The article provides a comprehensive comparative analysis of methods, technologies, and modern approaches to the use of speech recognition and natural language processing (NLP) technologies in the context of national security and information security. The key aspects of the use of technologies for monitoring communications, detecting suspicious activity and application in the field of intelligence and counterintelligence, the role in ensuring cybersecurity, the possibilities of biometric identification by voice, ethical and legal aspects, and technological challenges are considered. The problem statement focuses on the challenges associated with the widespread adoption of speech recognition and NLP technologies, in particular, the lack of accuracy of algorithms, which creates risks to the reliability of security systems. The author also emphasizes the importance of addressing ethical and legal issues related to the privacy of citizens and the possible misuse of technologies for mass surveillance. The paper provides examples of systems for cybersecurity purposes, such as mass listening and analysis systems, targeted monitoring systems, social media analysis platforms, biometric identification systems, and others. The results section of the study presents a high-level structure of threat protection systems that covers threat channels and levels of protection. The complexity of modern threats that can integrate into several channels simultaneously, in particular using voice information, is considered. The author details the place and role of voice information in the structure of threat protection, emphasizing the importance of integrating various systems and platforms to ensure comprehensive security. Two approaches to building a security system that works with voice information are considered: aggregation of the maximum possible information from existing systems and creation of a system for each specific problem. A comparative analysis of these approaches is carried out, their advantages and disadvantages are identified, and the limitations and risks of using voice recognition methods are described, including the reliability and accuracy of technologies, the availability of data for training models, the cost of implementation, issues of confidentiality and privacy, data security, use in military and intelligence activities, ethical issues, and the risks of voice fraud and artificial voices.
