Improving Cybersecurity Incident Response through Natural Language Processing

Abstract

Natural Language Processing (NLP) is becoming a transformative technology in cybersecurity, especially in improving incident response systems. This study investigates how incorporating NLP methods into cybersecurity frameworks can improve incident management's effectiveness and efficiency. With the rise in cyber threats, organizations often find traditional methods of detecting threats and responding to incidents ineffective because of the intricate and disorganized data. Using NLP, cybersecurity professionals can automate examining written information in incident reports, logs, and communication platforms, allowing for quick detection and sorting of dangers. This paper explores different uses of NLP in cybersecurity, such as creating incident reports automatically, identifying phishing attempts, and collecting threat intelligence from social media and forums.

Moreover, it showcases how NLP can decrease reaction times and improve decision-making by offering precise, valuable insights from intricate data sets. The research also discusses the difficulties of incorporating NLP solutions in cybersecurity, like worries about data privacy and the necessity of ongoing model training to counter changing threats. In the end, the main goal of this study is to show how NLP can be a vital instrument in strengthening cybersecurity efforts, allowing organizations to handle incidents better and protect their digital assets.