AI for Predictive Cyber Threat Intelligence

Siva Subrahmanyam Balantrapu

Abstract


As cyber threats become increasingly sophisticated, traditional cybersecurity approaches struggle to keep pace with emerging risks. This research paper explores the application of artificial intelligence (AI) in predictive cyber threat intelligence, focusing on how AI-driven systems can anticipate and prevent attacks before they occur. By leveraging advanced machine learning (ML) techniques, AI can analyze vast amounts of historical and real-time data to identify patterns, detect anomalies, and predict potential threats with greater accuracy. We examine key AI technologies used in predictive threat intelligence, including natural language processing (NLP) for analyzing unstructured data, and deep learning for complex threat pattern recognition. The paper also evaluates the effectiveness of AI in reducing false positives, enhancing threat hunting capabilities, and enabling proactive defense strategies. Furthermore, we discuss the challenges of implementing AI-based predictive systems, such as data privacy concerns, algorithmic transparency, and the need for skilled personnel. Through case studies and a comprehensive review of the current landscape, this research highlights the transformative potential of AI in reshaping cybersecurity practices and emphasizes the importance of developing robust, ethical, and adaptable AI systems for future cyber threat mitigation.


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