This research employs Natural Language Processing (NLP) to automatically identify and categorize emerging cyber risks in order to improve cybersecurity intelligence and early attack detection. Due to the quick development of digital technology and online communication platforms, cyber threats are constantly expanding and becoming more complicated. Real-time threat identification is challenging since modern threat detection systems rely on organized data and predefined signatures. Large volumes of unstructured textual data from blogs, forums, threat intelligence feeds, cybercrime reports, and social media platforms are evaluated using natural language processing (NLP) in order to address this problem. Data collection, threat entity identification, and cyber intrusion pattern detection are all done automatically by the application. The system classifies emerging cyber threats by characteristics, attack potential, and severity using machine learning and text mining. By increasing the speed and accuracy of cyber threat intelligence, this automated solution helps businesses get ready for emerging security threats. The results of the test indicate that the NLP-based method can identify and characterize distinct cyberthreats. This
enhances risk reduction and safety monitoring.