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What is Log Analysis? Use Cases, Best Practices, and More

What is Log Analysis?

Computers, networks, and other IT systems generate records called audit trail records or logs that document system activities. Log analysis is the evaluation of these records and is used by organizations to help mitigate a variety of risks and meet compliance regulations.

How Does Log Analysis Work?

Logs are usually created by network devices, applications, operating systems, and programmable or smart devices. They comprise of several messages that are chronologically arranged and stored on a disk, in files, or in an application like a log collector.

Analysts need to ensure that the logs consist of a complete range of messages and are interpreted according to context. Log elements should be normalized, using the same terms or terminology, to avoid confusion and provide cohesiveness. For example, one system might use “warning” while another uses “critical.” Making sure terms and data formats are in sync will help ease analysis and reduce error. Normalization also ensures that statistics and reports from different sources are meaningful and accurate.

Once the log data is collected, cleaned, and structured, they can be properly analyzed to detect patterns and anomalies, like network intrusions.

Use Cases for Log Analysis

Log analysis serves several different purposes:

  • To comply with internal security policies and outside regulations and audits
  • To understand and respond to data breaches and other security incidents
  • To troubleshoot systems, computers, or networks
  • To understand the behaviors of your users
  • To conduct forensics in the event of an investigation

Some organizations are required to conduct log analysis if they want to be certified as fully compliant to regulations. However, log analysis also helps companies save time when trying to diagnose problems, resolve issues, or manage their infrastructure or applications.

Log Analysis Software

Logs can be generated for just about anything: CDN traffic, database queries, server uptimes, errors, et cetera. Log analysis tools help you extract data from logs and find trends and patterns to guide your business decisions, investigations, and general security. These tools help you make data driven decisions, and are especially useful to system administrators, network administrators, DevOps, security professionals, web developers, and reliability engineers.

Best Practices for Log Analysis

Log analysis is a complex process that should include the following technologies and processes:

  1. Pattern detection and recognition: to filter messages based on a pattern book. Understanding patterns in your data can help you detect anomalies.
  2. Normalization: to convert different log elements such as dates to the same format.
  3. Tagging and classification: to tag log elements with keywords and categorize them into a number of classes so you can filter and adjust the way you display your data.
  4. Correlation analysis: to collate logs from different sources and systems and sort meaningful messages that pertain to a particular event. Correlation analysis helps discover connections between data not visible in a single log, especially since there are usually multiple records of a security incident. For instance, if you have just experienced a cyber attack, correlation analysis would put together the logs generated by your servers, firewalls, network devices, & other sources, and find the messages that are relevant to that particular attack. This process is often associated with alerting, as the data you gather from correlation analysis can help you craft alerts when certain patterns in the logs arise.
  5. Artificial ignorance: a machine learning process to identify and “ignore” log entries that are not useful and detect anomalies. Artificial ignorance will ignore routine log messages such as regular system updates but allow for new or unusual messages to be detected and flagged for investigation. Artificial ignorance can also alert you about routine events that should have happened but did not.

In addition to these technologies and processes, log data should be centralized and structured in meaningful ways so that they can be understood by humans and interpreted by machine learning systems. By aggregating all log data from various sources, you can correlate logs to more easily pinpoint related trends and patterns. Practice end-to-end logging across all system components, including infrastructure, applications, and end user clients, to get a complete overview.

Log analysis is an important function for monitoring and alerting, security policy compliance, auditing and regulatory compliance, security incident response and even forensic investigations. By analyzing log data, enterprises can more readily identify potential threats and other issues, find the root cause, and initiate a rapid response to mitigate risks.