THE INFLUENCE OF NEURAL NETWORKS ON THE DEVELOPMENT OF CYBER SECURITY IN THE CONDITIONS OF REGULATORY CHANGES

Abstract

The article studies the influence of neural networks on the development of cybersecurity in conditions of constant changes in the regulatory field. In today's digital world, where the complexity and frequency of cyber attacks are growing rapidly, traditional security methods are becoming insufficient. Neural networks, as one of the key technologies of artificial intelligence, open up new opportunities for improving the efficiency of cyber defense systems by automating threat detection, anomaly analysis and attack prevention. The integration of neural networks with other emerging technologies, such as blockchain and quantum computing, opens up new horizons for creating more sustainable systems. However, challenges such as adversarial attacks, opacity of algorithms (the "black box" problem) and compliance with regulatory requirements, in particular GDPR and ISO 27001, require special attention. The study also examines the ethical and legal aspects of using neural networks in cybersecurity, emphasizing the importance of developing explanatory artificial intelligence (XAI) and maintaining human control for safe and ethical implementation. The article concludes that neural networks are a promising tool in the fight against cyber threats, but their effectiveness will depend on the ability of organizations and states to solve privacy problems, ethical issues and technical vulnerabilities.