Abstract
Modern cyber threats are characterized by a high degree of complexity and variability, which makes the task of protecting corporate networks from confidential data leakage especially relevant. Traditional approaches to information security are losing their effectiveness when faced with attacks based on anomalous user behavior and the use of covert communication channels. In this regard, the role of next-generation firewalls (NGFW) and intrusion detection and prevention systems (IDS/IPS) is increasing. This study is aimed at exploring the possibilities of integrating NGFW and IDS/IPS with machine learning technologies to enable intelligent analysis of network traffic. As part of the practical component, a module was developed in the Python programming language, based on the Random Forest algorithm, which provides automatic threat classification. The module was tested to see how well it works when people try to attack it. This was done to find out if it is really useful in the world. This work is, about keeping information safe and secure using artificial intelligence. We looked at the problems we are facing now with keeping data safe and we tried to come up with some solutions.