Cybersecurity Monitoring: Patterns, Metrics, Tools & Architecture

Cybersecurity monitoring is a subset of observability that focuses on detecting and responding to security threats using your telemetry data. While observability uses telemetry to provide insights into overall system behavior, performance, and reliability, cybersecurity monitoring applies these same principles and tools to enable cyber threat detection. This targeted use of existing observability tools can help eliminate the need for parallel or specialized monitoring systems; the telemetry sources that help debug your performance and error issues can also detect security incidents. 

This article explains how to use your observability stack and telemetry pipeline for cybersecurity monitoring and covers cyber attacks, threat detection patterns, and metrics relevant to cybersecurity.

Many companies maintain separate tooling for cybersecurity monitoring and observability, leading to duplicated effort, increased cost, and fragmented visibility. This separation stems from historical practices where security teams operated independently from operations and engineering teams. However, the telemetry needed for cybersecurity monitoring largely overlaps with performance and reliability monitoring; the difference lies primarily in how this data is viewed, analyzed, and acted upon.

A more integrated approach leverages the existing observability infrastructure for security use cases. For example, the same distributed tracing data that helps debug service latency issues can identify unusual access patterns or potential data exfiltration attempts. Application metrics that track error rates and resource utilization can be used for performance monitoring and denial-of-service detection. Log aggregation systems collect application errors for developers and are equally valuable for detecting injection attempts and application attacks.

Success with this integrated approach requires organizational changes alongside technical ones. Security teams must collaborate closely with platform and operations teams, sharing access, tools, and practices rather than maintaining separate stacks. This means joint planning for telemetry collection, shared access to observability platforms, and coordinated incident response procedures. While security teams may still maintain specialized tools for particular needs like threat intelligence and vulnerability scanning, the core observability infrastructure becomes a shared resource serving multiple teams' needs.

The shift toward infrastructure as code and automated deployments further reinforces this integration. Security controls and monitoring standards can be embedded directly into service definitions and deployment pipelines, making security observability a built-in feature rather than an afterthought. This “shift-left” approach to security ensures; 

•  Comprehensive coverage

•  Minimized operational overhead

• True real-time monitoring and the ability to act 

 Shifting left from analytics platforms enables faster response to high-priority issues.

Cyber attacks target systems at multiple layers: 

• Network infrastructure (DDoS, port scanning, DNS attacks), 

• Application layer (injection attacks, API abuse),

• Authentication systems (credential attacks, session hijacking), 

• System level (malware, rootkits, container escapes), and 

• Data layer (SQL injection, exfiltration, ransomware, privacy violations). 

We will examine each threat's key observability metrics and detection approaches. Analyzing these signals, ideally in real time and at the front-facing edge of your infrastructure, is essential to protecting against cyber attacks. 

Network infrastructure attacks target the foundational layers of connectivity, attempting to disrupt service availability or gain unauthorized network access through protocol and routing manipulation. 

The following table examines the different threat patterns and their metrics/responses. We’ll use this format to review all the different types of threats.

These attacks exploit vulnerabilities in web applications and APIs, attempting to manipulate application logic, inject malicious code, or abuse application functionality.

Authentication attacks focus on compromising user identities and access controls, using techniques ranging from brute-force attempts to sophisticated session manipulation.

System-level attacks target the underlying computing infrastructure, attempting to compromise hosts, containers, and system processes to gain persistent access or execute unauthorized code.

Data-focused attacks aim to exfiltrate sensitive information, encrypt systems for ransom, or exploit system resources for unauthorized purposes like cryptocurrency mining.

Security monitoring pipeline architectures are increasingly responsible for adding vital threat intelligence and business context to raw telemetry data. To support this alongside other security use cases, effective security monitoring pipelines start with edge filtering to reduce noise and unnecessary data processing. Their architectures are specifically designed to support real-time processing capabilities and maintain scalable data ingestion pathways while context enrichment happens throughout the pipeline.

Pipeline implementation must balance multiple factors, including data retention requirements, which vary by data type and compliance needs. A well-constructed pipeline will provide the right data to downstream tools in an optimized way. For example, if Splunk's Common Information Model (CIM) and Palo Alto Networks (PAN) plug-ins are used, certain fields must be propagated, or the downstream analytics will fail.

Processing latency targets for pipelines should be defined based on threat detection requirements, while cost optimization strategies must account for data volume and processing needs.

Real-time security analysis requires performance tuning and filtering across the entire stack. This includes implementing strategic caching layers to reduce lookup times, deploying parallel processing for high-volume telemetry streams, and optimizing memory usage patterns. Processing at the edge and queue management become critical at scale, requiring careful balancing of processing throughput against resource utilization. This can be very complex to set up, and it’s difficult to maintain real-time response times. Thankfully, platforms like Onum, among others, make it easier to manage real-time distributed telemetry pipelines. 

Cybersecurity monitoring relies on a wide variety of custom tools for scanning, detection, and response. The table below summarizes the key areas of functionality and provides examples of representative tools for each category. 

Security monitoring requires deep collaboration among security, development, and operations teams. This includes establishing shared monitoring practices, coordinating incident response procedures, and maintaining consistent tooling across teams. Knowledge- and data-sharing processes must be formalized, especially around instrumentation design, detection engineering, and threat response. Inter-team communication can be much smoother if everyone has the same context and talks about the same underlying data. 

Standardizing methodologies across teams minimizes blind spots, reduces detection gaps, and accelerates resolution times.

Effective collaboration prevents redundant investigations and reduces time wasted on false alarms triggered by legitimate activities from other teams. A shared observability stack fosters transparency, providing consistent access to relevant telemetry and security insights for all teams. As organizations scale, leveraging a unified platform ensures the seamless integration of security capabilities without requiring each team to develop bespoke solutions. Cross-functional alignment on security observability promotes efficiency, reduces operational overhead, and enables proactive threat mitigation across the organization. Leveraging your existing observability stack can make scaling across your organization much smoother since it prevents each team from developing bespoke solutions.

In this article, we have explored various cybersecurity threats, from network intrusions and application vulnerabilities to authentication exploits and data breaches. These threats pose real risks, and history has shown that even the largest organizations are not immune. Below are a few examples:

By extending existing observability practices with security-focused tools and proactive monitoring, you can detect and mitigate threats before they escalate. 

We have shown that effective cybersecurity monitoring doesn't require an entirely separate toolset from your observability platform. By extending existing monitoring practices with security-focused collectors and processors, you can achieve comprehensive threat detection while reducing costs and maintaining operational efficiency. It’s essential to use a powerful analytics tool to extract cybersecurity metrics from your observability stack efficiently, so we recommend a tool like Onum to solve the complexity of integration. A well-integrated observability stack and advanced analytics tools ensure real-time threat detection and the best attack protection.