Network Intelligence: 10 Critical Use Cases

Table of Contents

What is network intelligence?1. Correlate diverse data to provide contextualized network visibility 2. Identify traffic patterns, seasonality, baselines and benchmarks 3. Intelligent traffic engineering 4. Capacity planning 5. SLA violation prediction 6. Troubleshoot connectivity and performance issues 7. Intelligent incident triage 8. DDoS detection 9. Identify potential security threats 10. Reliable data for closed‑loop automation and orchestration Bringing it all together with network intelligence How can Kentik bring the benefits of network intelligence to your organization?

Modern networks stretch across clouds, data centers and the public internet. Monitoring tools give you statistics, but they rarely connect the dots between traffic metrics and the services and customers they impact. Network intelligence takes observability a step further by fusing high‑fidelity telemetry with business context and machine learning. It surfaces what’s normal, predicts what’s coming and explains why anomalies occur — before users notice.

This article explores ten network intelligence use cases that are described further in Kentik’s ebook, 10 Critical Use Cases for Network Intelligence.

What is network intelligence?

Network intelligence is an AI-assisted layer on top of network observability that turns raw telemetry (flows, routing, device and cloud metrics, logs, and synthetics) plus business context into answers and actions. It correlates and explains issues, predicts risk (like SLA breaches), and can trigger remediation across on-prem and multicloud networks.

Older definitions focus on deep packet inspection (DPI) and communications-service-provider use cases. Kentik’s approach spans flows, routing, SNMP and cloud telemetry, contextual enrichment, and LLM-driven natural-language workflows. Today’s network intelligence is about moving beyond dashboards to proactive answers and actions.

With this definition, let’s look at ten of the most critical use cases for network intelligence.

1. Correlate diverse data to provide contextualized network visibility

Network intelligence ingests high‑volume telemetry (flows, routing tables, metrics and logs) and enriches it with business dimensions such as applications, customer segments and revenue impact. Instead of staring at separate graphs for latency and packet loss, engineers see a single narrative about which service is affected when packets drop. Contextualized visibility reduces mean time to resolution and helps teams prioritize triage activities based on business impact.

Network intelligence: Chart showing average latency and packet‑loss percentage with a rolling standard‑deviation band.

Telemetry such as latency and packet loss are measured over time and compared to a dynamically created rolling baseline of normal behavior. These metrics can then be correlated with each other in a time series to show how a spike in one relates to a spike in another.

2. Identify traffic patterns, seasonality, baselines and benchmarks

By continuously analyzing network data, network intelligence learns the natural rhythm of network traffic — from lunchtime video spikes to quarter‑end ERP surges. It establishes dynamic baselines and benchmarks that adapt as the environment grows while still flagging anomalies and sending alerts when performance drifts from service‑level targets. Machine‑learning models capture seasonality patterns so engineers can tell at a glance whether a peak is expected or problematic.

Network intelligence: Time‑series chart of average HTTP latency with a light‑blue band illustrating the rolling standard deviation; callouts show the current latency and the bounds of the baseline.

Notice that the system programmatically created a rolling standard deviation using Z-scores to determine what “normal” HTTP latency is over a given timeframe. That way, when HTTP latency deviates significantly, the system can measure the delta, flag it as an anomaly, and send an alert.

3. Intelligent traffic engineering

Raw telemetry becomes an always‑on traffic‑engineering brain when paired with AI. Models monitor latency, jitter, loss and link utilization in real time, predict congestion before it happens and recommend adjustments to mitigate it. They can even suggest which circuits to drain for maintenance during low‑utilization periods and when to shift bulk transfers to off‑peak paths. The result is faster page loads, more stable SaaS performance, fewer outages and lower transit costs.

Network intelligence dashboard showing an interface‑utilization spike.

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4. Capacity planning

By combining real‑time and historical flow records, interface counters, application response times and even cloud‑billing data, network intelligence forecasts when and where links will saturate. It detects long‑term growth trends, learns demand cycles and runs “what‑if” scenarios (for example, launching a new SaaS region or weathering a marketing‑driven traffic surge).

With these insights, engineers can schedule upgrades months in advance, right‑size circuits, renegotiate contracts and shift traffic to off‑peak paths — avoiding both over‑ and under‑provisioning. Learn more about network capacity planning and how Kentik can help.

Network intelligence: Capacity‑planning report summarizing runout and utilization across backbone interfaces. Peaks approaching capacity are highlighted to show when upgrades are needed.

Capacity‑planning report summarizing runout and utilization across backbone interfaces. Peaks approaching capacity are highlighted to show when upgrades are needed

5. SLA violation prediction

Service‑level agreements often hinge on tight latency, throughput or packet‑loss budgets. Network intelligence computes rolling baselines for these metrics and predicts when an SLA is likely to be violated before the breach occurs. When telemetry deviates significantly from the learned baseline, the system measures the delta, flags it as an anomaly and can automatically alert operators or trigger remediation workflows.

Network intelligence: Graph illustrating a sudden increase in bandwidth on a downstream interface. The shaded region and callout identify the cause of the spike and the source port implicated in the violation.

Network telemetry is ingested over time to determine typical behavior for the network. When a change occurs, the system is able to dynamically identify, as opposed to using static thresholds, if it falls outside of the normal range and therefore out of SLA.

6. Troubleshoot connectivity and performance issues

Network intelligence synthesizes telemetry into a searchable context so engineers don’t have to pivot through dozens of dashboards. Kentik Journeys turns troubleshooting into an interactive dialogue: you ask in plain language, “Why can’t users in New York reach our SaaS front‑end?”, and the system automatically assembles the right flow queries, path traces and anomaly checks.

Because the conversational history is retained, follow‑up questions refine the scope (for example, filter by ASN or narrow to a five‑minute window) without losing context. This guided root‑cause analysis isolates not just the failing path but also why it failed and suggests remediations.

Network intelligence: Interactive query showing a traffic spike on a router with an explanation of what caused the increase. Two stacked charts break down the spike and the AI annotates the root cause.

Interactive query showing a traffic spike on a router with an explanation of what caused the increase. Two stacked charts break down the spike and the AI annotates the root cause.

7. Intelligent incident triage

When an incident occurs, a network‑intelligence platform treats every flow record, BGP update and device metric as part of a single, time‑aligned dataset. It clusters events that share key dimensions (common AS paths, identical loss signatures or matching application tags) and presents them as a coherent narrative rather than a flood of isolated alerts.

Anomaly‑detection policies run in the background, and each hit is tagged with application context. The conversational AI then guides engineers through the triage, refining queries on the fly and revealing the root cause. With predetermined baselines and automated mitigations, teams get faster triage and fewer all‑hands calls.

Network intelligence: Incident‑triage dashboard showing an unexpected drop in traffic. A cause‑analysis table lists the drop event, the magnitude of the change, the time it occurred and a summary tying the issue to specific applications and devices.

This image shows an unexpected drop in network traffic, an event that typically wouldn’t trigger an alert like a spike might. Kentik correlates and analyzes various types of telemetry, enriching the data with relevant metadata to provide context. This allows engineers to quickly understand the contributing factors—in this case, UDP traffic between two IPs tied to a specific application.

8. DDoS detection

Network‑intelligence engines ingest flow, metrics and threat‑feed enrichments at line rate, learning normal traffic volumes, protocols and source‑destination pairs for every interface, location, application and customer. When inbound packets suddenly surge beyond those baselines — whether due to a volumetric flood, SYN storm or slow‑rate application attack — the system flags the deviation, enriches it with path and geolocation context and raises a “possible DDoS” event. It can then auto‑trigger mitigation via RTBH, a scrubbing service or a third‑party CDN, shrinking time‑to‑defense and enabling closed‑loop protection. Learn more about DDoS detection and how Kentik can help.

Network intelligence: Alert overview for an ICMP flood attack. The graph shows packet rates exceeding both the baseline and a severe threshold, while the panel lists severity, state, start time and target details.

Here we see how the system has predetermined what “normal traffic” should look like, so that when a spike occurs, an automation kicks off to identify if the anomalous traffic matches any known malicious patterns. If it does, a programmatic mitigation will engage to remediate the attack and resolve the issue.

9. Identify potential security threats

By baselining regular network behavior, network intelligence quickly flags anomalous activity such as an infected device sending high‑volume uploads to unfamiliar IP ranges or a service dribbling data out at odd hours. Historical telemetry lets analysts pivot back weeks to determine if a spike is a harmless backup or potential exfiltration. Engineers can simply ask to view all flows matching an exfiltration policy over a given window, and the platform generates the appropriate queries across its data engine, enriched by threat feeds and machine‑learning models.

Network intelligence: Time‑series comparison of botnet and threat‑feed traffic. Purple shaded regions highlight anomalies where traffic matches known malicious patterns compared with a baseline.

Here we see multiple data points represented in a time series where they are compared to a dynamically-learned baseline as well as known malicious traffic definitions such as from threat feeds.

10. Reliable data for closed‑loop automation and orchestration

Network intelligence doesn’t just visualize telemetry—it provides a trustworthy data pipeline. Flow logs, BGP tables, SNMP counters, cloud metrics and host telemetry are ingested at line rate and passed through a built‑in extract‑transform‑load pipeline for normalization, enrichment, outlier filtering and feature extraction. This cleansed data is written into the Kentik Data Engine where queries return in sub‑second, machine‑learning‑ready form. Because Kentik owns the entire pipeline, operations teams don’t need to build or maintain a separate data lake. Orchestration and automation platforms can subscribe to the enriched event stream and launch remediation workflows (reroute around a flapping link, throttle a tenant, spin up extra cloud capacity) with confidence.

Network intelligence: Diagram showing a real‑time streaming data pipeline. Sources such as eBPF, NetFlow, SNMP and logs feed into stages that normalize, enrich, transform and route telemetry to destinations like Kentik, Kafka, Amazon S3 and Snowflake.

In a modern network intelligence solution, sources such as eBPF, NetFlow, SNMP and logs feed into stages that normalize, enrich, transform and route telemetry to destinations like Kentik, Kafka, Amazon S3 and Snowflake.

Bringing it all together with network intelligence

Network intelligence unifies every piece of telemetry—flows, routing updates, device metrics and synthetic tests—into a coherent story about how your network supports the business. It predicts outages before they hit, optimizes capacity, bolsters security and makes troubleshooting intuitive. Instead of frantically digging through data and constantly putting out fires, engineers gain superpowers: the ability to let machines handle repetitive analysis so they can focus on strategy and innovation. With costs optimized, performance assured and security enforced, your network becomes a smart, resilient foundation that propels the business forward.

How can Kentik bring the benefits of network intelligence to your organization?

Kentik offers a suite of advanced network intelligence solutions designed for today’s complex, multicloud network environments. The Kentik Network Intelligence Platform empowers network pros to monitor, run and troubleshoot all of their networks, from on-premises to the cloud.

Kentik’s network monitoring solution addresses all three pillars of modern network monitoring, delivering visibility into network flow, powerful synthetic testing capabilities, and Kentik NMS, the next-generation network monitoring system.

Start a free trial or request a demo to try it yourself.

Updated: August 13, 2025