ADOPTION OF AI-POWERED THREAT INTELLIGENCE IN CLOUD INFRASTRUCTURES FOR REAL-TIME RISK MITIGATION AND AUTOMATED ANOMALY DETECTION THROUGH PREDICTIVE SECURITY ANALYTICS

Anuj Aggarwal

doi:10.29121/digisecforensics.v2.i1.2025.87

Authors

Mr. Anuj Aggarwal Cybersecurity Engineer, DFS Corporate Services LLC, Delaware, USA

DOI:

https://doi.org/10.29121/digisecforensics.v2.i1.2025.87

Keywords:

AI-Powered Threat Intelligence, Cloud Infrastructures, Real-Time Risk Mitigation, Automated Anomaly Detection, Predictive Security Analytics, Machine Learning, Cybersecurity, Data Breach Prevention

Abstract

The exponential growth of cloud infrastructures has amplified cybersecurity vulnerabilities, with breaches costing organizations an average of $4.88 million in 2024. This study investigates the adoption of AI-powered threat intelligence to enable real-time risk mitigation and automated anomaly detection via predictive security analytics. Utilizing a mixed-methods approach, including simulations on synthetic datasets mimicking 2023-2024 cloud traffic patterns and analysis of ML algorithms like LSTM and Isolation Forest, the research assesses AI's efficacy in preempting threats. Findings indicate a 68% reduction in detection latency and 75% improvement in false positive rates, alongside a 52% decrease in breach propagation risks. These results highlight AI's transformative potential in enhancing cloud resilience. The study concludes by advocating for integrated AI frameworks to foster proactive defenses, informing policy and practice for scalable, secure cloud ecosystems.

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