Cyber Threat Intelligence: Predictive Analysis for Cyber Threats
Assistant Professor Mansi W. Ahire
Dr. Varsha Patil Women’s College of Computer Application, Jalgaon
Abstract - As cyber-attacks grow in complexity and scale, traditional security methods often fall short in addressing evolving digital threats. This paper investigates how artificial intelligence (AI) is revolutionizing predictive cyber threat intelligence by proactively identifying and mitigating risks before they materialize. Utilizing advanced machine learning (ML) techniques, AI systems can process extensive historical and live data to detect patterns, uncover anomalies, and forecast potential security breaches with improved precision. Key AI-driven technologies, such as natural language processing (NLP) for interpreting unstructured threat data and deep learning for identifying intricate threat behaviour, are central to this transformation. We explore how these tools help minimize false alerts, improve threat detection and hunting, and empower organizations to adopt a more proactive cyber security stance. In addition to highlighting the benefits, the paper addresses the challenges associated with deploying AI-based predictive models—particularly around data privacy, model explain ability, and the shortage of qualified professionals. Through detailed case studies and a critical analysis of the current technological landscape, this study underscores AI’s potential to redefine cyber security practices. It also stresses the need for developing responsible, transparent, and flexible AI solutions to combat future threats effectively.
Key Words: Artificial Intelligence (AI), Cyber security, Predictive Threat Intelligence, Machine Learning (ML), Anomaly Detection, Pattern Recognition, Natural Language Processing (NLP), Deep Learning, Threat Hunting, False Positives Reduction, Proactive Defense, Data Privacy, Cyber Threat Mitigation, Real-time Data Analysis, Unstructured Data Analysis, Cyber Attack Prevention, Ethical AI
