Enhancing Cybersecurity Through AI-Driven Predictive Threat Intelligence Systems

Abstract

The integration of Artificial Intelligence (AI) into Predictive Threat Intelligence systems on a cybersecurity basis is examined. The focus is on AI-driven Predictive Threat Intelligence systems' potentialities in strengthening protective capabilities against cyber threats on a cybersecurity scheme, particularly with respect to the next-generation Alpha defense scenarios. The current cybersecurity schemes as well as emerging threats and tactical defense methods are discussed. Intelligent approaches including Machine Learning (ML) and Deep Learning (DL) algorithms are introduced, implemented, and tested in order to identify and mitigate the rapid-fire, evasive, and bursting-piece cyber threats which are predicted to be emerging with severe consequences such as strategic cyber/physical sabotages, data breaches, and permanent damage. Additionally to conventional investment into cyber firewalls and protection gaps, most of the private enterprises and nation-states have been engaging in predictive readiness, and even preemption of the forthcoming cyber threats by the integration of Predictive Threat Intelligence (PTI) Systems. The PTI systems collect, process and analyze massive-scale cyber incident data and emerging hazard signals, and further utilize that data for predicting possible threats, vulnerabilities, and impacts. In such monitor-detect-predict-defend cybersecurity mechanisms of the upcoming Beta defense scenarios, the implemented PTI systems turn out to be standalone defensive units. The upgrade of PTI systems with AI capabilities such as data analytics, ML, and DL that automate early recognition, granular event analysis in semi-real time and speed-up commitment of the remediation measures.