Observability Improves Cybersecurity

What is observability?

Observability has several meanings and applications. Overall, it is a management strategy that prioritizes the most important and fundamental issues in a business operating process. This means it’s a valuable tool for improving user experience, application availability, and performance. It can reduce operating costs by easing the management of adverse conditions and reducing irrelevant or redundant information. This is particularly evident in larger business operations with large operational teams.

Observability practices also provide helpful information in managing reliability and performance, infrastructure design, and tool selection, helping to understand the internal state of a complex system, and enabling faster and more accurate navigation from identified performance problems to their root causes without additional testing or coding. 

Is observability the same as monitoring?

Observability also refers to software processes that separate critical data from routine information. It can be used to extract and process information critical to higher-level operating system architecture. From this description, it might seem that observability is akin to monitoring. 

Monitoring involves collecting, analyzing, and using information to track progress toward objectives, focusing on specific metrics. Observability involves understanding the internal state of the entire system through analyzing generated data, such as logs, metrics, and traces.

Difference 1

The two share some characteristics, but these might best be labeled as complex: monitoring relies on that to gather, and in a passive manner, a wide variety of data, much of which is futile. Observability, however, acts as a precision instrument because it actively collects the relevant data, which allows managers to make better operational decisions and actions.

Difference 2

Monitoring is primarily an operational function that examines a system’s internal performance and reports problems. Observability goes beyond this by examining server logs, traces, events, and indicators to determine the escape process and identify culprits responsible for spikes in CPU usage. 

Observability uses collected data to identify what is wrong and why, automating the tedious tasks of manually analyzing, correlating, and locating errors. It also incorporates advanced features like data correlation, distributed tracing, and anomaly detection. Observability can reveal what former Secretary of State Donald Rumsfeld described as “unknown unknowns”—issues that the DevOps team may not have ever considered because monitoring focuses on a system’s known state.

Difference 3

Where monitoring gathers information from available sources, such as management information, APIs, and logs, observability often adds new access points to gather essential information. Monitoring focuses on infrastructure, while observability focuses on applications, often including workflows. 

As the name implies, the challenge of observability comes from making the right observations. Observability assumes that data sources contribute to an analytical process that reflects the status of an application or system. 

Thus, observability is crucial for companies that control and depend on complex distributed systems, as it provides better insight into the system’s functionality. By focusing on the status of a system rather than the status of the individual elements of that system, observability improves the user experience by prioritizing existing critical data and linking it to key performance indicators (KPIs).

Difference 4

Of course, observability does need adequate monitoring tools and associated practices. Collecting data on overall health, security, hardware of a system host operating system, and other data needed for visibility is still important. The choice of tools and the specific data collected depends on the monitoring objectives and how the data will be used for observability. The tools are software applications used to collect and aggregate data, while policies guide and focus the collection of data necessary for visibility and define how data is protected and stored.


Still, unlike monitoring, where the idea of ‘more is better’ might apply, observability means keeping track of what is most relevant. Thus, monitoring and observability address the same problem from two different perspectives: monitoring provides situational awareness, while observability helps identify what is happening and what to do about it. When used in a complementary manner, these approaches can improve company results. 

Security and observability

Security is a critical aspect that intertwines with observability. By integrating security measures with observability practices, businesses can enhance their ability to detect, respond to, and mitigate cyber threats. Observability provides the necessary visibility into systems and applications, allowing for the detection of unusual patterns or anomalies that could indicate a security breach. This proactive approach enables quicker identification and response to potential threats, thereby reducing the impact of cyber-attacks.

Moreover, observability tools can be configured to include security-specific metrics and logs, such as unauthorized access attempts, abnormal data transfers, and other indicators of compromise. This comprehensive monitoring ensures that security incidents are not only detected but also understood in the context of the overall system’s performance and health.

Observability platforms often come equipped with advanced security features like encryption, authentication, and access controls, which safeguard the collected data against unauthorized access and tampering. By maintaining the integrity and confidentiality of the data, businesses can ensure that their observability efforts also support their overall cybersecurity strategy.

Observability security with ML and AI

Additionally, integrating security into observability involves using advanced machine learning (ML) and AI techniques. These technologies can analyze vast amounts of observability data to identify patterns that might indicate a security threat. 

For example, ML models can be trained to detect anomalies in network traffic that could suggest a distributed denial-of-service (DDoS) attack or to identify unusual user behavior that might indicate a compromised account.

Another critical aspect is the implementation of real-time alerting and automated response mechanisms. When observability data indicates a potential security threat,

the system can trigger alerts to the security operations center (SOC) and, if predefined conditions are met, automatically initiate response actions such as isolating affected systems, blocking malicious IP addresses, or escalating the issue for human intervention.

Observability platform

To implement observability and include it in cybersecurity efforts, companies must first adopt an overall strategy or plan. The plan should identify the desired benefits, link them to necessary data, and consider available data from monitoring and telemetry. The plan can then be fulfilled through an appropriate ‘architecture,’ leading to the observability platform. 

The observability architecture should represent the relationship between source data and presentation to operations personnel, artificial intelligence, and machine learning systems. Proprietary and open-source tools are available for monitoring and observability, but options are also designed to address specific targets – presumably, this would be the choice for many enterprises. 

The final implementation step is a specific observability platform. An observability platform is an integrated software application that collects information, performs analytics and presents actionable results to operations users.

Such a platform is built with a set of monitoring tools or capabilities, operated by either a person—that is, a human; in this artificial intelligence (AI) age, it seems worth remembering—or even a customized separate software layer for collective analysis. The value of observability depends on implementing these three implementation steps in order. Skipping or skimping could put the concept and investment at risk.

In summary, incorporating security into observability practices provides a holistic view of the system’s state, enhancing the ability to maintain robust security postures while ensuring optimal performance and reliability. This integration is essential for businesses to protect their assets in today’s complex and threat-prone digital landscape.

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