AI-Driven Cloud Security and Privacy Frameworks Advancements, Challenges, and Future Directions

@article{1708209,
      author = {Anant Mittal},
      title = {AI-Driven Cloud Security and Privacy Frameworks Advancements, Challenges, and Future Directions},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {8},
      number = {11},
      pages = {657-659},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1708209.pdf},
      abstract = {The rapid adoption of cloud computing has amplified security and privacy concerns, necessitating advanced frameworks to safeguard sensitive data and infrastructure. Artificial Intelligence (AI) offers transformative potential in enhancing cloud security through real-time threat detection, anomaly identification, and privacy-preserving techniques. This paper explores AI-driven cloud security and privacy frameworks, analyzing their applications, challenges, and emerging trends. By reviewing recent advancements in machine learning (ML), deep learning (DL), and federated learning, we propose a comprehensive framework integrating AI for proactive threat mitigation and regulatory compliance. The study highlights challenges such as adversarial attacks, data quality, and scalability, while offering future research directions, including quantum-resistant AI and explainable AI (XAI) for cloud environments. This research aims to guide organizations and researchers in adopting robust AI-driven solutions for secure cloud ecosystems.},
      keywords = {Artificial Intelligence, Cloud Security, Privacy Frameworks, Machine Learning, Federated Learning, Cybersecurity},
      month = {May},
  }