Intelligent Cloud-Based Threat Detection and Privacy-Preserving Machine Learning for Modern Enterprise Applications

Abstract

The rapid growth of cloud computing, artificial intelligence, and enterprise digital transformation has significantly increased the complexity of cybersecurity threats targeting modern enterprise applications. Traditional security systems often struggle to manage sophisticated cyberattacks, large-scale data processing, and privacy protection requirements in distributed cloud environments. This study explores the integration of intelligent cloud-based threat detection systems and privacy-preserving machine learning techniques for securing modern enterprise applications. The research focuses on the application of artificial intelligence, deep learning, behavioral analytics, and anomaly detection methods within cloud infrastructures to identify and mitigate cyber threats in real time. Additionally, privacy-preserving machine learning approaches such as federated learning, homomorphic encryption, differential privacy, and secure multi-party computation are examined to ensure data confidentiality while maintaining analytical performance. The study highlights how cloud-native security architectures, zero-trust frameworks, and AI-driven automation improve enterprise resilience, scalability, and operational efficiency. Furthermore, the research discusses the challenges associated with adversarial attacks, data governance, computational complexity, and regulatory compliance in intelligent cloud environments. The findings indicate that integrating cloud-based threat detection with privacy-preserving machine learning provides a robust, scalable, and adaptive cybersecurity framework capable of supporting secure digital transformation and protecting sensitive enterprise data in modern interconnected ecosystems.