International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 10 | Oct 2025
p-ISSN: 2395-0072
www.irjet.net
AI-Powered Cyber Threat Intelligence: An Integrated Data-Driven Model Dr. Bakhtawer Shameem1, Dr. Nagendra Sahu2, Dr. Anirudh Kumar Tiwari3, Dr. Satish Tewalkar4, Thanendra Kashyap5 12345Guest Lecturer, Department of Higher Education, Chhattisgarh, India
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Abstract - The digital landscape is changing at a breakneck
respond in kind, developing countermeasures that are just as intelligent, adaptive, and predictive.
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 the system's ability to adapt its response. When put to the test, our model consistently showed higher precision and a significantly lower 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.
1.2 Cyber Threat Intelligence and Its Significance At its core, Cyber Threat Intelligence (CTI) is the disciplined process of gathering, analyzing, and making sense of information about potential cyber threats. The ultimate goal is to turn a flood of raw data into actionable insights that inform defense plans, improve an organization's understanding of its threat landscape, and facilitate swift action during security incidents. When implemented effectively, CTI empowers organizations to foresee and preempt attacks, significantly reducing both operational downtime and financial damage. Yet, for all its promise, CTI struggles with significant hurdles: it's difficult to scale, the data comes in countless formats, and the analysis is inherently complex. Consider the sheer volume of threat data produced every day—from new malware variants and firewall logs to discussions on dark web forums—a deluge that easily overwhelms conventional analysis tools. Relying on manual or partially automated processes for this not only slows everything down but also makes it easy to miss critical clues. This is precisely why the integration of artificial intelligence is becoming a gamechanger for CTI, opening the door to real-time processing, systems that learn continuously, and dynamic decisionmaking.
Key Words: Artificial Intelligence, Cyber Threat Intelligence, Machine Learning, Data-Driven Security, Anomaly Detection, Threat Prediction, Cybersecurity Analytics, Automated Defense Systems 1.INTRODUCTION
1.1 Background
Digital transformation is reshaping our world, but this progress comes with a steep price: a dramatic rise in the scale and complexity of cyber threats, fueled by an explosion of data and interconnected systems. While organizations depend on this digital infrastructure for its clear operational benefits, this very reliance has opened them up to devastating attacks, including ransomware, phishing campaigns, massive data breaches, and advanced persistent threats (APTs). The financial impact is staggering, with recent estimates projecting global cybercrime costs to reach trillions of dollars each year—a figure that underscores the critical need for smarter, more proactive defenses. However, traditional cybersecurity measures are failing to keep up. Typically, reactive and bound by rigid rules, these legacy systems depend on static signatures and manual analysis, leaving them blind to novel and evolving attack methods. Compounding the problem, attackers are now weaponizing AI and automation to launch increasingly sophisticated campaigns. It is clear that the defense community must
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1.3 Role of Artificial Intelligence in Threat Intelligence The rise of Artificial Intelligence (AI), including its subfields of machine learning (ML) and deep learning (DL), has fundamentally reshaped cybersecurity. It has introduced a new paradigm of data-driven decision-making and largescale pattern recognition. When applied to Cyber Threat Intelligence (CTI), these AI techniques bring powerful automation to the process, capable of pinpointing anomalies, revealing hidden connections between threats, and even forecasting potential system weaknesses. For instance, while machine learning algorithms are adept at spotting subtle irregularities in network traffic, natural language processing (NLP) can sift through vast amounts of text from security feeds and online forums to extract critical Indicators of Compromise (IoCs). This infusion of AI doesn't just add new tools; it elevates the entire threat intelligence process, boosting its accuracy, accelerating its speed, and ensuring it
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