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AI for Cloud Security

In: Understanding AI in Cybersecurity and Secure AI

Author

Listed:
  • Dilli Prasad Sharma

    (University of Toronto)

  • Arash Habibi Lashkari

    (York University)

  • Mahdi Daghmehchi Firoozjaei

    (MacEwan University)

  • Samaneh Mahdavifar

    (McGill University)

  • Pulei Xiong

    (National Research Council of Canada)

Abstract

Cloud computing has become a critical enabler of modern digital infrastructure, providing scalable, flexible, and cost-effective computing resources. However, its widespread adoption introduces significant security and privacy challenges, including misconfiguration, unauthorized access, data breaches, malware injections, insecure APIs, insider threats, and account hijacking. This chapter explores these vulnerabilities and how AI-driven security solutions can enhance cloud protection through predictive analytics, proactive defense, behavioral analysis, threat hunting, security automation, and compliance monitoring. AI-powered security mechanisms provide real-time threat detection, adaptive response strategies, and automated risk mitigation, but challenges such as adversarial AI threats, evolving attack techniques, and regulatory compliance issues persist. This chapter provides a comprehensive analysis of AI-enhanced cloud security, detailing current and emerging threats, AI-driven defense strategies, and the future of AI in securing cloud environments.

Suggested Citation

  • Dilli Prasad Sharma & Arash Habibi Lashkari & Mahdi Daghmehchi Firoozjaei & Samaneh Mahdavifar & Pulei Xiong, 2025. "AI for Cloud Security," Progress in IS, in: Understanding AI in Cybersecurity and Secure AI, chapter 0, pages 95-111, Springer.
    • Handle: RePEc:spr:prochp:978-3-031-91524-6_6
    DOI: 10.1007/978-3-031-91524-6_6
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