This article examines the role of generative artificial intelligence in the digital transformation of economic sectors, with a particular focus on assessing implementation efficiency and ensuring technological security. The study argues that generative AI is becoming a strategic tool for improving productivity, operational speed, decision-making quality, and innovation capacity across manufacturing, finance, logistics, public administration, and other sectors. At the same time, the article emphasizes that the benefits of generative AI are accompanied by serious technological risks, including data leakage, model misuse, privacy violations, unreliable outputs, and cybersecurity vulnerabilities. The research is based on a qualitative comparative analysis of recent open-access academic and institutional sources, including OECD, World Bank, NIST, and sector-specific studies. The findings show that the effectiveness of generative AI should be evaluated through a dual framework that combines economic performance indicators with technological security criteria. The article concludes that sustainable implementation requires not only digital infrastructure and organizational adaptation, but also strong governance mechanisms, human oversight, and continuous risk monitoring.

Uzbekistan has accelerated the digitalization of public services through legal reforms, infrastructure investments, and the expansion of unified service channels. However, international research shows that simply increasing the service catalog does not lead to meaningful transformation unless back-office interoperability, data governance, and user-centered design help reduce document burdens and procedural uncertainties. This paper presents a three-wave maturity model of Uzbekistan’s progress: (1) establishing basic infrastructure and internal digitization; (2) platform development and hybrid service delivery via a unified portal and one-stop shops; and (3) moving toward data-driven, proactive services based on life events. Using qualitative analysis of documents, institutional mapping, and benchmarking against OECD/UN/World Bank frameworks, the study links each stage’s enablers to expected outcomes like efficiency, transparency, and inclusion, as well as key risks such as data quality issues, interagency coordination gaps, cybersecurity and privacy threats, and uneven adoption. An analytical matrix highlights priority policy actions, including clearer data stewardship, enforceable interoperability standards, security-by-design, and institutionalized user experience (UX) standards integrated into administrative procedures and remedy mechanisms. Lessons from India’s digital public infrastructure show how reusable core components can accelerate scaling while also heightening governance requirements for accountability and trust.

The rapid development of internet technologies has made digital networks an integral part of modern life, making cybersecurity a pressing issue. The need to protect sensitive personal, corporate, and government data in the online environment is constantly growing. This study analyzes key cybersecurity threats, including data leaks, malware, and phishing attacks, based on publicly available scientific databases, and assesses the capabilities and limitations of existing security technologies. The results demonstrate that cyberattacks are becoming increasingly sophisticated, and traditional defense measures are not always sufficient. The conclusion substantiates the need to ensure future cybersecurity through integrated, proactive approaches, artificial intelligence-based solutions, and comprehensive strategies based on increased user awareness.

This article examines the transformative role of neural networks in econometrics and financial decision-making, emphasizing their influence on personal finance, automation, healthcare, transportation, and human-computer interaction. Neural networks, inspired by the structure of the human brain, have the potential to revolutionize these sectors by enhancing efficiency, accuracy, and decision-making capabilities. In personal finance, they can optimize budgeting, savings, and expenditure management through automated models such as the McCulloch-Pitts neuron. In healthcare, neural networks improve diagnostic capabilities and enable predictive treatment. The article also highlights the applications of neural networks in econometrics to analyze financial patterns, detect fraud, and manage risks more effectively. However, it also addresses the ethical concerns related to data privacy, security, and biases in algorithmic decision-making, stressing the importance of responsible development. Ultimately, it concludes that, despite the challenges, the benefits of integrating neural networks into econometric models and financial systems are substantial and indispensable for modern advancements.