Anshul Sharma

From Reactive to Cognitive: The Rise of AI-Native Network Operations

Abstract:

Enterprise networks are growing rapidly in scale and complexity due to the expansion of cloud services, mobile users, Internet of Things devices, and distributed applications. Traditional network operations have historically relied on reactive troubleshooting, static thresholds, and manual correlation of telemetry data. While these approaches have supported network management for many years, they are increasingly insufficient for modern environments that require continuous availability, adaptive performance, and rapid problem resolution.

The stakes are particularly high in mission critical environments such as healthcare, financial services, and public infrastructure, where network disruptions or security incidents can have significant consequences. In healthcare networks, connectivity failures may impact access to patient records, medical devices, or telemedicine platforms. In financial institutions, network anomalies or misconfigurations can expose systems to fraud, service outages, or data breaches. As digital services become deeply embedded in critical operations, network reliability, visibility, and security become essential requirements.

Artificial Intelligence and Machine Learning are transforming network operations by enabling networks to evolve from reactive infrastructures into cognitive systems capable of learning, reasoning, and automated decision making. AI-native network operations leverage large volumes of telemetry, flow data, device metrics, and application performance indicators to generate actionable insights in real time. AI-driven network analytics provide deeper visibility into traffic patterns, operational anomalies, and emerging performance bottlenecks. Complementing this capability, AI-based endpoint analytics enhances visibility into device behavior and user activity, allowing operators to identify abnormal patterns, compromised devices, or misconfigured endpoints that may affect performance or security.

Another important capability is the use of crowdsourced intelligence, where anonymized operational insights collected across multiple environments contribute to a shared knowledge base that improves anomaly detection and operational recommendations. When integrated into intelligent automation frameworks such as AI-driven operational systems, these insights can be translated into automated remediation workflows that reduce manual intervention.

The convergence of AI-driven analytics, endpoint intelligence, and collaborative data models enables the emergence of self-healing networks. In these environments, AI systems can detect performance degradation, perform root cause analysis, and initiate automated corrective actions through closed loop automation. This talk explores the architectural evolution toward AI-native network operations and discusses how cognitive networking models can improve reliability, operational efficiency, and user experience in modern enterprise networks. It highlights how the integration of analytics, automation, and shared intelligence is shaping the future of autonomous and resilient network infrastructures.

Profile:

Anshul Sharma is a Customer Success Technical Leader at Cisco Systems in Research Triangle Park, North Carolina. In this role, he works closely with enterprise customers to drive the adoption of Cisco technologies, with a focus on software defined networking, enterprise architecture, automation, and platform integrations across modern digital infrastructures. His work involves helping organizations design, deploy, and operationalize advanced networking solutions that support scalable, secure, and resilient enterprise environments.

Anshul actively contributes to the broader technology community through research, technical writing, and mentorship. He is IEEE Senior member and has served as both an author and peer reviewer for multiple IEEE journal publications and regularly shares industry insights through technical articles published on DevOps.com, TechStrong IT, and DZone. His work often focuses on emerging trends in networking, including AI-driven operations, automation, and next generation infrastructure.

Beyond his professional responsibilities, Anshul is passionate about fostering innovation and supporting the next generation of technologists. He has served as an industry mentor and judge at several technology hackathons, where he helps guide students and professionals in solving complex technical challenges. He is particularly interested in making complex technologies easier to understand and more accessible to engineers and organizations worldwide.