AI enabled observability: Leveraging emerging networks for proactive security and performance monitoring - Olufemi, Omoniyi David; Oladejo, Adedeji Ojo; Anyah, Vincent; Oladipo, Kamaldeen; Ikwuogu, Friday Ogochukwu (International Journal of Innovative Research and Scientific Studies, 2025)
The emergence of advanced networks is transforming the digital landscape, driving unprecedented complexity, decentralization, and service requirements across industries. As networks evolve towards increasingly dynamic, software-defined, and virtualized architectures, traditional monitoring techniques prove insufficient in managing the scale and complexity of next-generation infrastructures. This paper introduces observability as a proactive, intelligent, and security-aware framework for gaining real-time insights into the internal states of future networks. By integrating AI-driven analytics and leveraging open-source technologies alongside standards from globally recognized institutions such as 3GPP, ETSI, 5GPPP, Linux Foundation, ISACA, and ISC2, we propose a robust approach to managing the complexities of network slicing, smart cities, edge computing, and beyond. The framework emphasizes intelligent decision-making, autonomous network management, and predictive analytics to enhance performance monitoring, incident detection, and regulatory compliance in increasingly autonomous, interconnected environments. Detailed architectures, code examples, and tooling references are provided to support implementation in diverse real-world use cases. This paper envisions a future of secure, resilient, and adaptive networks, driven by AI and observability, capable of meeting the demands of digital transformation and evolving cybersecurity challenges.