Predictive Threat Modeling : Data-Centric AI for Proactive Cyber Threat Intelligence

Abstract

Thereby, it could be declared that traditional 'defensive' measures that presuppose an organization's readiness to be merely passive could prevent today's high-speed cyber threats from penetrating the systems. The threat modeling system is predictive and offers threat modeling for Data-centric AI; therefore, cybersecurity professionals search for threats, and concurrently, machine learning loops and scans vast datasets and executes routines. By employing the Big Data Processing functionality, Artificial Intelligence cybersecurity tools provide ways of real-time threat detection to avoid cybercrimes based on the identification of configured patterns. Complementing the anomaly detection method of analysis, behavioral analytics functions along with the natural language processing (NLP) and self-security reaction systems provide fundamental technological components of this analysis. In this manner, the authors respond to the practical difficulties associated with model bias in using AI for the cybersecurity protection of different systems and the risk of adversarial AI technologies and privacy legislation. For instance, the study will employ real-life business examples to shed light on how industries alter cybersecurity systems due to advancements in AI technology. This section of the work also provides a future outlook focusing on trends currently emerging in AI's threat modeling. It looks at quantum AI together with self-safeguarding or self-repairing cybersecurity and the use of blockchain to identify the most imminent pre-emptive cybersecurity defenses.