6 Best Practices for Tuning Network Monitoring Alerts

This guest post is prepared by ilert partner Auvik and written by Auvik's Product Marketing Manager, Nolan Greene.

Network monitoring and alerting provide the foundation for efficient IT operations and cyber resilience. By keeping track of the status and performance of network infrastructure and applications, network monitoring tools can automatically generate alerts when defined thresholds are exceeded or specific events occur. These network monitoring alerts allow IT teams to detect outages, performance degradation, and potential security incidents so they can respond swiftly to minimize disruption.

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However, a major challenge with network monitoring alerts is the high volume of notifications generated from the myriad of devices and systems in modern IT environments. IT teams can receive up to millions of alerts per day from firewalls, intrusion detection systems, servers, applications, and more. 

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Many of these alerts are redundant, irrelevant, or signify minor issues that do not require intervention. This deluge of alerts leads to “alert fatigue,” where IT staff start ignoring or dismissing alerts because they cannot keep up with the overwhelming volume. Critical alerts get lost in the noise, allowing major problems to go undetected. Excessive alerts waste IT resources, increase mean-time-to-resolution (MTTR) for major incidents, and ultimately leave organizations vulnerable to outages and breaches.

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To address these challenges, organizations need to optimize their network monitoring alerts through a process known as network alert tuning. Well-tuned network alerts are accurate, relevant, and actionable, allowing IT teams to focus on the most significant events. Alert tuning is crucial for building an efficient IT operations team and enhancing cyber resilience.

What is network alert tuning?

Network alert tuning involves configuring monitoring tools to generate network monitoring alerts that are meaningful and appropriate for an organization’s specific IT environment and security policies. This includes setting thresholds for alerts, eliminating unnecessary notifications, mapping alerts to real-world impacts, and tagging alerts based on severity and priority levels.

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The main goals of network alert tuning include:

• Reducing false positives and alert noise
• Focusing on alerts that require investigation and intervention
• Speeding up response times by highlighting the most critical events
• Mapping alerts to business impacts for greater relevance
• Optimizing alerts based on the organization's unique tech stack and security risks

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Effective network alert tuning requires both a deep understanding of the IT infrastructure and the ability to analyze large volumes of performance data and events. While proper alert configuration requires some initial overhead, the long-term benefits include more efficient operations, reduced risk of breaches, and optimized use of IT resources.

6 best practices for tuning network monitoring alerts

Effective network monitoring relies on timely, accurate alerts tailored to an organization's infrastructure and risks. However, poorly configured out-of-the-box alerts often fail to meet these criteria resulting in alert fatigue.

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Tuning and optimizing network alerts is essential but can be challenging without a systematic approach.

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The following 6 proven best practices provide a framework for maximizing the value of network monitoring alerts:

1. Maintain accurate network mapping

In Auvik Network Management, mapping means a virtual representation of how every network device (switch, router, firewall, wireless AP, and controller) is connected within the network and how they all relate to each other.

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Mapping provides a detailed visualization of the network topology down to ports and cables. It discovers all configured network infrastructure and shows traffic flows between devices. Mapping illustrates dependencies and relationships between critical systems.

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Mapping is crucial because you can't tune or optimize alerts for a network environment that is incomplete or inaccurate. Manual mapping methods involving spreadsheets quickly become outdated as infrastructure changes. They are error-prone due to technicians mis-recording connections or missing devices. Legacy maps often fail to include new systems added during upgrades.

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Without an accurate network map, there will be blind spots and gaps in visibility. Certain devices won't be monitored and alerted properly. IT teams can't optimize alerts if they don't have full awareness of what systems comprise the infrastructure.

Network mapping optimization tips

To build an accurate and actionable network map that powers effective alert tuning, consider the following tips:

• Adopt an automatically generated and updating mapping solution that discovers new network devices as soon as they are added.
• Utilize automated mapping tools that provide a real-time view of device connections and dependencies.
• Ensure mapping data is continuously monitored and updated rather than relying on periodic manual reviews.
• Integrate mapping into monitoring and alerting systems so alerts can reference device locations and relationships.
• Enrich mappings with hardware inventory, configurations, and performance data for contextual alerting.
• Maintain comprehensive documentation of network segments, subnets, and traffic flows referenced in alerts.

2. Establish performance baselines

Baseline establishment involves measuring and documenting the normal behavior and performance of the network infrastructure under regular conditions. This provides a starting point to compare against tuning alerts.

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Metrics gathered during baseline establishment may include:

• Average and peak network traffic and bandwidth utilization.
• Number of concurrent connections and sessions.
• Bandwidth, latency, jitter for critical applications.
• CPU, memory and storage utilization of key systems.
• Uptime and responsiveness of essential services.
• Frequency and types of events and alerts generated.

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Establishing a usage and performance baseline is crucial because it is difficult to determine what constitutes normal vs. abnormal behavior without an understanding of day-to-day operations. Baselines quantify expected variations based on time of day, day of week, and seasonality.

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Without defined baselines, anomaly detection is unreliable. Alert thresholds can only be approximated, which leads to excessive false positives or missing real issues. Every network has unique characteristics, so requires custom alert tuning.

Baseline establishment optimization tips

• Monitor network activity for 2–4 weeks, capturing all key performance indicators.
• Analyze traffic patterns, loads, and events to identify peaks, valleys, and trends.
• Define acceptable ranges for each metric with upper and lower thresholds.
• Identify baseline variances by location, application, role, and other factors.
• Store baseline data to allow comparison with future periods.
• Review and update baselines quarterly to account for network growth and changes.
• Integrate baselines into alerting rules to accurately detect anomalies.

3. Implement continuous monitoring and periodic reviews

Continuous monitoring means having network monitoring and alerting systems actively inspect the IT infrastructure 24/7. This enables the immediate detection of performance issues, outages, and security events as they occur. 

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It provides:

• Real-time visibility into network activity and events.
• Rapid notification of incidents requiring intervention.
• Identification of transient anomalies versus persistent threats.
• Correlation of related events across multiple monitoring tools.
• Historical data for identifying trends and emerging risks.

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Continuous monitoring is essential because network threats and anomalies can appear and escalate rapidly. Human operators cannot monitor networks consistently. Many critical events only last seconds or minutes. Without automated continuous monitoring, abnormal incidents will go undetected, allowing them to worsen.

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Periodic reviews are also necessary to analyze collected monitoring data and alerts for insights not discernible in real time. Reviews help assess the overall effectiveness of alert rules and thresholds. They identify tuning opportunities based on evolving traffic patterns, new applications, and changes in network scale. Periodic reviews are an opportunity to reflect on lessons learned from past incidents and include them in alert optimizations.

Continuous monitoring and periodic review optimization tips

• Implement single-pane-of-glass monitoring to aggregate alerts enterprise-wide.
• Tune alert rules to minimize false positives based on baselines.
• Correlate alerts with threat intelligence to identify critical events.
• Review all monitoring data and alerts weekly/monthly for deeper insights.
• Maintain dashboards analyzing alert volumes, types, and trends.
• Monitor tuning metrics like false positives, response times, and analyst feedback.
• Update alert rules based on learnings from periodic reviews.
• Automate reporting to remove manual effort and ensure consistency.

4. Prioritize and eliminate redundant alerts

Prioritization involves classifying incoming alerts based on severity and potential business impact so that IT teams know which events require immediate investigation. Alerts are typically grouped into high, medium, and low priority levels.

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Eliminating redundant alerts means tuning alert rules to minimize duplicate or excessive notifications from the same underlying issue. For example, configuring grouping and aggregation to avoid alert storms.

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Prioritization and elimination of redundancies are crucial because the volume of incoming alerts can easily overwhelm IT security and operations teams. Alert fatigue sets in when staff are bombarded with a barrage of notifications. Important alerts get overlooked, leading to delays in response.

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Prioritizing alerts allows teams to focus on the most severe events first. Consolidating related alerts into a single notification reduces duplicated efforts. Eliminating unnecessary alerts removes distractions, enabling analysts to use their time efficiently.

Prioritization and reducing alert redundancies optimization tips

To avoid alert fatigue and ensure critical incidents are addressed promptly, consider the following tips:

• Categorize alerts based on IT asset criticality and security threat levels.
• Design dashboards and views filtered by priority levels and categories.
• Route high-priority alerts to senior engineers on-duty 24/7.
• Configure intelligent de-duplication and aggregation of related alerts.
• Increase thresholds or disable low-value alerts with excessive false positives.
• Identify and blacklist persistent false positive alerts.
• Establish optimal alert volumes per device/service to avoid overload.
• Continually gather feedback from analysts to identify tuning opportunities.

5. Incorporate contextual data

Contextual alerting refers to data-driven alerting, where systems collect information about users, applications, and devices to establish normal behavior baselines. This contextual data is then used to detect anomalies and generate more intelligent, accurate alerts.

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Contextual data provides the situational awareness needed to understand what is "normal" versus unusual activity that warrants alerts. By leveraging historical data and machine learning algorithms, contextual alerting can account for regular fluctuations and adapt alerting thresholds dynamically.

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Contextual alerting is important because predefined static thresholds often generate excessive false positives or miss real threats. User behavior, application workloads, and network traffic all vary over time. A static threshold leading to an alert in one context could be meaningless in another. Contextual data enables the establishment of dynamic and personalized baselines tailored to specific users, systems, and time periods.

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Alerts without context lack the insights needed for IT teams to prioritize and investigate them efficiently. Contexts such as user identity, location, peer group activity, and timing provide vital clues for determining the severity and validity of alerts.

Contextual alerting optimization tips

Consider the following tips when implementing contextual alerting:

• Collect rich contextual data sources such as logs, network traffic, endpoint telemetry, and user credentials.
• Leverage machine learning to establish dynamic thresholds customized to specific users, applications, and devices.
• Correlate alerts with contextual data like user identity, peer group activity, timing, and network topology.
• Prioritize alerts using contextual factors such as user risk score, vulnerability criticality, and asset value.
• Include contextual information like user department, system owner, and location details within alerts.
• Continuously refine contextual baselines and algorithms to account for changing behaviors over time.
• Balance reliance on contextual data with maintaining visibility across all systems and accounts.
• Provide tools for IT teams to efficiently search, analyze, and visualize contextual data related to alerts.

6. Leverage threat intelligence

Threat intelligence refers to analyzed information about potential security threats that can inform an organization's defenses. Threat intelligence provides insights into the tactics, techniques, and procedures used by attackers based on research into emerging risks, malware, hacking forums, and real-world attacks against other organizations.

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By leveraging threat intelligence, organizations can improve situational awareness about the risk landscape and configure controls to detect and respond to the latest attack methods proactively. Ongoing collection and monitoring of threat intelligence enables security teams to continuously tune defenses aligned with evolving threats.

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Threat intelligence is important because it provides actionable information to security teams that would be difficult or impossible to gain through internal monitoring alone. Research into the dark web, reverse engineering of malware payloads, and monitoring of hacker communications can uncover imminent threats well before they reach an organization’s doors.

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Threat intelligence fills knowledge gaps and supplements internal telemetry, enabling stronger threat detection, alerting, and mitigation capabilities. It can feed into threat-hunting exercises and help train machine-learning models to recognize new attack variants and techniques in their early stages.

Threat intelligence optimization tips

To gain maximum value from threat intelligence, organizations should consider the following tips:

• Prioritize the collection of threat intelligence aligned with business risk assessments and security roadmaps.
• Ensure threat intelligence feeds provide strategic insights as well as tactical, technical details on adversary tradecraft.
• Establish processes to rapidly disseminate threat intelligence to security monitoring, alerting, and response teams.
• Integrate threat intelligence into security controls like SIEMs, firewalls, and endpoint detection and response (EDR) tools.
• Leverage threat intelligence to continuously fine-tune detection rules, anomaly thresholds, and risk-scoring algorithms.
• Enrich alerts with threat intelligence details like related campaigns, adversary TTPs, and MITRE ATT&CK classifications.
• Measure coverage and value gained from threat intelligence platforms and feeds.
• Participate in information-sharing communities to contribute internal threat intelligence for the benefit of others.

Focus attention where it's needed with network alert tuning

Effective network monitoring alerts are the linchpin enabling IT teams to maintain resilient infrastructure and robust security. However, poorly tuned alerts create distractions and false alarms instead of powering efficient operations. Organizations must invest in continuous optimization of monitoring systems to generate meaningful alerts. 

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Actionable alerts focus analyst attention on events requiring intervention. Well-tuned alerts incorporate context, priorities, de-duplication, and threat intelligence to highlight the most critical incidents. With properly configured alerts, you gain an early warning system and avoid alert fatigue, helping you and your team detect and fix issues before they escalate.

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