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An increase in the volume of world trade and the resulting pressure placed on European Customs requires the implementation of new technologies. The European Commission has prioritised the development of digitisation and the acceleration of information exchange processes between customs officials and economic operators. This article theoretically and practically examines how this reform might impact customs control in the European Union and how it might contribute to its digital transformation. The attitudes of customs officials in the Republic of Bulgaria and their readiness to implement the imminent changes were investigated. The results suggests that improvements in digitisation and information exchange are interdependent and should be addressed simultaneously.
In the current digital environment, AI serves as a central force driving innovation across various sectors, including brand management. The selection of the topic for the paper is based on its relevance and the opportunity to examine different aspects of AI in the context of branding. In relation to the abovementioned, the current paper seeks to emphasize its importance in branding. The purpose of the paper is to explore the intersection of AI and branding by conducting a case study analysis, highlighting the ways in which leading brands, such as Netflix and Starbucks, utilize AI technologies in digital brand management to boost consumer engagement, and establish stronger connections with their customers thus securing a leading position in the market. The results of the study highlight the importance of AI in influencing the future of brand management, providing essential insights for marketers and companies aiming to leverage the complete capabilities of AI. Nevertheless, using AI in business introduces significant concerns related to privacy, transparency, and social responsibility, which are essential for sustaining consumer trust.
Public procurement allocates public resources; in the IT sector it requires complex specifications, rapid change and heightened compliance and audit risk. This paper reviews Bulgaria’s regulatory framework and control architecture (PPA and CAIS EOP) and presents a case study of Gamma Consult. Using desk research, CAIS EOP bulletin data (2022- 2025) and process mapping, we observe fewer participants, stable procedure volumes and a drop in EU-funded tenders.We highlight risk points in documentation, evaluation and contract performance. The paper outlines financial control tasks - threshold monitoring, budgeting, bid preparation and reporting - and notes copyright constraints affecting negotiated procedures. Recommendations stress transparency and traceability via improved CAIS EOP reporting/exports and clearer specifications and evaluation methods.
Recent innovations in Artificial Intelligence (AI) have led to the development of new paradigms in machine processing, shifting from data-driven, discriminative AI tasks to advanced tasks, enabled by generative AI. These technologies allow companies to manage extensive volumes of both structured and unstructured data and produce actionable insights with remarkable speed and precision. Through the application of generative AI, businesses can create predictive models that reflect market scenarios, customer behaviours, and competitive dynamics. The current paper researches the relationship that exists between generative AI and machine learning, emphasizing their roles in market analysis and business development. The two terms are often used in both scientific and media contexts, at times interchangeably, and at other times with distinct meanings. The paper reviews relevant literature and aims to offer a foundation for (interdisciplinary) discussions and future studies on the topic. As businesses adopt these innovations, it is essential that they focus on ethical considerations in order to create systems that deliver both value and integrity, which is crucial for achieving long-term success and sustainability in a business environment, influenced by AI. The paper seeks to be a vital resource for researchers, students, and practitioners seeking insight into the complex journey to data-driven decision making through (Gen)AI.
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.
