With the rapid transformation of IT infrastructures, organizations are shifting from reactive monitoring to network observability, ensuring granular visibility to anticipate performance issues and respond to security incidents before they impact operations.
This evolution is driven primarily by the adoption of multi-cloud environments, microservices architectures, the expansion of SD-WAN networks, and the proliferation of IoT endpoints, all of which have made corporate ecosystems infinitely more dynamic, distributed, and complex.
What is network observability?
Network observability is the ability to infer the internal state of the entire communications infrastructure based on continuous access to and analysis of the data generated by its external components (switches, routers, firewalls, links, and access points).
While conventional monitoring only alerts you when a resource fails or becomes unavailable, observability addresses why the failure occurred, where the root cause lies, and how traffic behavior is evolving in real time.
How does network observability work in practice?
To deliver this actionable intelligence, network observability operates through a continuous four-step cycle:
- Multi-source data collection (Data Ingestion): continuous, high-speed capture of hardware telemetry, packet records, traffic flows (NetFlow/IPFIX), and system logs.
- Data correlation and normalization: the system cross-references information from different layers (from Layer 2 physical to Layer 7 application) and distributes the data to a centralized, high-performance repository.
- AI-powered behavioral analysis (AIOps): Machine learning algorithms compare real-time data against the baseline of the organization’s normal behavior to identify subtle deviations.
- Visualization and automation: Centralized dashboards display the physical and logical network topology, enabling automatic responses (such as rerouting or isolating threats) before end users notice any performance degradation.
Implementing this capability is the key to maintaining operational resilience, eliminating blind spots in the infrastructure, and protecting the corporate ecosystem against sophisticated cyber threats.
The Evolution from Traditional Monitoring to Network Observability
To understand the practical value of network observability, it is essential to distinguish this model from the traditional approach to infrastructure management.
Traditional monitoring has historically relied on periodic polls via the Simple Network Management Protocol (SNMP), at intervals ranging from 3 to 5 minutes.
In modern high-speed networks, a 5-minute window represents a critical blind spot: instantaneous traffic spikes, packet drops, and data exfiltration attacks often last only milliseconds, going completely unnoticed by SNMP.
Network observability, on the other hand, relies on cross-referencing multiple data streams in real time:
- Streaming telemetry (gNMI / NETCONF): continuous, event-driven transmission of hardware metrics, CPU usage, memory utilization, and interface status, eliminating the overhead of SNMP polling.
- Flow data (NetFlow, sFlow, IPFIX): detailed records of who is communicating with whom, which applications are being used, traffic volume, source/destination IP addresses, and ports used.
- Packet analysis and application metrics: packet capture and deep packet inspection (DPI) to analyze round-trip time (RTT), TCP retransmissions, and application-layer latency (HTTP/HTTPS, DNS, database).
- Event and audit logs: consolidated logs from network devices, firewalls, Wi-Fi controllers, and authentication systems.
Identifying performance bottlenecks in real time
Network bottlenecks result in a poor user experience, slow ERP/CRM systems, and degraded voice and video call quality. Network observability plays a direct role in accurately identifying these performance anomalies.
Analysis of Microbursts and Buffer Saturation
One of the most difficult problems to diagnose in corporate networks is the microburst: extremely short bursts of high-speed traffic that saturate the switches’ buffer memory in milliseconds. Since average bandwidth usage appears normal when measured per minute, conventional diagnostics fail.
Observability tools that use streaming telemetry can capture frame drops in the switch queue at the exact moment they occur, allowing adjustments to buffer size or QoS (Quality of Service) policies before applications experience degradation.
Latency Diagnosis in Hybrid Environments and SD-WAN
When a user reports slow performance in a cloud application, the root cause could lie in several areas: the local Wi-Fi connection, an overloaded access switch, the traffic-breaking rule on the SD-WAN router, the internet service provider’s link, or the cloud instance itself.
Network observability analyzes the entire path of the TCP session, measuring the connection establishment time (TCP handshake) and the server response time. If latency accumulates in the transport phase of the WAN link, the orchestrator can dynamically reroute critical traffic to a secondary link with better quality.
Identification and Mitigation of Cyber Threats in the Network Environment
The data network is the only element common to all of a company’s digital assets. Therefore, using cybersecurity-focused network observability is one of the most effective strategies for detecting intrusions that bypass conventional perimeter defenses.
Integration with NDR (Network Detection and Response) and AIOps
Modern observability platforms use NDR (Network Detection and Response) capabilities combined with machine learning algorithms (AIOps). The system establishes a baseline for normal traffic behavior within the company and flags suspicious deviations in real time:
- Network Scanning (Port Scanning): Identification of an internal endpoint attempting to scan ports on multiple servers on the network within a short period of time.
- Atypical Data Exfiltration: Detection of an abnormal volume of data transferred outside of business hours from a workstation to an unknown external IP address.
- Lateral Movement: Identification of direct connections between corporate computers that normally should not communicate with each other, indicating the presence of an attacker attempting to spread across the local network.
Proactive Mitigation of Denial-of-Service (DDoS) Attacks
Volumetric or application-based DDoS (Distributed Denial of Service) attacks require an immediate response.
Through continuous traffic analysis (IPFIX/NetFlow), network observability identifies sudden spikes in the packets-per-second (PPS) rate directed at a specific IP address.
This allows mitigation mechanisms and traffic diversion (scrubbing center) to be automatically triggered within seconds, preserving service availability.
How does Tracenet Solutions build network observability into its infrastructure?
Implementing a network observability architecture requires integrating data collection tools, defining behavioral baselines, and correlating complex network and security events.
Tracenet Solutions combines expertise in network engineering and cybersecurity to transform telemetry data into operational intelligence for your business:
- Network architecture and automation projects: modernization of physical and logical infrastructures with support for streaming telemetry (gNMI), flow protocols (IPFIX), and management automation.
- Integrated management via NOC (Network Operations Center): continuous and proactive monitoring of network health, identifying bottlenecks, packet loss, and link degradation before they impact users.
- Security and incident response via SOC (Security Operations Center): 24/7 monitoring of network traffic for anomaly detection, vulnerability analysis, and immediate containment of cyber threats.
- Advanced troubleshooting and consulting: root cause diagnosis for complex performance issues in campus, data center, and wireless network environments.
Do you want to improve visibility into your infrastructure and ensure full control over the performance and security of your digital ecosystem?
