Key Challenges and Limitations of the OSINT Framework in the Context of Cybersecurity

In today's world, Open-Source Intelligence (OSINT) has gained popularity as a method of gathering data, both for cyber-attacks and detecting cyber threats. It involves using publicly available information from the internet to identify and evaluate potential cybersecurity risks. Despite its usefulness, the OSINT framework is not without its challenges and limitations. The objective of this research study is to examine the key challenges and limitations of the OSINT framework specifically in the context of data gathering for cybersecurity purposes. This study relies on a comprehensive review of relevant literature on OSINT and its applications in cybersecurity, as well as case studies. Some of the primary challenges and limitations of the OSINT framework that have been identified include issues with data quality, data quantity, data integration, analysis and interpretation, privacy, and ethical considerations. To tackle these challenges, this research study proposes a range of potential solutions, such as the development of more advanced analytical tools and techniques, the integration of machine learning and artificial intelligence algorithms, and the implementation of responsible and ethical data collection and analysis practices. Overall, this research provides valuable insights into the challenges and limitations that need to be addressed when using OSINT as a method of data gathering for cybersecurity purposes.

I. Introduction

Open-source intelligence is a term used to refer to a framework that is primarily used to gather and compile open source data such as usernames, sources, and other relevant information. This framework has numerous applications, including information gathering and business analysis.