WISe Artificial Intelligence

WISeAI.IO uses sophisticated algorithms to monitor Cybersecurity and Digital Identities for people and objects. The machine-learning tool is designed to examine how humans and objects use their computers, mobiles and digital identities when they connect to the Internet, thus protecting users against ransomware, sophisticated malware and the like.

WISeAI will be able to detect abnormalities by interpreting the flow of information collected by the sensors secured by WISeKey’s Secure Element being strongly authenticated as provisioned by WISeKey Root of Trust (RoT) now reinforced with post-quantum technologies.

AIoT integrates semiconductors, smart sensors, IoT systems, Artificial Intelligence and a data cloud to deliver to customers a unique offering to power innovation and digital transformation. Using WISeKey’s cybersecurity technology and IoT network, data will be collected where it can be processed and acted upon in real time in a highly secure environment.

AIoT is the brain that will power the nervous system of the network of IoT objects operating in the WISeKey Ecosystem, which currently connects over 1.6 billion IoT devices secured with WISeKey’s VaultIC.

By integrating Generative AI, WISeAI increments its ability to learn from patterns and data to create new and original content or data. This technology has the potential to significantly impact how autonomous (IoT) devices function as Generative AI can be used to create intelligent and autonomous devices that are better at adapting to different scenarios. With the use of Generative AI, autonomous devices can learn and adapt to their surroundings and environments to make more informed decisions. E.g, a self-driving car that uses Generative AI can make better decisions on the road, such as avoiding obstacles and responding to changing road conditions.

Generative AI can be used to create more personalized experiences for users of IoT devices. For instance, smart home devices can learn the user’s preferences over time and make recommendations or adjustments accordingly and to improve the efficiency of autonomous devices. By analyzing data and patterns, Generative AI can help devices make more accurate predictions and optimize their performance. This can lead to better energy consumption, cost savings, and reduced downtime for maintenance.

In what relates to WISeKey Cybersecurity , Generative AI can be used to improve the overall security of autonomous devices. With the use of Generative AI, devices can learn to identify and respond to potential security threats and vulnerabilities, thus making them more secure and less prone to hacking and have a significant impact on how autonomous (IoT) devices function by creating devices that are more intelligent, personalized, efficient, and secure.

Generative AI can be used to improve the overall security of autonomous devices by detecting and preventing attacks that are difficult to detect using traditional rule-based systems. Generative AI refers to a type of machine learning that involves training models to generate data that is similar to a given dataset. It can improve security by creating adversarial examples. Adversarial examples are inputs that are intentionally designed to cause a machine learning model to make an incorrect prediction. By generating and testing adversarial examples, researchers can identify vulnerabilities in a machine learning model and develop countermeasures to prevent attacks.

Generative AI can be used to improve security is by generating synthetic data to train machine learning models. Synthetic data is data that is artificially created using algorithms. By using synthetic data, researchers can train machine learning models without having to rely on real-world data, which may be limited or sensitive. This can improve the accuracy of machine learning models and reduce the risk of attacks that exploit vulnerabilities in the data.

It can also be used to detect anomalies in data, which can help to identify potential security breaches. Anomaly detection involves training machine learning models to recognize patterns in data and alerting security personnel when those patterns are disrupted. This can be used to identify and prevent attacks that are not detected by traditional security systems.