AI-Driven Configuration Drift Detection in Cloud Environments

Gogulakrishnan Thiyagarajan; Vinay Bist; Prabhudarshi Nayak

Author(s)

Gogulakrishnan Thiyagarajan Engineering Technical Leader, Cisco Systems Inc,Austin,USA

Vinay Bist Principal Engineer, Dell Inc.,Austin,USA

Prabhudarshi Nayak FET, Sri Sri University,Cuttack,India

Abstract

This paper presents an AI-driven framework for detecting configuration drift in cloud environments using Infrastructure as Code (IaC) and AWS Config logs. The framework trains machine learning models on historical and real-time configuration data to identify deviations between runtime states and IaC-defined baselines. It integrates easily with Terraform and AWS Config to reduce the dependence on manual monitoring, speed up drift detection, and increase the reliability of cloud operations. Scalability and adaptability make it especially suitable for dynamic, multi-cloud environments. The transformative role of artificial intelligence in automating drift detection, operational risk mitigation, and compliance support is outlined in a nutshell. This work paves the way toward further proactive cloud management, such as anomaly detection and support for hybrid cloud environments.

Publication Details

Page(s) 721-743

DOI DOI not available

Published Journal: International Journal of Communication Networks and Information Security, Volume: 16, Issue: 5, Year: 2024

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Keywords

Cloud Security, Infrastructure as Code (IaC), AI-Powered Detection, Configuration Drift, Auto-Remediation

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