OSINT-Based Threat Intelligence: Investigating Leaked Data on the Dark Web

The Dark Web has become a hotspot for cybercrime, serving as a market for stolen credentials, financial data, and sensitive corporate information. It poses an emerging threat for organizations to identify and counter threats that are created from leaked data, as cybercriminals utilize advanced anonymiza- tion tools and encryption in an effort to remain anonymous to law enforcement. Open-Source Intelligence (OSINT) has been a motivating factor for cybersecurity researchers to track, analyze, and assess such threats on the basis of publicly available information as well as automated reconnaissance techniques. This study paper examines OSINT-driven threat intelligence processes to analyze leaked data on the Dark Web. The paper explores the processes through which cybersecurity analysts use tools such as Maltego, SpiderFoot, and Scrapy in order to monitor Dark Web markets, forums, and hidden sites. The paper explores data collection methods, legal and ethical issues, and methods of evading cybercriminal detection. The study also presents real- life case studies of data breaches, breaking down the patterns in cybercrime attacks and the type of information that is most often leaked. The findings of this research provide information on the extent and impact of leaked data, establishing trends in cybercrime activity and offering countermeasures. The study highlights the importance of proactive monitoring, real-time processing, and automation in OSINT-driven threat intelligence. Future research will include the integration of AI-driven threat intelligence systems to enhance detection rates and automate Dark Web investigations, thereby improving cybersecurity defenses for or- ganizations and government agencies.