International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 10 | Oct 2024
p-ISSN: 2395-0072
www.irjet.net
Enhancing Multi-Cloud Security and Automation through AI/ML-driven Infrastructure as Code (IaC) Solutions Kishan Gugulotu -----------------------------------------------------------------------***-------------------------------------------------------------------------Abstract This article deals with the framework that integrates Artificial Intelligence (AI) and Machine Learning (ML) with Infrastructure as Code (IaC) to address multi-cloud environment challenges. It also explores the potential of AI/ML-driven IaC solutions to enhance security, compliance, and efficiency across cloud platforms. Some essential elements are predictive security analysis, automated compliance checking, intelligent resource optimization, and AI-enhanced configuration validation. This approach enhances resource utilization and lowers operating expenses associated with security incidents. Compared to conventional IaC approaches, this article also shows notable gains in threat identification, cost-effectiveness, and resource management. The study also describes current challenges and future research directions in AI/ML-driven multi-cloud management.
Keywords: Multi-Cloud Security, Infrastructure as Code (IaC), Artificial Intelligence (AI), Machine Learning (ML), Cloud Optimization
1. Introduction Organizations have turned to embracing multi-cloud architectures as their primary strategy in order to maximize resilience, avoid vendor lock-in, and take use of the unique advantages of several cloud providers. As per the Flexera poll [1], 87% of firms have adopted a hybrid architecture that integrates both public and private clouds, while 93% of businesses have a multi-cloud strategy. The availability of best-of-breed services from several providers, flexibility, and disaster recovery were all enhanced by this setup. However, this approach complicates resource management and maintains consistent security postures to ensure platform compliance. A study conducted by IBM Security found that companies using more than one cloud are 33% more likely to experience a data breach, which results in an average loss of $4.99 million [2]. Infrastructure as Code (IaC), a crucial tool in addressing these difficulties, allows organizations to create and manage their infrastructure using machine-readable definition files. The global IaC market is predicted to have expanded at a rate of 27.5% yearly from $0.82 billion in 2021 to $2.76 billion by 2026 [1]. Despite of the IaC advantages, several gaps still need to be addressed in achieving real-time security enforcement and optimization in multi-cloud environments. For instance, 70% of organizations report difficulties consistently applying security policies across multiple cloud platforms, and 63% need help with real-time threat detection in their multi-cloud setups [2]. This research recommends integrating AI and ML technologies with IaC to overcome these problems and enhance the overall security and efficacy of multi-cloud systems.
Research Objectives: 1. 2. 3. 4.
Assign an architecture to existing IaC systems so they may leverage AI/ML to increase automation and security in multi-cloud environments. We aim to reduce security misconfigurations by 75% and improve compliance adherence by 90% across diverse cloud platforms. Explore methods for real-time threat detection and automated remediation using AI/ML-driven IaC. We aim to decrease threat detection time by 60% and achieve a 95% accuracy rate in identifying genuine security incidents. Examine AI/ML apps across various cloud platforms to maximize efficiency and save expenses. It improves performance metrics while cutting cloud spending by 30%. Compare the effectiveness of the suggested AI/ML-enhanced IaC solution to more conventional methods. It also accomplishes comprehensive benchmarks to demonstrate a 50% increase in overall security and a 40% gain in operational efficiency.
This study aims to advance the topic by providing a comprehensive framework for leveraging AI/ML in IaC, new methods for enhancing multi-cloud security, and empirical evidence of the benefits of this strategy in the form of performance evaluations and case studies. By addressing the current problems in these situations, organizations managing multiple clouds can reduce their annual security incidents related to cloud computing by 65% and increase their utilization of cloud resources by 45%.
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