In this review, the significance of OSINT in contemporary cybersecurity is critically analysed, emphasizing the dual
nature of OSINT in terms of being an effective instrument for threat identification and a latent liability in case of the
alleged abuse. Our analysis examines the progression of OSINT methodologies, including the shift from raw manual
data collection to the blending of AI-powered analytics for real-time intelligence acquisition. We highlight critical
challenges, including how to handle an overwhelming amount of unstructured data, how to ensure that accurate data
points are used without many erroneous data points, and how privacy and regulatory issues should be addressed.
Furthermore, we found a lack of standardization in how data validation processes are handled in OSINT platforms,
especially regarding the incorporation of fallback mechanisms. To mitigate these issues, we propose prevention
strategies including rigorous quality assurance processes, robust encryption and secured access mechanisms, and
autonomous, explainable AI models to map technical data to top-level threat intelligence. This review intends to create
a holistic framework to facilitate the ethical and effective application of OSINT, with an eye towards more resilient
cybersecurity in collectively informing research, policy, and practice in a rapidly digitalized world of October 2023.