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Intelligent Enterprise Retail Infrastructure using AI Driven Cybersecurity and AWS CloudWatch Alert Automation

Abstract

Modern enterprise retail systems are increasingly dependent on cloud-native infrastructures, distributed microservices, and real-time data pipelines. This transformation has expanded the attack surface, making cybersecurity a critical concern. At the same time, operational efficiency requires continuous monitoring, automated alerting, and intelligent incident response mechanisms. This paper explores an intelligent enterprise retail infrastructure that integrates AI-driven cybersecurity with AWS CloudWatch-based alert automation to enhance resilience, scalability, and threat detection accuracy

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The proposed system leverages machine learning models for anomaly detection, behavioral analytics for fraud prevention, and predictive security intelligence to identify potential threats before they escalate. AWS CloudWatch serves as the centralized observability layer, collecting logs, metrics, and events from distributed retail services such as payment gateways, inventory systems, and customer applications. Automated alerting workflows trigger AWS Lambda functions for incident response, reducing mean time to detection (MTTD) and mean time to recovery (MTTR).

 

The integration of AI with cloud monitoring enables proactive defense mechanisms, adaptive thresholding, and intelligent noise reduction in alerts. This hybrid approach ensures that enterprise retail infrastructures remain secure, highly available, and operationally efficient while minimizing human intervention in cybersecurity operations.

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