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  A DUAL-LAYER HYBRID SECURITY FRAMEWORK: ANALYZING DIGITAL MODELS TO ENHANCE CYBERSECURITY MANAGEMENT FOR START-UPS AND SMES

  The rapid evolution of generative AI is transforming cybersecurity practices by providing advanced capabilities for threat detection and response. For start-ups and SMEs, these technologies offer a cost-effective way to defend against sophisticated online threats without the need for massive capital investment. While AI facilitates the ... The rapid evolution of generative AI is transforming cybersecurity practices by providing advanced capabilities for threat detection and response. For start-ups and SMEs, these technologies offer a cost-effective way to defend against sophisticated online threats without the need for massive capital investment. While AI facilitates the identification of anomalies and malicious behavior, it also introduces new risks, such as deepfakes, zero-day exploits, and the potential for attackers to manipulate AI models themselves. The study examines the impact of large language models and artificial intelligence on the software and architecture of cybersecurity systems. The main types of attacks on artificial intelligence systems and the changes in the software development paradigm that result from them are being considered. To address the evolving threat landscape, this study proposes a Dual-Layer Hybrid Security Framework that integrates high-speed algorithmic detection with cognitive analysis. We present an algorithm for building a security system using LLM models. We also propose a software application tailored explicitly for the comprehensive analysis of network traffic while detecting cybersecurity incidents using Python and the Pandas library. Furthermore, it leverages artificial intelligence to provide a comprehensive approach to detecting and mitigating cybersecurity threats. This will enable start-ups and small businesses to manage their cybersecurity in-house.

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  DIGITAL DISTANCE AND TRADE DYNAMICS: THRESHOLD EFFECTS ON TURKIYE’S EXPORT PERFORMANCE

  This study investigates whether digital distance—defined as disparities in digital and technological capabilities between countries—constitutes a critical determinant of Turkey’s export performance in the evolving architecture of international trade. Moving beyond traditional gravity-based explanations centered on geographical distance, the ... This study investigates whether digital distance—defined as disparities in digital and technological capabilities between countries—constitutes a critical determinant of Turkey’s export performance in the evolving architecture of international trade. Moving beyond traditional gravity-based explanations centered on geographical distance, the article develops a multidimensional digital distance index derived from the Frontier Technological Readiness Index (FTRI) and the Economic Complexity Index (ECI). Using annual panel data for 108 trading partners over the period 2008–2022, the analysis integrates GDP and exchange-rate controls within a panel threshold regression framework, complemented by panel causality tests. The findings reveal a statistically significant single-threshold structure, demonstrating that digital distance exerts nonlinear and asymmetric effects on Turkey’s exports. While convergence in ICT infrastructure and financial access tends to enhance export performance, narrowing gaps in certain dimensions—particularly skills and R&D—does not uniformly yield positive trade outcomes. In fact, larger disparities in selected technological capabilities may generate temporary competitive advantages, consistent with technology-gap theory. Robustness checks incorporating economic complexity distance confirm these threshold dynamics. Moreover, causality results indicate predominantly bidirectional relationships between exports and digital dimensions, underscoring mutual reinforcement between trade and digital transformation. Overall, the results challenge the presumption that digital convergence universally promotes trade. Instead, they highlight the strategic importance of selective digital specialization and innovation-driven competitiveness. By conceptualizing and empirically validating digital distance as a structural trade determinant, the study contributes to the emerging literature on digitalization and international trade and offers policy insights for adaptive, data-driven export strategies in an era of technological disruption.

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  SUPPLY CHAINS OF PERISHABLE AGRI-FOOD PRODUCTS IN THE EAEU: CONCEPTUAL APPROACHES AND DEVELOPMENT OF LOGISTICS INFRASTRUCTURE

  The purpose of this study is to develop a conceptual approach to the formation of an integrated logistics platform for perishable agri-food products within the Eurasian Economic Union (EAEU). The methodological framework is based on systems and scenario analysis, SWOT assessment, comparative analysis of the logistics potential of EAEU member ... The purpose of this study is to develop a conceptual approach to the formation of an integrated logistics platform for perishable agri-food products within the Eurasian Economic Union (EAEU). The methodological framework is based on systems and scenario analysis, SWOT assessment, comparative analysis of the logistics potential of EAEU member states, and modeling of logistics chain efficiency under different levels of integration using panel empirical data for the period 2015–2023. Selected indicators of logistics infrastructure are employed to provide a descriptive assessment of the current state of agrologistics as of 2023. The modeling results demonstrate that coordinated investment policies and regulatory harmonization can significantly enhance the efficiency of agrologistics chains in the context of regional integration. The findings confirm that digitalization and the development of cold chain infrastructure are key determinants of logistics sustainability, while the coordination of regulatory and investment mechanisms substantially improves logistics efficiency across the EAEU member states. Based on the estimated parameters, a scenario-based forecast of agrologistics development up to 2030 is proposed. The study introduces a scalable model of an integrated agrologistics platform incorporating a cluster-based approach, a unified digital framework, and public–private partnership mechanisms adaptable to the specific conditions of EAEU countries. The scientific novelty of the research lies in substantiating a platform-based approach to cross-border agrologistics grounded in the synergy of digital and physical solutions, institutional coordination, and targeted infrastructure policy.