AI-Driven Cloud Security: Threat Detection, Case Studies, and Digital Risk Management Framework
DOI:
https://doi.org/10.69968/ijisem.2026v5Si1192-197Keywords:
AI-driven cybersecurity, cloud security, anomaly detection, machine learning, predictive threat analysisAbstract
AI-driven cybersecurity is transforming how organizations protect and manage digital risks in cloud environments. Traditional security systems depend on predefined rules and can only detect known threats, whereas AI analyzes live data, identifies anomalies instantly, and detects previously unseen and emerging attack patterns. AI systems continuously learn from past attacks and can predict vulnerabilities before attackers exploit them. With increasing cloud complexity, hybrid infrastructures, and evolving cyber threats, conventional security frameworks struggle to remain resilient. Artificial intelligence, through machine learning, behavioral analytics, and predictive automation, enables adaptive and intelligent defense mechanisms to safeguard cloud data. This paper presents insights drawn from real-world case studies, industry practices, and emerging challenges associated with AI-enabled cloud security. It discusses how AI protects cloud environments from cyber threats, examines challenges in digital risk management across multi-cloud systems, and highlights best practices, risk mitigation strategies, and ethical concerns. The study emphasizes the growing role of AI in enhancing cloud cybersecurity while outlining the key challenges organizations must overcome for its effective adoption.
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Copyright (c) 2026 Vandana Verma
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