<![CDATA[The increasing use of QR codes in daily life has created opportunities for cyber threats, such as the insertion of malicious URLs that are difficult for average users to recognize. This research aims to qualitatively analyze best practices in URL security detection extracted from QR codes, particularly focusing on artificial intelligence (AI) approaches and the integration of digital audit systems. A systematic literature review method was applied, drawing from internationally indexed publications and thematic observations about user behavior and threat detection technologies for phishing and malware. Findings indicate that AI-based detection systems effectively identify security threats earlier and more accurately, especially when combined with user education features and activity logging through the Wazuh/ELK Stack. However, human error due to limited digital security literacy remains a major challenge. The study concludes that integrating automated detection technologies, user education, and digital auditing is crucial to mitigating cyberattacks via QR codes. Development of adaptable tools for emerging threat patterns and behavior-based user education strategies are recommended to enhance the national cybersecurity ecosystem.]]>
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