A COMPREHENSIVE FRAMEWORK FOR AI-BASED THREAT INTELLIGENCE IN CLOUD CYBER SECURITY

Abstract

In the realm of cloud cyber security, the evolution of AI-based threat intelligence has become pivotal in safeguarding digital assets against increasingly sophisticated threats. This paper proposes a comprehensive framework for leveraging artificial intelligence (AI) to enhance threat intelligence capabilities within cloud environments. The framework integrates various stages of threat intelligence, including data collection, preprocessing, and feature engineering, with advanced AI and machine learning techniques such as supervised and unsupervised learning, and deep learning models. Key components include the threat intelligence lifecycle of detection, analysis, and mitigation, supported by diverse data sources like network traffic, user activity logs, and external threat feeds. Integration with existing security systems, evaluation metrics, and real-world case studies highlight practical implementations and successes. Challenges such as data privacy, scalability, and model interpretability are discussed, with future directions focusing on AI advancements and collaborative efforts to address evolving threats.