## 2 Authenticity Management in Normative Systems and Food Safety Standards  
In the era of globalized supply chains, food authenticity has become one of the key areas of quality management. It is understood as the consistency of the declared characteristics of a product with its actual properties, including geographical and botanical origin, production method, ingredient integrity, and the absence of intentional consumer deception [3; 21; 22]. For the honey sector, characterized by high commercial value, complex supply chains, and a high risk of fraudulent practices, this issue is particularly significant. Within normative systems and food safety management standards [23; 24; 25], the requirement is to identify and assess the risk of fraud. One of the tools supporting these efforts is the VACCP (Vulnerability Assessment and Critical Control Points) framework, which extends the classic HACCP approach by including an assessment of vulnerabilities to economic and reputational risks. VACCP involves identifying points susceptible to fraud, conducting risk analyses, and implementing appropriate preventive and corrective actions [26; 27]. However, it should be noted that not all advanced analytical techniques, such as LC-HRMS, NMR, or DNA-based methods (MDA), are universally accepted as standard methods within these systems. This is due to the lack of fully harmonized procedures, analytical complexity, and variability in results, which can depend on laboratory conditions and sample type. Additionally, some of these methods are not yet formally recognized by regulatory bodies, as highlighted by industry organizations such as FEEDM [28] and the European Commission [12]. The effectiveness of authenticity management largely depends on the quality of input data, the availability of current market analyses, supply chain transparency, and the degree of integration with other quality system components [29; 30]. Key factors in this context include the following: traceability, which is the ability to track the origin of a product throughout the supply chain; documented verification of origin; supplier oversight; and validation of applied fraud detection methods [31]. A summary of the main categories and components of authenticity management systems is presented in Table 1.  
The modern scientific literature emphasizes that effective authenticity management cannot be limited to technical aspects alone. Equally important are organizational factors, such as management commitment, the maturity of organizational culture, staff competencies, and effective communication with stakeholders [32,33]. In this context, the concept of \"food integrity culture\" is gaining recognition, reflecting an organization's sensitivity to fraud risks and its ability to proactively manage them. Despite the growing awareness of this issue, the implementation of VACCP systems still faces several challenges. Among the most frequently reported barriers are the lack of standardized methods for authenticity validation, limited access to analytical tools (especially in small- and medium-sized enterprises, SMEs), low operational awareness among employees, and poorly adapted risk assessment checklists [26,27,34,35]. Additionally, there is a need for broader use of predictive tools, such as risk modeling based on digital data and better integration of information from documentation and laboratory analyses. In this context, tools supported by Industry 4.0 technologies, particularly artificial intelligence (AI), are gaining importance. Automated analytical techniques, such as pollen analysis, spectroscopy, and predictive modeling, can significantly improve the objectivity, speed, and repeatability of authenticity verification, while also supporting real-time decision making in quality management. The integration of data from various sources (traceability, chemical analyses, risk models) is currently one of the most promising directions for the development of modern food quality systems.