Artificial Intelligence in Cybersecurity: Applications, Challenges, and Future Developments

Cybersecurity is facing increasingly sophisticated threats, such as ransomware, complex DDoS attacks, and zero-day attacks. These evolving threats reveal the limitations of traditional detection, prevention, and response methods. In this context, the integration of Artificial Intelligence (AI) emerges as a promising solution. Machine Learning (ML) and Deep Learning (DL) algorithms enable enhanced detection of malicious behaviors, real-time anomaly identification, and the anticipation of cyberattacks before they cause significant damage. However, the adoption of AI in cybersecurity introduces new challenges, such as adversarial attacks targeting AI models, the lack of transparency in algorithmic decision-making, and the dependence on data quality. This document reviews the current applications of AI in cybersecurity, highlighting its contributions, limitations, and future perspectives. It also explores emerging solutions to counter zero-day attacks, the development of human-AI collaborative systems for adaptive defense, and the establishment of ethical and regulatory frameworks to ensure the responsible use of AI, aimed at protecting critical systems in the long term against increasingly sophisticated threats.