From my point of view, there are many different types of systems in which you can easily integrate AI to increase the security of your system.

For instance, if you want a practical example, you can have a look at a recent publication in which I am one of the authors: Article Security at the Edge for Resource-Limited IoT Devices

Integrating AI into cybersecurity systems presents several significant challenges, including the need for high-quality, large-scale data to train accurate models, which is often difficult to obtain due to privacy concerns and the rarity of certain attacks. Adversarial attacks, where attackers manipulate AI inputs to deceive systems, further complicate reliability. The complexity and "black-box" nature of many AI models hinder interpretability, making it hard to trust or debug their decisions. Additionally, integrating AI with legacy systems, ensuring scalability, and keeping up with the dynamic threat landscape are resource-intensive tasks. Regulatory compliance, ethical considerations, and the high costs of development and maintenance also pose barriers. Finally, the shortage of skilled professionals who understand both AI and cybersecurity slows adoption, while issues like false positives and negatives can undermine system effectiveness and lead to alert fatigue. Addressing these challenges requires ongoing research, collaboration, and investment in both technology and talent.