AI-Powered Security Threat Identification and Mitigation in Cloud-Based Systems
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V5I3P117Keywords:
Cloud Security Management, AI-Driven Threat Detection, Automated Threat Response, AI-Enhanced Security Operations Centers (SOC), Cloud Computing Risk Management, Data Protection And Compliance, AI-Based Security Analytics, Containerized Security Operations, Near-Real-Time Incident Remediation, Security Workflow Automation, Human-In-The-Loop Security Systems, Decision Support For Cybersecurity, Uncertainty Management In Security Operations, Rule-Based And Machine Learning Security Models, Threat Intelligence Integration, Automated Playbook Execution, Malware Analysis And Sandboxing, Secure Cloud Architectures, AI-Assisted Compliance Management, Resilient Cloud Security SystemsAbstract
The rapid adoption of cloud computing infrastructures has introduced unprecedented security challenges that traditional defense mechanisms struggle to address effectively. This research examines the integration of artificial intelligence technologies for autonomous threat detection and response within cloud environments. As cloud platforms become increasingly complex and distributed, conventional signature-based security approaches prove inadequate against sophisticated, evolving cyber threats. This study explores how machine learning algorithms, deep learning models, and intelligent automation can enhance real-time threat identification, anomaly detection, and automated incident response capabilities. We investigate various AI methodologies including supervised and unsupervised learning techniques, neural networks, and behavioral analysis algorithms deployed across multi-tenant cloud infrastructures. The research evaluates the effectiveness of AI-driven security frameworks in identifying zero-day exploits, advanced persistent threats, and insider attacks while minimizing false positives. Additionally, we analyze the challenges associated with implementing intelligent security systems in cloud environments, including data privacy concerns, computational overhead, model training requirements, and integration with existing security infrastructure. Through experimental analysis and case studies, this work demonstrates that AI-enhanced threat detection systems can significantly reduce response times, improve accuracy in threat classification, and provide adaptive security measures that evolve with emerging attack vectors. The findings suggest that artificial intelligence represents a transformative approach to cloud security, offering scalable, intelligent, and proactive defense mechanisms essential for protecting modern cloud computing infrastructures against dynamic and sophisticated cyber threats.
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