Log Management Explainers:
Log Management: Process, Tools, and Tips for Success
What Is Log Management?
A log is an automatically generated, timestamped record of events related to a particular system. Almost all software applications and systems create log files. Log management includes processes and technologies that help organizations create, transmit, analyze, store, archive, and eventually dispose of large volumes of log data from IT systems.
Effective log management is critical to both security and compliance. Monitoring, recording, and analyzing system events is a key component of threat detection and response (TDR) efforts. Regulations such as HIPAA, the Gramm-Leach-Bliley Act (GLBA), and the Sarbanes Oxley Act (SOX) have specific requirements for audit logs.
Why Is a Log Management System Important?
Configuring an application to output log data is not enough. Even if the data is generated, log information will be lost if it is not properly collected and stored. Log data needs to be sent somewhere—ideally, a central location where it can be properly analyzed and retrieved by other services as needed.
Centralized data collection is only one step in the log management process. Log management involves handling all parts of the log life cycle, from emitting log data to eventual archiving or deletion.
Many modern applications include microservices or and cloud-based services, where each part of the system emits its own log data. In these distributed systems, collecting and analyzing log data can be more complex.
Successful log management allows organizations to:
- Reduce context switching – avoid the need to check for logs in multiple locations or tools to resolve an issue.
- Fix problems faster – quickly capture, analyze and visualize log data in context, to quickly identify and eliminate issues before they impact users.
- Instantly search logs – advanced search capabilities that allow staff to drill down into logs and quickly find the data they need.
- Visualize all data in one place – with centralized log data, it is easy to integrate the data with visualization tools and build custom visualizations and dashboards.
The Log Management Process
The log management process typically involves the following steps:
- Collection: Log data is collected from various sources, such as servers, applications, and all manner of devices. This can be done using specialized log management and forwarding tools, application- or architecture-audit services (like Log4J in Apache) or exported from the system’s operating system.
- Centralized storage: Log data is stored in a centralized location, such as a log server or a cloud-based service or repository, for easy access and analysis.
- Analysis: Log data is analyzed using specialized tools and techniques, such as log analysis software or business intelligence software, to identify patterns, trends, and anomalies. This may involve filtering and parsing the log data, searching for specific keywords or phrases, and generating graphs and charts to visualize the data. For example, event correlation is the process of analyzing log data to identify relationships and patterns between different events. For example, if an application error occurs at the same time as a network outage, event correlation can help identify whether the two events are related and whether one may have caused the other.
- Reporting: Results of the analysis are used to generate reports and alerts that provide insight into the system’s health and performance. These reports can be customized to meet the specific needs of an organization.
- Action: Based on the analysis and reports, appropriate action can be taken to address any issues or problems that are identified. This may involve correcting configuration errors, upgrading software or hardware, or taking other corrective measures.
Log Management vs. Log Analytics vs. Log Monitoring
Log management, log analytics, and log monitoring are related — however they represent distinct practices.
Log management is the combined process of collecting, storing, analyzing, and monitoring log data from various sources within an organization’s infrastructure. From simple IT collection to database, inventory, or other supply chain demands, log management brings them all into one place and provides the platform or data lake from which to pivot into interesting information.
Log analytics is the process of using specialized tools and techniques to analyze log data in order to identify patterns, trends, and anomalies. This may involve filtering and parsing the log data, searching for specific keywords or phrases, and generating graphs and charts to visualize the data. Log analytics is often used to identify problems and optimize the performance of a system.
Log monitoring is the practice of continuously monitoring log data in real-time in order to identify and respond to problems as they occur. This may involve setting up alerts or notifications that are triggered when certain events or conditions are detected in the log data, including but not limited to availability, connection, and throughput. Log monitoring helps organizations detect and respond to issues more quickly, improving the availability and performance of their systems.
Considerations When Choosing a Log Management Tool
Consider the following when evaluating log management solutions:
- Data collection: The log management solution should be able to collect log data from a variety of sources, such as applications, servers, operational technology, and network devices. It should also be able to handle different types of log data, including structured data and unstructured data.
- Searchability: The log management solution should have a powerful search capability that allows you to quickly and easily find the log data you need. It should also provide tools for filtering and aggregating log data to help you narrow down your search results to pinpoint exact informational needs.
- Scalability: Consider the size and growth of your log data and choose a solution that can handle the volume of log data that you expect to generate over time. The solution should also be able to scale up or down as needed to meet changing demands.
- Security: Make sure the log management solution meets your security requirements and protects your log data from unauthorized access. This may include features such as encryption both in motion and at rest, access controls, and role-based data masking as appropriate for your business needs.
- Advanced analytics: Look for a log management solution that provides advanced analytics capabilities, such as machine learning and artificial intelligence, to help you make sense of your log data and extract insights. These tools can help you identify trends, patterns, and anomalies in your log data and provide actionable recommendations for improving your systems and applications.
6 Log Management Best Practices for Security
Plan Out Security Use Cases in Advance
To build a secure system, it’s important to first understand the use cases the system needs to address. Consider compliance requirements and your primary external and internal threats. Plan how to manage the data needed to address those threats—and consider which alerts, dashboards, and metrics will be able to provide it.
The MITRE ATT&CK framework can help identify threat vectors relevant to your organization and understand which indicators of compromise (IoC) are relevant for each threat vector. This can help plan for log data needed.
Store Data for the Appropriate Amount of Time
Decide what data you need and how long you need to track active dashboards and alerts on that data. System breaches can occur months after a system has been compromised, so determine how long to collect and store data to aid investigations. Also, determine the required retention period for regulatory compliance purposes. Some regulations require that data be stored for at least one year, while others, like HIPAA, require that data be stored for six years or more.
Some log management platforms optimize storage through compression, making it possible to store significantly more data than traditional log management systems. Consider a solution’s compression ratio and the expected cost of storing log data for the required retention period.
Centralize Logs for Improved Access and Security
Centralized log management not only improves data access, but also greatly enhances an organization’s security capabilities. By storing and connecting data in a centralized location, organizations can detect and respond to anomalies faster. A centralized log management system can help reduce the time needed to detect and react to breaches.
Include Context in Log Messages
When you need to search through a large volume of log messages, a unique identifier such as an IP address or user ID is essential to filter out unwanted data. Structured logging makes it easier to include context, by adding custom fields with important information.
Context is more than just a set of identifiers. Having context also means having data that distinguishes an event from other events. This can be anything from the source that generated the log message, to the specific code that resulted in a failure, or detailed error messages. These details are useful when reviewing logs and can assist in troubleshooting issues.
Apply Access Controls
Logs contain data that is highly valuable for attackers, and log data is often subject to the same regulatory requirements for security as the files themselves. Therefore, as your team grows and multiple members need access to specific subsets of log files, it is critical to set up strong access control for these files. Deleting logs is a sensitive operation and should only be allowed for trusted members of the team.
It is rarely necessary for all team members to have the same access to all files, and so log management tools let you assign user access to individual log files. Use the principle of least privilege to ensure everyone has access only to the logs they need to do their job. To make this easier, you can group your log files by the type of system that created them, the geographic origin, or the organizational unit they belong to.
Leverage the Cloud for Added Scalability and Flexibility
As data volumes grow, organizations should consider investing in a modern cloud-based solution for their log management system. Cloud-native solutions offer easier scalability, allowing organizations to increase or decrease processing power and storage capacity according to their changing needs.
Learn more: 7 Critical Log Management Best Practices
Security Log Management with Exabeam
Built to support security use cases, Exabeam Security Log Management is a cloud-native solution, providing an entry point to ingest, parse, store, and search security data for your organization in one place, providing a lightning fast, modern search and dashboarding experience across multi-year data. Exabeam Security Log Management provides your organization affordable log management at scale without requiring advanced programming or query-building skills.
Exabeam SIEM extends the cloud-scale capabilities of Exabeam Security Log Management with features to help teams with threat detection, investigation, and response. Exabeam SIEM includes Case Management, a centralized system of record for investigation and response, 100s of pre-built correlations, integrated threat intelligence for more improved detection, and powerful dashboarding capabilities.
The solution delivers analysts new speed with multi-year search capability for query responses across petabytes of data in seconds. Alert and Case Management improves analyst productivity with a guided incident checklist, and a ticketing system specifically designed for security. If more storage, longer storage time, or additional processing power is needed, Exabeam SIEM easily scales to meet your needs.
Learn more: Exabeam for Security Log Management