A conceptual model for network security automation: Leveraging ai-driven frameworks to enhance multi-vendor infrastructure resilience
1 Independent Researcher, USA.
2 Independent Researcher, United Kingdom.
3 Independent Researcher, Canada.
4 CISCO, Nigeria.
5 Amstek Nigeria Limited.
Review
International Journal of Science and Technology Research Archive, 2021, 01(01), 039-059.
Article DOI: 10.53771/ijstra.2021.1.1.0034
Publication history:
Received on 19 July 2021; revised on 15 September 2021; accepted on 18 September 2021
Abstract:
The increasing complexity of multi-vendor network infrastructures presents significant challenges in maintaining robust security. Traditional network security approaches are often insufficient to address the dynamic and sophisticated nature of modern cyber threats. This study proposes a conceptual model for network security automation, leveraging Artificial Intelligence (AI)-driven frameworks to enhance resilience across multi-vendor environments. The model integrates advanced AI techniques, including machine learning, predictive analytics, and natural language processing, to automate threat detection, response, and prevention. A central feature of the proposed framework is its ability to harmonize security protocols and policies across diverse vendor systems, enabling seamless interoperability and real-time threat intelligence sharing. The model incorporates automated anomaly detection to identify irregular network behaviors and a risk-based decision-making engine to prioritize and mitigate threats proactively. By employing AI, the model ensures adaptive learning, allowing the system to evolve with emerging threats and changes in network architecture. Key components of the framework include a centralized security orchestration layer, vendor-agnostic APIs, and a unified dashboard for real-time monitoring and analytics. This approach enhances operational efficiency by reducing manual intervention, accelerating incident response times, and minimizing false positives. Furthermore, the model emphasizes compliance with industry standards and regulatory frameworks, providing organizations with a robust foundation for secure multi-vendor network management. Preliminary findings suggest that adopting this AI-driven security automation model significantly improves threat resilience, operational scalability, and resource optimization in complex network environments. The study concludes by highlighting the potential of such frameworks to redefine network security practices, offering a transformative approach to managing risks in increasingly interconnected and heterogeneous infrastructures.
Keywords:
Network Security Automation; Artificial Intelligence; Multi-Vendor Infrastructure; Threat Detection; AI-Driven Frameworks; Interoperability; Cybersecurity Resilience; Predictive Analytics; Anomaly Detection; Compliance
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Copyright © 2021 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0