Keywords
artificial intelligence
cybersecurity
intelligent algorithms
machine learning
predictive models
How to Cite
Abstract
The objective was to determine the relationship between artificial intelligence predictive models and protection against advanced persistent threats (APTs) in the Lima banking system in 2024. A secondary source analysis was conducted on AI predictive models that detect persistent cybersecurity threats such as malware. A survey was administered to a sample of 79 employees from banking institutions in Lima. The research is non experimental, quantitative, and cross-sectional. The results showed improved security system performance in the banking sector following the implementation of predictive models in areas such as APT detection, mitigation, and prevention, strengthening cybersecurity in a critical environment. These findings highlight the models' usefulness in more accurately predicting the impact of attacks on financial institutions. The survey revealed that the majority of participants believe AI predictive models contribute to protection against APTs and optimize problem-solving, indicating a favorable trend toward the implementation of AI-based tools in the banking system. The application of predictive AI models improves the resilience of the banking system against APT attacks, as their ability to optimize processes enhances resistance and has an effect on the evolution of protection against APT attacks, giving them adaptability to new threats as they improve and become more sophisticated.
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