In the contemporary digital environment, data quality has become a crucial element affecting the reliability and effectiveness of cybersecurity systems, anti-fraud mechanisms, and artificial intelligence solutions utilizing Small Language Models (SLM). The implementation of a multilevel data quality scoring methodology, covering comprehensive monitoring from basic infrastructure checks, statistical analysis, primary data quality assessments, behavioral anomaly detection, to synthetic data testing, ensures effective and timely identification of data anomalies and inconsistencies. Poor data quality can significantly compromise critical systems, leading to operational disruptions, overlooked cybersecurity threats, financial losses, and increased risks. A systematic approach integrating accumulated scientific knowledge on data quality assessment algorithms, correlation engine technologies, and scoring mechanisms specifically designed for anti-fraud systems substantially enhances the effectiveness of data management. Comparative analysis with existing international data quality evaluation methodologies clearly demonstrates the advantages of the proposed multilevel scoring approach in terms of accuracy, practicality, and reliability, making investment in robust data quality management a strategic priority in today’s data-driven landscape.