<![CDATA[Social engineering attacks are one of the most prevalent cyberattack methods in Indonesia, with more than 30,000 phishing cases recorded in 2024 and nearly 97% of businesses becoming targets. This condition poses significant risks to information security, making early detection critically important. This study aims to detect social engineering attacks at an early stage in Indonesia by utilizing the National Institute of Standards and Technology Cybersecurity Framework (NIST CSF), particularly the Detect and Protect functions. The research method is conducted through an analysis of current attack patterns based on data from the business sector and micro, small, and medium enterprises. The results show that the implementation of the NIST CSF can help organizations detect attacks through AI-based SIEM technologies and enhance user awareness through training and attack simulations. The implementation of this research emphasizes the importance of using standardized frameworks in addressing continuously evolving cyber threats and provides recommendations for organizations, especially micro, small, and medium enterprises, to improve their cyber resilience.]]>
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