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Top Use Cases for Log Analysis

Investing in a log analysis tool provides many benefits: it saves time needed to detect and troubleshoot a problem, reduces churn by providing a better user experience, and improves system security. There is a wide scope of use cases for log analysis – from tackling security and performance issues head-on to enhancing the quality of your services. What are some of the most common use cases for log file analysis?

  • Compliance
  • Security
  • Troubleshooting
  • Performance improvement

Why Compliance is Important for Log Analysis

As systems and software solutions grow more complex, compliance with industry requirements such as ISO and other standards becomes a burning issue. You have probably noticed that different standards have emerged and evolved over the years, each with firmer rules (including the use of logging in your system).

What Types of Compliance is Log Analysis Used for?

  • Compliance with security policies
  • Regulatory compliance
  • Compliance with audits

These directives are a good thing, since they dictate that companies comply to unique standards that guarantee safety and functionality. This oversight is especially important in cases where you use third-party services. Since your system is only as strong as its weakest link, compliance provides a guarantee that all components adhere to the same requirements.

The downside of increased compliance demands is that it is nearly impossible to perform manual log file analysis for systems that accumulate large amounts of logged data. Thanks to manual analysis being replaced by log analysis tools, the process is becoming more sophisticated, faster, and more reliable. The use of artificial intelligence and machine learning detects patterns and behaviors that would have otherwise flown under the radar, and the future of log analysis lies in developing smarter self-protecting and self-healing systems.

How Log Analysis Improves Security

One of the most widely known reasons to use log analysis is to protect against security threats. Every company with an IT environment (which amounts to nearly every company in the world) needs to keep its systems secure, but some fail to grasp the importance of quality log file analysis software and rely on only basic protection such as firewalls and other network security software.

What Kind of Data Should You Log to Improve Security?

You can improve security by logging and analyzing the following:

  • Used IDs
  • Log event timestamps
  • Terminal identity
  • Access attempts (successful/failed)
  • Accessed files and networks
  • System configuration changes
  • System utilities use
  • Warnings and other security-related events
  • Activation of protective software

Data stored in logs contains an extraordinary amount of information: IP addresses, client/server requests, HTTP status codes, etc. With proper analysis, you can track down suspicious requests and system vulnerabilities and set up limitations and configuration parameters that protect you from similar future threats. Log analysis software can alert you via email or Slack whenever a suspicious activity (for example, a login attempt from outside your trusted network) occurs, so you can act quickly and eliminate the threat. One of the advantages of using log analysis as a security measure is that it keeps up with the perpetrators. Hackers are likely constantly probing your system for vulnerabilities, making it impossible to prevent all attacks. But thanks to the intelligence gathered from data logs, you can learn from each attack and improve your security with every iteration.

You can read more on how to perform network security monitoring here.

Log Analysis for System Troubleshooting

Log file analysis helps troubleshoot both software and hardware problems, from application crashes to configuration issues and hardware failure. This feature is especially important to your user base: repeated outages and impaired performance causes your churn rate to skyrocket.

How to Perform Troubleshooting in Six Steps

  1. Detect the problem – identify the issue you’re troubleshooting
  2. Find the likely cause – assume the root cause based on log data
  3. Check your theory – test your assumption to confirm or deny it
  4. Make an action plan – create necessary steps to resolve the problem
  5. Test for functionality – perform system testing to see if the problem has been resolved
  6. Report your findings – document all actions and outcomes from previous steps

The first step in troubleshooting is to find the root cause of the problem. Use data log information to your advantage and discover possible culprits by combining and comparing different types of logs; for instance, finding the correlation between parts of the application that were used and the type of error message logged around that time. By filtering log timestamps to the time when the problem occurred (give or take a couple of minutes), you can simplify the process of tracking down the cause. Once you think you have found the probable cause, you can plan on how to resolve the issue and prevent problem recurrence. By testing your solution against several test cases, you can use log analysis once again to check if the problem cause has been correctly identified, and repeat this process until the results are as predicted.

Troubleshooting with log file analysis is often used in production monitoring, since it can prevent production downtime or at least minimize it thanks to enabling a quicker response.

Performance Improvement Through Log Analysis

Improving system performance is one step beyond troubleshooting–using information gathered from logs to predict and prevent problems before they occur. One of the ways to utilize log analysis tools is to search for bottlenecks. While your system may work properly despite them, getting rid of bottlenecks improves speed and efficiency. When talking about performance, most engineers think in terms of memory and time resources, and optimization revolves around reducing losses on either of these two fronts.

Log analysis can reveal which parts of the system can be improved, for example by pinpointing potential overload and distributing workload evenly through load balancing. Using log analysis tools makes bugs in code more visible, which speeds up the debugging process, especially with bugs that aren’t a threat to system stability but cause a waste of resources.

How Log Analysis Can Improve Performance

  • Detect bottlenecks
  • Optimize processes
  • Perform load balancing
  • Discover hard-to-find bugs




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