The Internet of Things (IoT) has entirely transformed the operations of industries, homes, and cities by making everything smarter, more efficient, and automated. The IoT connects devices that communicate over the Internet. From smart thermostats and wearables to automated factories, IoT-based solutions are pervasive. However, with the widespread growth of IoT devices, cyber threats have also increased at the same rate. In 2025, there are already over 18.8 billion IoT devices worldwide, which is expected to reach around 40 billion by 2030. This rapid expansion not only increases opportunities for hackers to exploit vulnerabilities but also demands proactive measures to secure the devices and the networks.

Artificial Intelligence (AI) plays an important role here. AI can quickly analyze large amounts of data, find unusual activity, and even take action to stop attacks before they cause harm. Using AI-driven security is now essential to protect the growing IoT network from new and more complex threats.

What Makes IoT Vulnerable to Attacks

Many IoT devices have weak security, and the reason for this is that they are often developed quickly and lack strong protections. According to IBM X-Force Threat Intelligence, over 50% of IoT devices have security vulnerabilities that hackers can exploit. Moreover, around 60% of IoT breaches are due to outdated software and unpatched firmware. Similarly, lots of devices still use default passwords, which make it easy for attackers to gain unauthorized access.

The variety of IoT devices, ranging from basic sensors to advanced medical equipment, makes it challenging to implement security measures consistently. As the number of devices increases, the number of entry points for hackers also increases, making IoT networks more vulnerable.

Key IoT Cyber Threats include:

  • Data Breaches: Cybercriminals target IoT devices to steal personal and corporate information. About one in three data breaches involve these devices, making them a significant point of vulnerability.
  • Ransomware: In certain industries, like healthcare and manufacturing, attackers lock IoT devices and demand a ransom for access. This type of cyberattack is becoming more prevalent as IoT devices are integrated into critical operations.
  • Malware and Botnets: Harmful software can infect IoT devices and turn them into bots for large-scale attacks. A well-known example of this is the Mirai botnet, which has been responsible for major disruptions to websites and online services.

The financial impact of IoT breaches is significantly high. According to a PSA certified report, each breach costs businesses an average of $330,000. Additionally, sectors subject to more strict regulations often face even greater penalties. Besides financial losses, around 78% of consumers say they would discontinue a company’s services after a major IoT-related breach according to the IoT Security Foundation. This highlights the severe reputational damage businesses have to experience due to IoT breaches.

Securing IoT devices and networks is therefore essential to protect privacy, maintain trust, and ensure the smooth operation of the increasingly connected smart environments.

The Role of Artificial Intelligence in IoT Cybersecurity

As IoT devices increasingly proliferate, conventional security approaches find it challenging to address the expanding risks and complexities. Here is why artificial intelligence is becoming essential for safeguarding IoT networks

Why Traditional Security Falls Short

Traditional security tools, such as firewalls and antivirus software, were developed for standard computer networks without consideration for the speed and scale of IoT networks. IoT devices continuously transmit vast amounts of data, but these older tools cannot process this data efficiently to prevent attacks before they occur.

One of the main problems with traditional security is signature-based detection. This method is suitable for only known threats. It cannot identify new or evolving attacks. Moreover, traditional tools’ fixed security rules are ineffective for IoT networks. Since IoT networks are constantly changing, these fixed rules cannot adapt to the rapid developments in IoT environments.

On the other hand, AI follows a different approach. It processes data in real-time and uses predictive techniques to detect threats before they cause damage. With AI, devices can learn from patterns and adapt to new, unknown attacks, thus ensuring a more dynamic and responsive defense.

How AI Transforms IoT Security

AI plays an important role in securing IoT networks. AI models can analyze data from all connected devices in real-time and identify unusual activity. This helps AI systems predict threats before they result in any harm and stop them early.

With predictive threat detection, AI analyzes past data to find patterns or unusual behavior that could signal an attack. This helps AI to act quickly and prevent a data breach. In addition, AI systems can automatically respond to threats and can reduce the need for human intervention. For example, if a threat is detected, AI can isolate the affected device or block harmful traffic, thereby stopping the attack.

Different AI techniques are used to secure IoT devices. Machine Learning (ML) is one of the most common. It helps AI learn from past data and improve over time. In IoT, ML can detect new attacks that have not been witnessed before.

Deep Learning (DL) is a more advanced form of ML. It can understand more complex data and detect hidden threats that simpler models might not be able to detect. This is useful in IoT networks, where threats can be challenging to detect.

Anomaly detection is another vital AI technique. It enables AI to identify unusual patterns within IoT networks, such as unexpected data transmissions. When such irregularities occur, AI marks them as potential security threats, facilitating quicker responses and enhancing overall protection.

Finally, federated learning enables AI models to learn from data across multiple devices without sharing sensitive information. This approach preserves privacy while still enabling AI to enhance security.

AI-Driven Approaches to Securing IoT Network

Below are some important AI-driven approaches currently being employed to strengthen IoT security.

Predictive Threat Detection and Anomaly Detection

AI systems continuously analyze the data generated by connected devices to find anomalies that deviate from standard behavior. For example, suppose an IoT device like a smart thermostat unexpectedly sends large amounts of data or connects to an unfamiliar endpoint. In that case, AI systems will detect this as suspicious activity and generate an alert. This preventive approach helps recognize possible threats at an early stage before they escalate into significant issues.

Automated Incident Response

Swift responses are essential in the domain of IoT security to avoid damage on a large scale. AI-powered Security Operations Centers (SOCs) automatically speed up the reaction time by handling several response tasks. When the AI system detects suspicious activity, it can immediately isolate compromised devices, block harmful traffic, and notify human analysts to investigate further. This automation not only speeds up the process but also reduces the workload of cybersecurity teams.

Adaptive Access Controls and Behavioral Authentication

In addition to automated incident response, AI improves access management in IoT networks. AI systems continuously track user and device behavior rather than just relying on fixed credentials. AI can promptly block or limit access if a user or device attempts to access resources from an unusual location or behaves suspiciously.

This approach is based on the zero-trust security model, which assumes no device or user should be trusted without proper verification. By dynamically adjusting access rights based on real-time behavior, AI helps prevent unauthorized access and keeps IoT networks secure.

Edge AI and Federated Learning for Privacy and Efficiency

Edge AI has become essential for timely threat detection and privacy protection. It involves processing data locally on IoT devices or nearby edge gateways. As a result, the time to identify and respond to threats reduces significantly. In addition, since the data does not need to be transmitted to a centralized server, edge AI helps preserve privacy as well.

Additionally, federated learning enables multiple IoT devices to train AI models using local data without sharing sensitive information. This decentralized method improves security while maintaining privacy. Moreover, federated learning enables AI to use collective intelligence to detect threats more accurately across the entire network.

The Rise of AI-Powered Cyber Threats in IoT Security

While AI is helping secure IoT devices and networks, cybercriminals are also using it to carry out more advanced attacks. Recently, AI-driven threats have become a serious concern for the cybersecurity domain.

AI-generated phishing Attacks are becoming more advanced and malicious. Cybercriminals use AI to create realistic phishing emails that look legitimate, making it difficult for traditional security systems to spot them.

Deepfake Technology is another tool being widely used by attackers. Hackers can create fake audio and videos that mimic trusted people. This is used in social engineering attacks, like fake wire transfers or leaking sensitive data.

In addition, adaptive AI-driven malware is a new kind of malicious software. It can change its behavior to avoid detection, making traditional security systems ineffective. This emerging threat demands more innovative and more flexible security measures.

5 Best Practices for Securing IoT with AI

The organizations should:

  • Map the IoT environment and identify all connected devices. Security efforts must focus on devices that handle sensitive data or are highly important.
  • Implement AI-based machine learning and anomaly detection tools to monitor IoT device behavior in real-time. This will help detect unusual activities quickly.
  • Enforce strict access controls using AI-powered behavioral analytics to continuously verify identities and adjust access based on risk levels.
  • Continuously retrain AI models with updated data to maintain detection accuracy and stay ahead of new threats.
  • Integrate AI security solutions that comply with regulations such as GDPR and CCPA, ensuring the protection of sensitive information and meeting legal requirements.

The Bottom Line

The growth of IoT devices brings numerous benefits but also increases security risks. Many devices have weak protection mechanisms, making them easy targets for attackers. Traditional security methods are insufficient to handle the fast and large-scale data of IoT networks. To that end, AI offers a better way by quickly finding unusual activity and stopping threats early. AI can learn from data, adapt to new attacks, and respond automatically, which helps protect connected devices and networks.

At the same time, attackers are also using AI to develop more advanced threats. This means security systems must keep improving and being flexible. Organizations should carefully know their IoT devices, use AI tools to observe device behavior in real time, control access strictly, and update their AI models often. Following these steps will help keep IoT networks safer and protect privacy and trust.