Modelos de Inteligencia Artificial para prevención de ataques cibernéticos en organizaciones

Abstract

The academic essay titled "Artificial Intelligence Models for Preventing Cyber Attacks in Organizations" addresses the growing relevance of artificial intelligence (AI) in defending against cyber threats in the organizational context. A variety of AI models used for attack detection and prevention are examined in detail, highlighting their practical applications and effectiveness in protecting critical systems and data. Throughout the essay, the different AI techniques are delved into, such as machine learning and predictive analysis, as well as their ability to identify patterns and anomalies in network traffic and user behavior. Additionally, the challenges and limitations inherent in implementing these models are explored, including the need for high-quality data, interpretation of results, and management of false positives. Strategies to mitigate risks are also discussed, such as collaboration between security teams, constant updating of systems and cybersecurity awareness. However, effectively implementing AI models in preventing cyber attacks is not without challenges. One of the most significant challenges is the need for high-quality data in sufficient quantity to train and continually improve AI models. The lack of labeled data and the presence of noise can hinder model performance, highlighting the importance of data quality in AI-based cybersecurity.