What Is Log Analysis?

What Are Logs?

Logs are files that house the continuous recording of events that occur within a specific environment, whether it’s strictly internal or communications with external environments and systems. Depending on the type of data administrators decide on logging, log files contain anything from failed and successful user access attempts to data transfers as well as changes in configurations and permissions.

Log files are divided into multiple categories depending on the type and location of the data they collect, such as:

• Event logs
• Network logs
• Application logs
• Endpoint logs
• System logs

Logging, however, is the continuous process of recording data points into logs about relevant events for archiving and analysis purposes afterwards.

What Is Log Analysis?

Log analysis is the act of analyzing data found in log files, converting it into insights and actionable information. Real-time log analysis is most commonly used to identify system bugs, security threats, and performance issues before they escalate.

Alternatively, log analysis can also include older files. This helps log analysts find patterns on a long-term scale and compare environment performance at different points in time.

Log Analysis at a later date is also incredibly beneficial and crucial for cyber forensics. It enables cybersecurity specialists to trace back attempted and successful attacks and network breaches, and determine their origin and how they accessed the environment.

How Does it Work?

There’s no one right or wrong way to perform log analysis. Every environment or network has different structures and needs that log analysis tools need to accommodate.

Generally, log analysis works in 3 steps:

Collecting log data

Log analysis begins with monitoring the activity within an environment and logging it within a file containing the time, date, and source of the data. Some log analysis systems are designed to log all activities while others are triggered by a specific type of event.

Cleaning the data

Data aggregated from a large network, application, or environment isn’t ideal for analysis. The log analysis software has to parse and clean the data; unify its format and remove fluff that might be considered irrelevant to the purposes of the analysis.

Analysis and visualization of data

The final step is to group together data points that fit and correlate together into visualized and easy-to-interpret graphs. Built-in data visualization tools and capabilities within log analysis software should be able to provide clean and comprehensible, real-time results that reflect the state of the logged environment.

The data analysis process is divided into multiple steps and categories, each looking into a different type of data and producing varying results: Intelligent pattern recognition: Upon looking over the logs, the data analysis tool should be able to detect patterns and trends, as well as anomalies in the log files

Correlation and causation: The smarter the analysis tool, the better it can differentiate between correlation and causation between data points Predictive analysis: With enough data-based training, analysis tools could be able to detect the early signs of cyber threats, system failures, or performance issues

Diagnostic analysis: Log analysis can be used to determine the cause of an error or an underperforming system.

The Importance of Log Analysis

The importance of log analysis tools ranges from beneficial to critical. For organizations in certain industries, such as financial services, log analysis is essential for regulatory compliance.

In terms of benefits, log analysis can help businesses and organizations gain a deeper understanding of their current environment. This will boost efficiency, agility, and resilience.

Benefits associated with log analysis include:

Business intelligence

Log analysis can be used in decision-making when it’s time to upgrade certain systems or switch out third-party providers and partners. Instead of basing decisions off of estimations and guesses, log data analysis can provide concrete evidence to executives and investors of what needs to be done for specific results.

Real-time Troubleshooting

Real-time troubleshooting is one of the primary objectives of log analysis. With proper and frequent data collection and analysis, administrators would be able to diagnose performance issues and understand why they happened in the first place. This would enable them to work towards an effective solution.

Better Environment Management

In addition to business and financial decisions, log analysis can help with making the right decisions when it comes to managing the monitored environment. Whether it’s adding or reducing servers or planning an expansion, log analysis can pinpoint the exact areas that need improvement and follow the results of any change.

Threat Detection

Constant log analysis enables organizations to respond promptly to intruders and threats as soon as they enter the environment. Combined with root cause analysis, it can help prevent future exploits of the same nature.

The Dangers of Not Utilizing Log Analysis Properly

The dangers and risks of not using a log analysis tool properly aren’t that different from not utilizing one in the first place. By not monitoring and analyzing your network or environment, you are blind to performance issues, errors, and optimization opportunities.

Non-compliance with regulations

Not using log analysis properly or at all could cost you mandatory and complementary certifications and harm your reputation in the long run. Falling to handle cyber threats

Without constant monitoring and analysis of activity, malicious and suspicious behaviour could remain unnoticed in your environment.

Misusing resources

By not closely tracking the performance of the various parts of your environment, you could end up with an abundance of resources in one area while another is underperforming.

Missing out on growth opportunities

Without the insights produced by a properly implemented log analysis tool, your company could end up missing out on numerous growth and improvement opportunities backed by data. This is a small risk when executed correctly.

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