For instance, in smart agriculture, models can adjust to variations in soil conditions or weather patterns, making devices more responsive to real-world changes without needing constant reprogramming from a central server.

● “Human Element” of IoT Security: Embedded ML learns Human Patterns. Embedded ML can also learn and analyze human behaviour patterns, improving security by spotting anomalies. This might sound futuristic, but it’s practical: imagine smart locks that identify suspicious movement around a door or industrial systems that detect when human presence seems “off.” This adds a layer of behavioural analysis to IoT security and highlights how it can align with the “Zero Tolerance” security model by ensuring that only verified and expected behaviour is allowed.

The Edge Awakens | The Future of Self-Sufficient IoT Security

Embedded ML security applications hold tremendous potential for creating a safer, more resilient IoT ecosystem by providing rapid, energy-efficient and privacy-centered security solutions directly at the device level. However, as with any emerging technology, there are challenges. Cyber criminals may exploit Embedded ML models to avoid detection, posing risks. To mitigate these threats, ongoing R&D efforts are required to maintain integrity and robustness, withstanding adversarial attacks and tampering. ML-based IoT threats can be broadly categorized into two types: security attacks and privacy violations. Security attacks focus on compromising data integrity and availability, whereas privacy violations target the confidentiality and privacy of data. Key examples of these threats include the following three attack types.

1. Integrity attacks

Integrity attacks seek to manipulate the behavior or output of a machine learning system by altering its training data or model. Injecting false data, attackers can degrade the model’s accuracy and erode user trust, much like mixing substandard products with high-quality ones during inspections lowers overall credibility. In IoT, tampering with sensor data for predictive maintenance can mislead the model, resulting in incorrect predictions or improper maintenance actions that impact equipment functionality and reliability.

2. Availability attacks

Availability attacks target the normal functioning of ML-based IoT systems by causing disruptions or generating inaccurate outputs, leading to crashes, service interruptions, or erroneous results. Similar to traffic congestion or communication interference, these attacks overwhelm systems to prevent legitimate responses. For example, denial-of-service attacks on a smart home system can overload it with commands, rendering it unresponsive, while flooding sensor networks with excessive or erroneous data can delay or prevent timely decision-making.

3. Confidentially attacks

Confidentiality attacks target ML systems to obtain sensitive or private data, similar to a thief breaking into a secure vault or a hacker stealing personal information. In IoT, such attacks can lead to unauthorized access and leakage of sensitive data, threatening privacy, trade secrets, or even national security. Attackers may exploit side-channel attacks to uncover details from power consumption patterns or use model inversion techniques to reconstruct personal information, such as facial features from a facial recognition system’s output.

And then we have the attacks on the training data of IoT scenarios, attacks on the model itself . Looking ahead, we may see Embedded ML models with adaptive, self-healing capabilities, automatically recalibrating after breach attempts, further fortifying IoT security.

The impact of Embedded ML on smart edge computing lies in its ability to deliver intelligent processing directly to the edge, enabling IoT devices to operate autonomously, efficiently and securely. This enhancement improves the responsiveness, sustainability and scalability of IoT ecosystems. As Embedded ML advances, its role in smart edge computing will expand, fostering innovation in areas that demand intelligent, low-latency and privacy-focused IoT solutions.

Investing in Embedded ML is not only more cost-effective than traditional cloud-based IoT security methods but also reduces cloud dependency and bandwidth requirements, yielding substantial cost savings and enhancing ROI, particularly in large-scale IoT networks where cloud expenses can accumulate quickly. For organizations, adopting Embedded ML strengthens IoT security while also delivering operational efficiencies and sustainability benefits that align with the evolving demands of IoT security.

Embedded ML is transformative for organizations dealing with complex IoT compliance standards, as it provides local data processing, reduces data transmission, and offers real-time threat detection. This technology empowers businesses to address key regulatory requirements for data privacy, cyber security, and auditing, making it a scalable and efficient solution to secure IoT systems under strict regulatory demands.

In summary, Embedded ML represents a powerful tool for innovation in IoT security, offering cost savings, regulatory compliance, and enhanced protection for organizations. However, as we adopt this technology, it’s essential to rethink the principles of security, integrity, and transparency that underpin it. The future of IoT security lies at the edge, and investing in Embedded ML now, alongside continued research, will be key to ensuring it is implemented responsibly and effectively.