What Is Log Management? Process & Tools

What Is Log Analysis? Process, Techniques, and Best Practices

Log analysis is the process of reviewing, interpreting, and understanding logs generated by systems, networks, and applications. These logs are like the digital footprints of every action that takes place within a system. They contain valuable information that can help us understand what’s happening in our IT environment, from identifying potential security threats to troubleshooting performance issues.

Log analysis can be done manually or by using specialized log analysis tools. Manual log analysis, while possible, can be time-consuming and error-prone, especially when dealing with large volumes of logs. On the other hand, log analysis tools automate the process, making it faster and more efficient. They can quickly process thousands of log entries, highlighting the ones that need your attention.

Benefits of Log Analysis 

Managing Security Events and Incidents

One of the primary benefits of log analysis is related to security. By regularly analyzing logs, you can identify unusual activities that could signal a potential security threat. For instance, multiple failed login attempts from a single IP address could indicate a brute force attack. By detecting such threats early, you can take action before they escalate into major security breaches.

In addition to threat detection, log analysis also aids in incident response. In the event of a security breach, logs can provide crucial information about the incident, such as how the attacker gained access, what they did, and when they did it. This information can guide your response efforts and help prevent similar incidents in the future.

Improved Troubleshooting

Log analysis can also significantly improve troubleshooting efforts. For example, when system performance issues arise, logs can provide insights into what’s causing the problem. They can reveal patterns and trends that might not be immediately apparent, allowing you to identify the root cause and take appropriate action.

For example, if your application is running slower than usual, log analysis might reveal a sudden spike in database queries or something like it. With this information, you can investigate further to determine why the increase in queries is happening and how to address it.


In many industries, maintaining compliance with various regulations is a critical aspect of operations. Regulations like GDPR, HIPAA, and PCI DSS require organizations to keep detailed logs for a specific period of time and be able to produce them upon request. Log analysis can help ensure that your logs meet these compliance standards. It can also provide evidence of compliance, should you need to prove it during an audit.

Learn more: Read our guide to log management best practices

The Log Analysis Process 

Log analysis typically includes the following stages:

1. Data Collection

The first step in the log analysis process is data collection. This involves gathering log data from various sources, such as servers, network devices, and applications. The data can be collected manually, but it’s often more efficient to use automated tools that can collect and centralize the logs in one place.

2. Data Indexing

Once the data is collected, the next step is data indexing. Indexing involves organizing the log data in a way that makes it easier to search and analyze. This usually involves categorizing the data based on various attributes, such as timestamp, source, and event type and then normalizing and parsing the order of the common fields against other log types. Proper indexing is crucial for efficient log analysis, as it allows you to quickly locate relevant log entries when needed.

3. Analysis

After indexing, the log data is ready for analysis. This is where you delve into the logs to extract valuable insights. You might look for patterns or anomalies that could indicate a problem or a security threat. You might also use the logs to answer specific questions, such as “Why is our application running slow?” or “Who accessed this file last night?”

4. Monitoring

Monitoring is a continuous part of the log analysis process. It involves keeping an eye on the logs to detect any unusual or suspicious activities. This can be done manually, but it’s typically more efficient to use log monitoring tools that can alert you when certain conditions are met, such as a sudden increase in error logs or multiple login attempts from an unusual location.

5. Reporting

The final stage of the log analysis process is reporting. This involves summarizing the findings of your analysis in a clear and concise report. The report might include charts, graphs, or live dashboards to visualize the data and make it easier for humans to understand, especially as changes over time. It might also include recommendations for action based on the findings.

Log Analysis Techniques and Methods 

Pattern Recognition

Pattern recognition in log analysis is like finding a needle in a haystack. It involves identifying patterns or trends in the log data that might indicate a problem or anomaly. Pattern recognition algorithms are often used to make this process easier and more efficient. They can help identify common patterns such as repeated failures, unusual activity, or spikes in resource usage.

Pattern recognition is not only about identifying problems, it also helps in predicting future trends. For example, if your log data shows regular spikes in server load at certain times of the day, you can use this information to anticipate and manage peak periods.

Anomaly Detection

Anomaly detection is a critical aspect of log analysis. It involves identifying unusual or abnormal behavior in the log data that deviates from the norm. This could be anything from a sudden surge in traffic to an unexpected system failure.

Anomaly detection can be challenging as it requires a good understanding of what constitutes ‘normal’ behavior. This is where machine learning algorithms can be useful. They can learn from historical data and identify anomalous behavior based on statistical models. Regular tuning and updating of these models is important to ensure they remain accurate and effective.

Root Cause Analysis

Root cause analysis is the process of identifying the underlying cause of a problem or issue. It involves analyzing log data to trace the sequence of events that led to the problem. This can be a complex and time-consuming process, especially when dealing with large volumes of log data.

Root cause analysis requires a systematic approach. Start by defining the problem clearly, then gather all relevant log data. Analyze the data to identify any patterns or anomalies, and trace the sequence of events leading up to the problem. Once the root cause is identified, steps can be taken to resolve the issue and prevent it from recurring.

Semantic Log Analysis

Semantic log analysis involves interpreting the meaning of log data. It goes beyond simply identifying patterns or anomalies, and seeks to understand the context and significance of the data.

Semantic log analysis can be challenging due to the diverse and complex nature of log data. It often involves the use of natural language processing (NLP) techniques to interpret the data. This can be a complex process, but it can provide valuable insights that other methods may miss.

Performance Analysis

Performance analysis is all about understanding how well your system is performing. It involves examining log data to identify any issues or bottlenecks that may be affecting performance.

Performance analysis can help identify issues such as slow response times, high CPU usage, or memory leaks. It can also reveal trends over time, such as increasing load or decreasing performance. This information can be used to optimize your system and ensure it is running at peak efficiency.

Log Analysis Best Practices 

Here are a few ways to make log analysis more effective.

Implement Secure Storage with Proper Access Controls

Secure storage is a fundamental aspect of log analysis. It ensures that your log data is stored safely and securely, and is protected from unauthorized access.

Implementing proper access controls is crucial. This involves setting up user roles and permissions, and ensuring that only authorized personnel have access to the log data. It’s also important to encrypt your log data, both at rest and in transit, to protect it from potential threats.

Tagging and Classification

Tagging and classification can make the process of log analysis much easier and more efficient. By tagging your log data with relevant labels or categories, you can quickly and easily filter and search your data.

Classification involves grouping similar logs together, making it easier to identify patterns or anomalies. For example, you might classify logs based on the system they relate to, the type of event they record, or their severity level.

Design Clear and Concise Dashboards

Dashboards are a powerful tool for visualizing and analyzing log data. A well-designed dashboard can provide a clear and concise overview of your system’s performance and status.

Your dashboard should be easy to read and understand, with clear and meaningful visualizations. Use color coding to highlight important information or alerts, and provide tool tips or explanations for complex data.

Set Actionable Alert Thresholds

Alerts are a key component of log analysis. They notify you when certain conditions (e.g., correlation) or thresholds are met, allowing you to respond quickly to potential issues.

Setting actionable alert thresholds is crucial. These thresholds should be based on realistic and meaningful criteria, and should trigger an alert when there is a genuine need for action. Too many false positives can lead to alert fatigue, while too few alerts can mean critical issues are missed.

Conduct Regular Audits to Ensure Compliance

Regular audits are an essential part of log analysis. They ensure that your log analysis practices are up to date and compliant with industry standards and regulations.

Audits can help identify any gaps or weaknesses in your log analysis process, and provide recommendations for improvement. They can also provide assurance to stakeholders that your log analysis practices are robust and effective.

Security Log Analysis with Exabeam

Exabeam Security Log Management is a cloud-native solution, providing an entry point to ingest, parse, store, and search security data for your organization in one place, providing a lightning fast, modern search and dashboarding experience across multi-year data. Exabeam Security Log Management provides your organization affordable log management at scale without requiring advanced programming or query-building skills.

Exabeam SIEM extends the cloud-scale capabilities of Exabeam Security Log Management with features to help teams with threat detection, investigation, and response. Exabeam SIEM includes incident/case management, a centralized system of record for investigation and response, over 160s pre-built correlation rules, integrated threat intelligence for more improved detection, and powerful dashboarding capabilities. 

The solution delivers analysts improved speed with multi-year search capability for query responses across petabytes of data in seconds. Alert and Case Management improves analyst productivity with a central control space with a ticketing system specifically designed for security. If more storage, longer storage time, or additional processing power is needed, Exabeam SIEM easily scales to meet your needs.