AI-Powered Cyber Threat Intelligence: An Integrated Data-Driven Model

The digital landscape is changing at a breakneck pace, and with it, cyber threats are becoming both more sophisticated and frequent. To counter this, we argue that defense mechanisms must be equally intelligent and grounded in data. In this paper, we introduce a new Cyber Threat Intelligence (CTI) model powered by AI, which brings together machine learning and data analytics to not only detect but also classify and predict emerging threats as they happen. Our framework works by pulling in data from a wide array of sources, then using feature engineering and supervised learning to improve both the accuracy of detection and thesystem's ability to adapt its response. When put to the test, our model consistently showed higher precision and a significantlylower false-positive rate than older, rule-based systems. Ultimately, by merging AI with live threat intelligence, we have built a scalable and interpretable CTI architecture that helps organizations move from a reactive to a genuinely proactive cybersecurity stance.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-10-15
DOI
https://doi.org/10.5281/zenodo.17440333
Primary Topic
Prenatal Screening and Diagnostics
Type
article
Field-Weighted Citation Impact
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article

AI-Powered Cyber Threat Intelligence: An Integrated Data-Driven Model

Zenodo (CERN European Organization for Nuclear Research)
Prenatal Screening and Diagnostics
article

AI-Powered Cyber Threat Intelligence: An Integrated Data-Driven Model

article en

Abstract

The digital landscape is changing at a breakneck pace, and with it, cyber threats are becoming both more sophisticated and frequent. To counter this, we argue that defense mechanisms must be equally intelligent and grounded in data. In this paper, we introduce a new Cyber Threat Intelligence (CTI) model powered by AI, which brings together machine learning and data analytics to not only detect but also classify and predict emerging threats as they happen. Our framework works by pulling in data from a wide array of sources, then using feature engineering and supervised learning to improve both the accuracy of detection and thesystem's ability to adapt its response. When put to the test, our model consistently showed higher precision and a significantlylower false-positive rate than older, rule-based systems. Ultimately, by merging AI with live threat intelligence, we have built a scalable and interpretable CTI architecture that helps organizations move from a reactive to a genuinely proactive cybersecurity stance.

Zenodo (CERN European Organization for Nuclear Research)
Peace, Justice and strong institutions
Openalex Percentile: Top 25%
Prenatal Screening and Diagnostics
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