Objectives:
The primary objective of this article is to discuss the potential application of solutions based on artificial intelligence and machine learning for detecting and countering threats in cyberspace. It describes processes through which AI can enhance the performance of security systems by improving threat identification, enabling automation, and predicting attack vectors. Both technical aspects and methods for implementing artificial intelligence solutions are addressed. Furthermore, the article explores legal and ethical concerns, as well as the risks that may arise from the large-scale deployment of such tools.

Methods:
An analysis of the technical aspects of AI/ML was conducted, covering supervised and unsupervised learning, intrusion detection systems (IDS), EDR solutions, and user behavior analytics (UBA). A literature review and case studies (e.g., automated firewalls, phishing detection) were employed to evaluate the effectiveness of these solutions. Furthermore, a critical approach was applied to identify limitations, such as the quality of training data and the demand for computational power.

Results:
It has been shown that AI significantly accelerates threat identification and enables the prediction of attack vectors; however, its effectiveness depends on data quality, and it is susceptible to model hallucinations and false alarms. Hybrid teams consisting of humans and AI have been identified as a key development path, providing a balance between automation and human intuition in security management.

Conclusions:
Integrating human and algorithmic systems is essential for effectively countering modern cyber threats. There is an urgent need to establish coherent ethical and legal frameworks, as well as to invest in developing the skills of technical personnel and enhancing computational infrastructure.