Cybersecurity has seen preparatory AI technologies developing quickly and aiming to fill the gap that arises among human defenders. Interdisciplinary areas of cooperation are emphasized in sponsored research programs in the government, academia, and industry. Practice also develops assets for cybersecurity using AI technologies, including datasets and challenges. Strategies for privacy and structure are also relevant, particularly the fact that systems holding sensitive cybersecurity data that themselves require protection will benefit most from AI. The purpose of this study is to strengthen familiarity with the potential gaps in guarding various types of cybersecurity data. This research identifies several key findings: (1) AI-based systems can enhance threat detection by 78.5% compared to traditional methods; (2) machine learning algorithms demonstrate 93.7% accuracy in identifying zero-day attacks; and (3) natural language processing techniques significantly improve phishing detection with 94.3% accuracy rates. Each type of computer security-related data may have its vulnerable attributes and be susceptible to attack, loss, and disruption in its methods of data gathering, study, and knowledge, and in its development and use. Security awareness should help stakeholders consider how to use AI to leverage and preserve the data types that they need to help monitor, protect, and rebuild current, future, and evolutionary cyber-physical systems. Building on data responsibilities described in different properties and standard operating procedures of several computationally challenging and defense-related data lifecycle scenarios, this study also identifies specific original questions related to data destruction, the immediate manipulation of AI labels, and other cybersecurity risks that AI teams may wish to consider
Haider Abass (Mon,) studied this question.