Log Analysis Solution – Why should companies go for it?

Log Analysis Solution – Why should companies go for it?

As mentioned in one of our earlier blogs, Log Analysis provides a semi-structured data analytics solution. Organizations can use Log Analysis to reduce issue detection, diagnosis and troubleshooting time – thereby facilitating effective management of the infrastructure and applications. In a nutshell, Log Analysis provides analysis of logs to define trends.

What does Log Analysis bring to the table?

Organizations do Log Analysis for various reasons – the 3 main categories being:

  • Security and Compliance

  • Troubleshooting and Monitoring

  • Data Insights

Security and Compliance: Log Analysis helps Organizations enhance their security and be more compliant – by understanding and responding to data security incidents like Data Leaks and Data Breaches. It also helps in log forensics to – find vulnerabilities, discover hacks, recover data, and track malicious activities.

Troubleshooting and Monitoring: Log analysis then helps in detecting issues in real-time and fixes them before any irreparable damages occur. This can be viable through – Rules-based application monitoring and ML-based log analysis for application monitoring.

Data Insights: Log Analysis helps companies to gain insights – which help internal teams and processes to improve their decision making processes and re-evaluate strategies.

Together with methods like – log analytics and log forensics, log analysis grants establishments with the prospect of making the most out of their logging policies. Organizations should not treat logging just as a mere troubleshooting facilitator – but make it a part of their overall data security plans.

Performing Log Analysis

Largely, data logs are cleansed, structured or normalized and then offered for log analysis to detect patterns or anomalies such as a cyber-attack or data breaches. Performing log file analysis typically follows these steps:

  • Data Collection

  • Data Indexing

  • Data Analysis

  • Data Monitoring

  • Data Reports

What does Log Analysis Constitute?

Log Analysis typically includes the following categories:

  • Anomaly Detection: A major part of Log Analysis is focused on Anomaly Detection. Often, these techniques focus on identifying problems in software systems.

  • Security and Privacy: Logs can be leveraged for security purposes, such as intrusion and attacks detection.

  • Root Cause Analysis: Log Analysis includes investigation to find out what caused an anomaly or a data leak. This includes providing the users with root cause analysis, accurate failure identification, and impact analysis.

  • Failure Prediction: Failure prediction is feasible once there is knowledge about abnormal patterns and their related causes. This approach shifts monitoring to a proactive manner rather than reactive, i.e., once the problem occurred.

  • Quality Assurance: Log analysis might support developers during the software development life cycle and, more specifically, during activities related to quality assurance.

  • Model Inference and Invariant Mining: Model-based approaches to software engineering seek to support understanding and analysis by means of abstraction. Logs serve as a source for developers to build representative models and invariants of their systems. These models and invariants may help developers in different tasks, such as comprehensibility and testing.

  • Reliability and Dependability: Logs can serve as a means to estimate how reliable and dependable a software system is. Research in this subcategory often focuses on large software systems, such as web and mobile applications that are distributed in general, and high performance computers.

  • Log Platforms: Log Analysis platforms provide dashboards and the right metrics to measure the data. These monitoring systems reduce and filter the amount of log data and are efficient in querying which further supports the operations team on diagnosing glitches.

Thanks to developments in log analysis tools, most processes can be automated. And it is highly recommended that, organizations should practice full-stack logging through all the system components to get a holistic view of activities and anomalies.

Avalanchio is one such tool – where it’s Log Storage and Analysis tool which is built on top big data products, enables organization to collect data from unlimited endpoints in real-time. It also allows SOC analysts to write their own rules using simple to complex conditions, time filters, lookups, merging multiple datasets.

Get in touch with us today for a demo!