ADVANCING CLOUD NETWORKING: A MULTI-VENDOR APPROACH TO SECURE AND SCALABLE ENTERPRISE NETWORKS

Abstract

It is becoming increasingly difficult to provide a strong security infrastructure owing to the multi-party network complexity that the infrastructures are growing into. Network security is often not sufficient as traditional network methods are not very efficient because the very new-age cyber threats are very complicated and change every minute. The research gives a theoretical framework for considering the effectiveness of automated network security using AI-based frameworks in multi-vendor environments. Machine learning, predictive analytics, and natural language processing are some high-order artificial intelligence methodologies that the model encompasses for automating threat signals, responses, and preventions. One important aspect of the proposed framework is its ability to standardize security policy and procedures across multiple vendor systems, facilitating seamless interoperability and real-time threat information exchange. The model uses a risk-based decision engine to proactively prioritize and mitigate risks and has automated anomaly detection for identifying any behaviors in the network that are incompatible with the assembled model. Constructed with an engine for risk-based decision making, it has automated anomaly detection that recognizes behaviors in the network that are not normal. Such an approach ensures the adaptation learning of the system with AI, whereby the system adapts to further novel threats and network changes. A consolidated monitoring and analytics dashboard, vendor-dependent APIs, and a centralized security orchestration layer form part of the basic system. The operational efficiencies are enhanced by reducing false positives with faster response times to incidents, thereby limiting human involvement. The model gives organizations a strong basis to operate in a safe multi-vendor network environment and highlight compliance with legislative frameworks and industry standards. This AI-based security automation seemed to be promising in the initial results in terms of optimizing resources, scaling operations, and making networks harder against threats. Finally, in conclusion, these frameworks have the potential to change network security practices by proposing a new way to address risks in the framework of interconnected and heterogeneous environments.