India’s digital infrastructure has undergone a significant transformation over the last decade, positioning the country among the leading economies in digital adoption. There are several reasons for the rapid growth of the Indian digital transformation market, including accelerated cloud migration, 5G adoption, more enterprise AI adoption, and the Indian government’s Digital India program, among other factors. With organizations relying on the complex webs of cloud, edge, and on-premise environments to support critical functions, establishing continuous visibility across network operations and applications takes top priority. Network monitoring solutions act as basic enablers of this visibility, ensuring reliability, performance, and security for modern enterprises. Unfortunately, traditional monitoring tools, which are inherently reactive, fall short in this rapidly changing space because they do little to predict or prevent problem escalation and typically send alerts only after an incident has occurred, impacting both customers and employees.
The Bad News: Shortcomings of legacy monitoring
As the digital ecosystems become more active and distributed, the reactive approach of legacy tools and systems can be problematic. Many of these tools are not designed to handle massive volumes of data generated from digital ecosystems in today’s Indian operations. Data silos in legacy systems hinder data-driven decision-making, which is otherwise crucial for efficient national-scale operations. Traditional Metrics, Events, Logs, and Telemetry (MELT) data can only reveal the existence of a problem, but not the ‘why’ of it. Traditional monitoring solutions do not provide IT and NetOps teams with both completeness and cost-efficiency. Ongoing maintenance costs take a bigger bite out of the budget, and cybercriminals love to target legacy systems because they often lack the protection, care, and feeding needed to truly protect the systems and information.
The Good News: AI is helping detect anomalies before outages occur
AI/ML-driven observability platforms can empower Indian enterprises and service providers to shift from reactive firefighting to proactive and predictive operations, preventing problem escalations or even outages before they cause severe damage. By integrating Deep Packet Inspection (DPI) with MELT, organizations achieve comprehensive situational awareness, harnessing the most effective telemetry while maintaining uncompromised system performance. AI/ML-driven observability solutions can also support automated responses, where the platform can initiate corrective actions once an anomaly is confirmed. The result is enhanced observability while monitoring to minimize downtime and ensure continuous service delivery.
Staying ahead with AI in network operations
AI/ML-driven observability is indeed playing a critical role in automating and optimizing network operations. By analyzing huge volumes of historical data, AI algorithms enable the identification of patterns, trends, potential issues, and subtle anomalies before they impact services. This shift from reactive to predictive is transformative for Indian enterprises handling millions of users operating at the same time. When network issues occur, traditional manual processes consume a lot of time to troubleshoot. AI and automation can help reduce mean time to detect (MTTD) and mean time to resolution (MTTR) by accelerating the mean time to knowledge (MTTK). In India’s highly regulated financial services and telecom industries, where downtime directly impacts revenue, compliance, and customer trust, AI-powered systems enable real-time anomaly detection and rapid, intelligent remediation.
AI/ML-Driven Observability can play a bigger role in critical industries
Financial institutions: AI/ML-driven observability platforms can deliver real-time network insights that enable organizations to rapidly troubleshoot issues, remain agile, and stay ahead of the curve. This is critical for the country’s high-volume payment systems such as UPI, NEFT, and others. Fraudulent transactions can be discovered faster, and risks can be contained while ensuring secure customer experiences. End-to-end visibility across data centers, cloud workloads, payment gateways, and customer-facing apps is enhanced by AI-driven observability models. Abnormal traffic patterns are detected by correlating network performance with transactional behavior that may signal cyber threats or fraudulent activities.
Also read: India’s AI Dream Needs Faster Networks to Bridge the Digital Divide
Telecom: Indian telecom providers support millions of users leveraging both 4G and 5G networks. AI/ML-driven observability can help the providers support millions of users, leveraging both 4G and 5G networks, offering seamless connectivity to customers by estimating, preventing, and quickly addressing network outages. AI/ML-powered observability platforms can unify telemetry data and correlate it to the context, offering an end-to-end view of the entire network, predicting possible disruptions, and providing actionable corrections to improve outcomes. The models trigger alerts early on about anomalies and ensure service quality is not impacted.
Large enterprises: Application complexity is increasing across India, as enterprises increasingly adopt hybrid and multi-cloud strategies and users expect near-instant app experiences. This is driving the demand for advanced monitoring and observability capabilities, with AI playing a pivotal role in enhancing performance, reliability, and user experience. AI/ML-driven observability can unify visibility across on-premises infrastructure, cloud, and edge locations to detect any degradations of network performance and optimize the use of cloud resources. By leveraging real-time comprehensive visibility, teams can enhance the efficiency of operations while aligning network performance with business outcomes.
The Last Word
AI/ML-driven Observability platforms can offer unmatched scalability and visibility into all parts of the network. Tool clutter and costs are significantly reduced while gaining comprehensive views and analysis. With enhanced decision-making capabilities of AI/ML-driven observability platforms leveraging AI-ready curated data, teams can drive better business outcomes more efficiently and effectively, and maintain exceptional user experiences by keeping critical networks and services always available and delivering value. In India, where the country’s economic progress is interlinked with its digital infrastructure, the rapid evolution of networks and maturation of AI have made AI/ML-driven observability a strategic necessity and a business imperative.
The article has been written by Gaurav Mohan, VP Sales – APAC, India & Middle East, NETSCOUT
