Year: 2025
Type: article
Abstract: The integration of generative artificial intelligence with cloud computing has fundamentally transformed cybersecurity frameworks, enabling unprecedented capabilities in threat detection and automated incident response. This technological convergence allows organizations to shift from reactive to proactive security postures through sophisticated anomaly detection and predictive analytics. Major cloud providers have embedded AI-driven security tools that analyze vast datasets to identify subtle patterns indicative of potential threats before they materialize into breaches. While delivering significant improvements in detection accuracy, response time, and cost reduction, this integration also introduces novel security challenges. Adversarial attacks against AI models, AI-generated phishing campaigns, and automated malware represent emerging threats that require comprehensive countermeasures. Multi-layered security frameworks incorporating access control, data protection, confidential computing, model security, and continuous monitoring provide effective defense mechanisms. Confidential computing emerges as a critical technology for securing AI operations, protecting sensitive data during processing through hardware-based isolation while facilitating secure multi-party computation for collaborative model training across regulated industries. The rapid evolution of this technological intersection demands ongoing adaptation of security strategies and governance frameworks to ensure that organizations can leverage the transformative potential of AI while maintaining robust defenses against increasingly sophisticated threat actors targeting the convergence of AI and cloud infrastructure.
Author: Raakesh Dhanasekaran
Institution: Illinois Institute of Technology
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4411464665