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AI-Driven Threat Intelligence Systems: Predictive Cybersecurity Models for Adaptive IT Defense Mechanisms

Year: 2025

Type: article

Abstract: In the rapidly evolving digital landscape, cyber threats have become increasingly sophisticated, necessitating advanced threat intelligence systems. Artificial Intelligence (AI) has emerged as a pivotal technology in cybersecurity, enabling predictive models that enhance adaptive IT defense mechanisms. This paper explores AI-driven threat intelligence systems, detailing their architecture, methodologies, and applications in mitigating cyber threats. We discuss machine learning (ML) and deep learning (DL) models in predictive cybersecurity, real-time threat detection, and automated response systems. Furthermore, we address the challenges, ethical considerations, and future trends in AI-powered cybersecurity. Additionally, we examine the role of AI in securing Android platforms, the significance of AI-driven security for Software Developers, and how Java-based security frameworks contribute to robust cyber defense strategies.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.14964172

Authors: Rutvij Shah, Karthik Puthraya, Josson Paul

Institutions: Meta (United States), Netflix (United States), Cisco Systems (United States)

Language: English

Topic: Network Security and Intrusion Detection

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: green

OpenAlex ID: W6949534099

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