Log Analysis Tools: Key Capabilities and 5 Tools You Should Know

What are Log Analysis tools? 

Log analysis tools are software applications that collect, parse, and analyze log data from a variety of sources, such as servers, network devices, and applications, and provide advanced features such as real-time monitoring, alerting, and visualization. This can be done manually, using tools such as text editors or spreadsheet software, or with specialized log analysis tools that automate the process and provide more advanced features.

Log analysis were traditionally used by system administrators for:

• Monitoring and debugging systems: Log data can help identify issues with systems, such as performance problems or errors, and can provide clues as to the cause of the issue.
• Analyzing and optimizing system performance: By analyzing log data, organizations can identify patterns and trends that can help them optimize their systems and improve efficiency.

More recently, organizations have used log analysis for security and compliance purposes. Logs can be used to track user activity and detect security incidents, such as attempted hacks or unauthorized access to sensitive data. However log analysis tools were not designed for these purposes and have many limitations when processing security-related logs. 

This is part of a series of articles about log management.

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Log analysis software: features and benefits 

There are a wide variety of log analysis tools available, ranging from simple command-line utilities to advanced, feature-rich applications. Some common features of log analysis tools include:

• Data collection: The ability to collect log data from multiple sources and store it in a central location, such as a log server or database.
• Data parsing: The ability to parse log data and extract relevant information, such as timestamps, log levels, and log messages.
• Visualization: The ability to visualize log data in a meaningful way, such as through graphs, charts, and tables.
• Alerting: The ability to set up alerts that trigger when certain conditions are met, such as when a system error occurs, when a log is deleted, or when another kind of meaningful event is detected.
• Real-time monitoring: The ability to monitor log data in near- or real-time and provide real-time alerts and notifications.

The key benefits of implementing a log analysis tool include:

• Improved efficiency: Log analysis tools can automate the process of collecting, parsing, and analyzing log data, making it more efficient and less time-consuming than doing it manually.
• Advanced features: Log analysis tools often provide advanced features such as real-time monitoring, alerting, and visualization, which can help organizations quickly identify and resolve issues, improve security, and optimize their systems.
• Centralized log management: Log analysis tools can collect log data from multiple sources and store it in a central location, making it easier to manage and analyze.
• Improved security and compliance: By analyzing log data, organizations can detect security incidents, such as attempted hacks or unauthorized access to sensitive data, and can use the information to improve their security posture. Log analysis tools can also help organizations meet regulatory compliance requirements by providing a central location for storing and analyzing log data.
• Better decision-making: By analyzing log data, organizations can gain valuable insights into their systems and can use this information to inform business decisions.

Learn more: Read our guide to log analytics

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5 Log Analysis Tools in Security

Log analysis tools play an important role in security by helping organizations to identify, investigate, and respond to potential cyber threats and security incidents.

Log analysis tools can analyze log data, looking for patterns and anomalies that may indicate a security event – including the erasure of logs. However, the frequency of attacks and the amount of data generated by modern IT systems places great strain on traditional log analysis tools, making it difficult to support security use cases.

This raises the need for specialized log analysis tools, designed for security purposes. These tools can be used both for real-time security log analysis, and for forensic analysis of log data after an incident has occurred. This can help organizations to understand the scope and impact of an attack, and to identify any weaknesses or vulnerabilities that may have been exploited.

Specialized security log analysis tools can also be integrated with other security tools and systems, such as intrusion detection systems (IDS), security information and event management (SIEM) systems, and firewalls. This enables security professionals to get a more comprehensive view of their security posture and respond more effectively to potential threats.

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5 Log Analysis Tools You Should Know

Graylog

Graylog is an open-source log analysis and management platform that allows organizations to collect, store, and analyze log data from a variety of sources. It is designed to be scalable, secure, and easy to use, and provides a range of features and capabilities for log analysis, visualization, and reporting.

Some of the key features and capabilities of Graylog include:

• Data collection: Graylog allows organizations to collect log data from a wide variety of sources, including servers, network devices, and applications. It can be configured to collect log data in real-time or on a scheduled basis, and can store the data in a central location, such as a log server or database.
• Data parsing: Graylog includes a powerful data parsing engine that can extract relevant information from log data, such as timestamps, log levels, and log messages. This information can be used to perform advanced searches and create custom reports and visualizations.
• Visualization: Graylog provides a range of visualization options, including graphs, charts, and tables, which can be used to view and analyze log data in a meaningful way.
• Alerting: Graylog allows organizations to set up alerts that trigger when certain conditions are met, such as when a system error occurs or when a security incident is detected. These alerts can be configured to send notifications via email, SMS, or other methods.
• Security: Graylog includes a range of security features, including role-based access control, encrypted data storage, and secure communication protocols, to ensure that log data is protected and that only authorized users have access to it.

Nagios

Nagios is an open-source monitoring and alerting tool that is used to monitor the availability and performance of IT infrastructure, including servers, network devices, and applications. It can be used to monitor systems in real-time and provide alerts when certain conditions are met, such as when a system goes down or when a performance threshold is exceeded.

Nagios can also be used for log analysis, as it includes a feature called Nagios Log Server, which allows organizations to collect, parse, and analyze log data from a variety of sources. Nagios Log Server provides advanced features such as real-time monitoring, alerting, and visualization, and can be used for a variety of purposes, such as monitoring and debugging systems, improving security and compliance, and optimizing system performance.

Some key features and capabilities of Nagios Log Server include:

• Data collection: The ability to collect log data from multiple sources and store it in a central location.
• Data parsing: The ability to parse log data and extract relevant information, such as timestamps, log levels, and log messages.
• Visualization: The ability to visualize log data in a meaningful way, such as through graphs, charts, and tables.
• Alerting: The ability to set up alerts that trigger when certain conditions are met, such as when a system error occurs or when a security incident is detected.
• Real-time monitoring: The ability to monitor log data in real-time and provide real-time alerts and notifications.

LOGalyze

Logalyze is a log analysis and management platform that helps organizations collect, analyze, and visualize log data from multiple sources. It is designed to provide real-time visibility into the activity, performance, and security of a system or application.

Logalyze is typically used by IT administrators and professionals to monitor the performance, security, and availability of a system or application. It can be particularly useful for large organizations with complex IT environments.

Some features of Logalyze include:

• Centralized log collection and storage: Logalyze can collect log data from multiple sources, such as servers, applications, and devices, and store it in a central location for analysis.
• Real-time analysis: Logalyze can analyze log data in real-time, alerting administrators to potential issues or anomalies as they occur.
• Customized dashboards and reports: Logalyze provides customizable dashboards and reports, allowing administrators to visualize log data in a way that is most relevant to their needs.
• Search and filtering: Logalyze includes powerful search and filtering capabilities, allowing administrators to quickly find and analyze specific log data.
• Integration with other tools: Logalyze can be integrated with other tools, such as monitoring and alerting systems, for a more comprehensive view of the system or application.

Fluentd

Fluentd is an open-source data collection and logging platform that can be used to collect, process, and forward log data from a variety of sources to a variety of destinations. It is designed to be scalable, flexible, and reliable, and can be used to collect and route log data from a wide range of sources, including applications, servers, network devices, and cloud-based services.

Fluentd can be used for log analysis in several ways: 

• Log data collection: Fluentd can be used to collect log data from various sources and forward it to a central location for analysis. This allows you to gather and centralize log data from multiple sources, making it easier to analyze and troubleshoot issues.
• Plugin system: Fluentd includes a plugin system that allows you to extend its functionality and integrate it with other tools and services. For example, you can use Fluentd plugins to parse and filter log data, perform statistical analysis, or visualize log data using data visualization tools. This allows you to use Fluentd as a platform for custom log analysis solutions.
• Integrations: Fluentd can be integrated with other log analysis tools, such as Splunk or Elasticsearch, to provide a complete log analysis solution. This allows you to use Fluentd to collect and forward log data to these tools, where it can be further analyzed and visualized.

Elastic Stack

The Elastic Stack is a collection of open-source tools for data management and analysis that was developed by Elastic. It consists of four main components:

• Elasticsearch: A distributed, scalable search and analytics engine that can be used to index, search, and analyze large volumes of data in real-time.
• Logstash: A data processing pipeline that can be used to collect, parse, and transform log data from various sources and send it to Elasticsearch for storage and analysis.
• Kibana: A data visualization and discovery tool that can be used to create interactive dashboards and charts to explore and analyze data stored in Elasticsearch.
• Beats: A collection of lightweight data shippers that can be used to send data from various sources (such as servers, applications, and network devices) to Elasticsearch or Logstash for further processing.

The Elastic Stack can be used for log analysis by collecting log data from various sources, processing and storing it in Elasticsearch, and using Kibana to visualize and analyze the data. 

For example, an IT administrator could use the Elastic Stack to collect log data from servers, applications, and network devices, and use Kibana to create dashboards and charts that show the performance and usage of the system over time. The administrator could also use Kibana to search and filter the log data to identify specific events or patterns, and use machine learning algorithms to identify anomalies in the data.

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Security log management with Exabeam

Managing cloud security can be a challenge, particularly as your data, resources and services grow. Misconfiguration and lack of visibility are frequently exploited in data and system breaches. Both issues are more likely to occur without centralized tools. 

Azure Log Analytics dashboards and services may be enough to provide basic visibility for specific development or DevOps teams. However, most organizations need more advanced security measures and have specific teams and groups that monitor security as a whole rather than specific tools or even IaaS/Paas like Azure. Logging onto multiple interfaces is not the most effective or efficient path to get a holistic view of events in your environment. 

Log Analytics solutions are therefore combined with SIEMs and user and entity behavior analysis (UEBA) tools. UEBA tools create baselines of “normal” activity and can identify and alert to activity that deviates from the baseline. 

Security Log Management via a SIEM or UEBA (or both in one, as in Exabeam Fusion) benefits cloud management by:

• Providing centralized monitoring – dispersed systems can be a challenge to monitor as you may have individual dashboards and portals for each service. Log Analytics can alert you to suspicious or policy-breaking behavior that you might otherwise miss in standalone dashboards.
• Creating visibility in multi and hybrid cloud systems – cloud-specific services may not be extendable to on-premises resources and vice versa. Log Analytics can help you ensure that policies and configurations are consistent across environments. For example, by monitoring data use and transfer in hybrid storage services.
• Helping you evaluate and prove compliance standards – Log Analytics can provide trackable, unified logging with evidence of actions taken. You can use Log Analytics logging and event tracking in compliance audits and certifications.
• Scaling to match your system needs – Log Analytics often use daemons or agents to monitor distributed systems. These agents allow you to scale your Log Analytics to match your environment size. You can take advantage of the scalability of any tools you use by accepting and incorporating data streams for tools across your system.
• Combining signals from Azure Log Analytics with other cloud security tools and logs such as cloud access security brokers (CASB), data loss prevention (DLP), Azure Active Directory Federation Services (AD FS) in a single platform like Exabeam can help build a full timeline of events, and gather in other associated alerts or actions that could indicate lateral movement from cloud to remote to on premise systems.