A Guide to Cybersecurity Monitoring Tools

For growing organizations, security incidents aren't a matter of “if” but “when.” The longer attackers remain undetected in your environment, the more data they can steal and the more damage they can inflict. While preventive security controls are essential, they can't catch everything. Sophisticated threats will eventually find a way through.

Effective cybersecurity monitoring detects threats that slip past your defenses and provides the visibility needed to respond quickly. The challenge is building a monitoring stack with components that actually work together rather than creating a collection of tools that generate more noise than insight.

This guide walks through the essential components of a modern security monitoring architecture. We explore how these tools work individually and as an integrated system, using real-world scenarios to illustrate their practical application. 

These are some of the tools every enterprise SOC should be aware of to ensure complete cybersecurity coverage:

Cybersecurity monitoring architecture

The diagram above shows a layered approach to security monitoring. The architecture has four main layers:

1. Data Sources include all the systems that generate security-relevant information: endpoints, servers, network devices, cloud services, applications, and IoT devices. Each produces different types of data: logs, network traffic, system metrics, and user activity.

2. Collection Layer tools actively monitor these sources. EDR systems like OpenEDR watch endpoints for suspicious processes. IDS/IPS solutions like Suricata inspect network traffic for known attack patterns. Network monitoring tools like Zeek capture detailed communication records.

3. Telemetry Pipeline tools like Onum standardize, enrich, and route all this data. Raw security events are often inconsistent and lack context. This layer normalizes data and adds valuable information like threat intelligence before sending it to analysis systems.

4. The Analysis Layer is where everything comes together. SIEM platforms like Wazuh correlate events to identify attack patterns. SOAR tools like TheHive automate response workflows. Threat intelligence platforms like MISP provide context about known threats.

SIEM platforms centralize log data across infrastructure, correlate events, and detect threats through real-time analysis. They provide custom detection rules, threat intelligence integration, compliance reporting, and historical data analysis for threat hunting.
This enriched data would enable rapid correlation of suspicious transactions by geography and risk score across multiple backend systems during a suspected fraud incident.

For example, a Wazuh SIEM could be configured to detect several SSH login failures across different AWS regions within 10 minutes. Individual attempts could seem routine, but Wazuh's correlation engine would identify a coordinated brute-force attack pattern.

The SIEM would automatically block attacking IPs through firewall APIs and create infrastructure tickets to enable multi-factor authentication on affected servers.

{
  "timestamp": "2024-01-15T14:23:45.123Z",
  "rule": {
    "level": 10,
    "description": "Multiple SSH authentication failures from same source",
    "id": "5712"
  },
  "srcip": "203.0.113.45",
  "data": {
    "srcport": "42891",
    "dstport": "22",
    "protocol": "TCP"
  },
  "decoder": {
    "name": "sshd"
  },
  "location": "/var/log/auth.log"
}

Wazuh excels as an open-source SIEM. Its agent-based architecture provides deeper endpoint visibility than log-only solutions. Built-in compliance reporting supports PCI DSS, GDPR, and other regulatory frameworks.

Other options include the ELK Stack (Elasticsearch, Logstash, Kibana), which offers flexibility for custom security solutions. ELK core components are free, but advanced security features require paid subscriptions. Graylog provides a middle ground with a free open-source core and commercial enterprise features. Finally, Splunk dominates enterprise markets with powerful search capabilities and an extensive app ecosystem, though licensing costs can escalate with data volume growth.

IDS/IPS tools monitor network traffic in real time to identify malicious activity using signatures, behavioral analysis, or anomaly detection. While IDS alerts on threats, IPS automatically blocks malicious traffic before it can cause harm.

A company's Suricata deployment on core switches can detect suspicious outbound traffic from an internal workstation to a restricted IP. The system then immediately blocks the connection and alerts the SOC.

Further forensic analysis would reveal that malware was installed via a phishing email that bypassed email filters, while the quick blocking prevented data exfiltration.

alert tcp $HOME_NET any -> $EXTERNAL_NET any (msg:"ET TROJAN Possible Zeus Banking Trojan POST"; flow:established,to_server; content:"POST"; http_method; content:"&p="; http_client_body; pcre:"/&p=[A-Za-z0-9+\/]{200,}/"; reference:url,doc.emergingthreats.net; classtype:trojan-activity; sid:2013028; rev:2;)

Suricata combines IDS, IPS, and network security monitoring in one platform. Its multi-threaded architecture handles high-bandwidth environments, inspecting traffic at line rate on 10+ Gbps networks. Its file extraction capabilities enable advanced malware detection through protocol decoders.

Snort, the original open-source IDS, maintains an extensive rule ecosystem. Snort 3.x addressed performance limitations with multi-threading and improved preprocessing, though new deployments increasingly favor Suricata's broader functionality.

Zeek focuses on behavioral analysis over signature matching. While it can function as an IDS, its strength is generating rich network metadata for sophisticated analytics. Organizations often deploy Zeek alongside Suricata for comprehensive network visibility.

EDR systems continuously monitor endpoint activity, processes, file changes, network connections, and user actions to detect abnormal behavior. When identified, threats enable rapid response actions like network isolation, process termination, or forensic data collection.

Falco is the de facto standard for container security monitoring, using kernel-level visibility to detect anomalous behaviors. This CNCF-graduated project excels in containerized environments with strong Kubernetes integration, though it can monitor traditional hosts with reduced functionality.

Consider this example: An employee opened what appeared to be a routine Excel file. Falco immediately detected a spawned PowerShell process attempting external communication, indicating a suspicious child process pattern. An investigation revealed that the Excel file contained a macro executing a reverse shell. The machine was quarantined, leading to company-wide macro restrictions for untrusted files.

On the other hand, Velociraptor provides powerful DFIR capabilities through its flexible VQL query language, which enables custom detection and collection rivaling commercial solutions. Despite its steep learning curve, it excels at targeted investigations rather than continuous monitoring, making it valuable during active incident response.

Trivy complements runtime security by identifying vulnerabilities before deployment. This container-focused scanner integrates with CI/CD pipelines to catch vulnerable packages and misconfigurations during the build process, shifting security left in DevSecOps workflows.

NTA tools capture and analyze network flows to identify lateral movement, beaconing, and other suspicious activity. They detect unusual patterns that may bypass traditional perimeter defenses, providing visibility into attacker movements within the network.

Zeek transforms raw network traffic into structured, protocol-specific logs that document network activity in remarkable detail. Unlike traditional detection tools, Zeek creates indexed, searchable records of all network communications. Its scripting language enables custom detection logic for environment-specific threats, making it invaluable for forensic investigations.

For instance, a Zeek deployment can detect unexpected SMB connections between workstations that normally don't communicate. This lateral movement pattern would indicate an active ransomware deployment phase targeting their financial systems. The SOC would then trace the activity to a compromised domain admin account, revoke credentials, and contain the attack before encryption could begin.

These tools scan hosts, containers, networks, and applications for known vulnerabilities and misconfigurations using public databases like CVE. They detect unpatched software, outdated libraries, and weak configurations that attackers could exploit.

Trivy leads container environment scanning through CI/CD integration. It targets artifacts including container images, filesystems, and infrastructure-as-code files, and enables "shift-left" security practices. DevSecOps teams generally integrate Trivy into their CI/CD pipeline. Before deploying a new Kubernetes cluster, for instance, Trivy helps identify container image vulnerabilities and blocks the build.

OpenVAS provides comprehensive network-based scanning with over 50,000 vulnerability tests. Its authenticated scanning reveals vulnerabilities that external scans miss, with risk scoring to prioritize remediation. However, it requires significant deployment resources and has a steep learning curve.

Finally, OWASP ZAP focuses on web application security with passive and active scanning capabilities. Its intercepting proxy architecture supports automated and manual testing and has an extensive list of plugins.

These tools collect logs from all systems, standardize formats, and route them to storage and analysis platforms. They consolidate data from infrastructure, servers, applications, network devices, and security tools, enabling efficient monitoring and threat detection.

Fluentd is a standard log collection solution with an extensive plugin ecosystem and CNCF graduation status. Its tag-based routing enables sophisticated log handling, directing different log types to appropriate systems. While resource-intensive, its stability makes it reliable for production environments.

For example, Fluentd can be used to aggregate logs from multi-region AWS infrastructure into a centralized ELK stack. During a forensic investigation of unauthorized IAM policy changes, the SOC can quickly identify the change initiator and timestamp. Without centralized logging, this correlation would have taken hours across multiple AWS accounts and regions.

On the other hand, Logstash provides powerful transformation capabilities through pipeline architecture and Grok pattern matching for structured data extraction. Its Java-based architecture consumes significant resources, leading many organizations to use it as a centralized processing tier rather than a distributed deployment.

These tools gather numerical data like CPU, memory, disk I/O, network throughput, and service response times to detect performance anomalies, capacity issues, or potential system failures that could create security vulnerabilities.

Prometheus is commonly used for metrics collection in containerized environments. Its dimensional data model enables high-cardinality metrics collection without performance degradation, while PromQL provides sophisticated analysis and alerting capabilities. If an API experiences performance degradation after a deployment, Prometheus could be used to see memory usage on the node and trigger alert thresholds that would help engineers identify a memory leak in new code. 

While not directly a security event, degraded services become targets for opportunistic denial-of-service attacks, making performance monitoring a security concern.

# PromQL query showing memory leak detection

# Alert rule configuration
- alert: MemoryLeakDetected
  expr: increase(container_memory_usage_bytes{container="some-api"}[1h]) > 100000000
  for: 15m
  labels:
    severity: warning
  annotations:
    summary: "Memory leak detected in {{ $labels.container }}"
    description: "Memory usage increased by {{ $value }} bytes in the last hour"

Grafana typically complements Prometheus for advanced visualization and longer retention requirements.

Grafana dashboard showing metrics

These tools capture and visualize request paths across distributed microservices, tracking timing, dependencies, and errors. They provide end-to-end visibility to identify performance bottlenecks, pinpoint failures, and detect security issues across interconnected services.

Jaeger provides comprehensive distributed tracing for microservices with specialized collection, storage, and visualization components. This CNCF-graduated project offers sampling strategies to balance visibility against performance impact, with OpenTelemetry integration for polyglot environments. However, it requires significant operational investment for high-availability deployments. It represents industry convergence on vendor-neutral observability standards, providing standardized instrumentation that exports to various backends, including Jaeger, Zipkin, and commercial platforms. 

A payment processing service experiencing intermittent errors can be analyzed using Jaeger traces, which might reveal latency spikes when requests route through a newly deployed microservice. Upon discovering that the service contains a vulnerability, developers can immediately roll back the deployment and apply security fixes before redeployment.

These pipelines ingest, enrich, transform, and route logs, metrics, and traces to appropriate destinations. They process observability data in real time, adding context, filtering noise, and delivering information to storage, analysis, or monitoring platforms for efficient, scalable observability.

Onum provides a managed platform designed specifically for security data, with built-in enrichment capabilities. It automatically enhances raw telemetry with threat intelligence, geolocation, and asset details, dramatically improving downstream analytics effectiveness while eliminating operational overhead through its managed service model.

Onum can process telemetry using a very flexible pipeline building interface. Each logged transaction event gets enriched with geolocation data, device fingerprints, and threat intelligence before routing to its SIEM, metrics store, or fraud detection system.

pipeline building interface

This enriched data would enable rapid correlation of suspicious transactions by geography and risk score across multiple backend systems during a suspected fraud incident.

SOAR tools automate incident response tasks and integrate with alerting, ticketing, and enrichment systems. They coordinate workflows across multiple tools, automating repetitive tasks like gathering context, updating tickets, and executing response actions to reduce response times and improve consistency.

TheHive demonstrates open-source SOAR value by transforming manual processes into automated workflows. Its integration capabilities with SIEM, threat intelligence, and ticketing systems enable comprehensive incident response automation, helping address the persistent shortage of skilled security personnel through efficiency gains.

TheHive can automatically trigger a response workflow if a high-priority phishing alert is detected. The system can look up the suspicious domain in threat intelligence sources, check EDR logs for employee interactions, and create a Jira ticket for follow-up. The system can automatically isolate an infected machine when malware is detected. What previously took minutes now completes in a few seconds.

The SOAR market increasingly emphasizes low-code/no-code automation, making security automation accessible to analysts without programming expertise while offering remarkable flexibility for organizations with development resources.
This enriched data would enable rapid correlation of suspicious transactions by geography and risk score across multiple backend systems during a suspected fraud incident.

Cybersecurity monitoring tools must function as a unified ecosystem to be effective. Each tool produces different telemetry types that must be normalized and correlated to provide analysts with clear system behavior visibility, enabling automation for reliable and scalable implementation.

Suppose the EDR detects suspicious endpoint activity. The alert is enriched with contextual log data from the SIEM, correlated against threat intelligence indicators, and automatically passed to the SOAR platform for containment actions.

Data formats must also be standardized using structured logging (JSON), timestamps normalized across time zones, and identity data consistent for correlation mapping across cloud providers, Active Directory, and local systems.

Telemetry pipelines play a critical role by preprocessing and routing data to appropriate destinations, ensuring that no tool operates in isolation. Poor coordination often results in alert fatigue and missed detections.

Effective cybersecurity monitoring requires combining complementary tools, integrated implementation, and continuous refinement. Organizations can build comprehensive coverage across their systems by understanding each tool's strengths and limitations.

However, building an effective monitoring stack involves practical challenges that require planning:

1. Security tools generate massive data volumes that can quickly become expensive. If not configured correctly, logs can drive costs up considerably.

2. Poorly configured rules create noise that masks real threats. Start with conservative thresholds and gradually increase sensitivity based on your environment's baseline behavior to avoid alert fatigue.

3. Open-source tools offer cost savings but demand significant expertise for deployment and maintenance. If internal resources are limited, budget for training or consider managed solutions.

4. Monitor your monitoring tools themselves. Set up capacity alerts for SIEM storage, telemetry pipeline throughput, and analysis platform performance before they become bottlenecks.

The open-source tools highlighted in this guide provide a cost-effective foundation but are complex and require a learning curve. Commercial alternatives like Onum offer ready-made solutions for organizations with limited internal resources.

Successful cybersecurity monitoring depends on transparent processes, skilled personnel, and executive sponsorship for security initiatives. The architecture shown in this guide provides a roadmap, but success comes from thoughtful implementation and continuous improvement.