Handling large quantities of data is a digital challenge that looms over an increasingly data-dependent world. Cybersecurity is no exception. Logs, the basic unit of data generated by computers to keep track of all system activity, continue to increase in number as more and more applications are developed and business operations expand.
Effective log management is key in this scenario and so is choosing the right tool to ensure centralization of the process. In this blog, we will briefly explore the following:
The most prominent solution for log management, SIEM, didn't materialize until the early 2000s. After syslog was invented in the 1980s, logs were used for log forensics and then for generating log monitoring reports before being used for security. Later, due to the large amount of data produced by intrusion detection systems, it became difficult to map this data to that about vulnerabilities in systems to eliminate false positives. The logic used here was: For systems that were invulnerable to an attack, the alert was a false positive.
This then led to uses cases like incident management, and then real-time correlation, ultimately leading to SIEM. The onset of compliance regulations changed the game, making it easy for companies to justify the amount of money spent on using SIEM software by attributing it to fulfilling compliance requirements.
The increase in the use of cloud services led to the development of cloud-based log management tools like Loggly. The late 2010s saw a lot of new "replacements" to SIEM tools with the advent of UEBA; security orchestration, automation and response (SOAR); and, more recently, extended detection and response in 2021. Most of these are no longer available as standalone software and have been integrated with SIEM solutions.
If you've used a log management tool before, you know that there are a few major functions that are commonly present in them. They include:
Log collection and aggregation: Collecting logs from different sources through agentless or agent-based methods.
Log parsing: Converting the aggregated logs into a common format that is easily searchable by data tools.
Normalization, categorization, and enrichment: Picking common attributes from parsed logs (normalization), linking them to system events, and adding additional meaning (categorization and enrichment).
Real-time correlation: Setting conditions or rules to trigger alerts when there is an event or a series of events that may lead to a security incident.
Log indexing and storage: Creating an index of common attributes of all log data and storing it for later retrieval or use.
Essentially, a log management tool collects log data, processes it, adds additional information (if any), indexes it, and stores it. While indexing, the tool stores logs as keys based on common attributes for quicker access and swift response during search queries. This means for every log that is being indexed, there is additional data being generated. This has led companies to consider index-free log management mechanisms, which follow a column-oriented database system as opposed to conventional keyword-based indexing.
Used effectively, log management can help create efficient security defenses. Here's how using a comprehensive SIEM solution like Log360 with extensive log management capabilities can help you prevent cyberattacks:
Choosing the most effective log management tool on the market means choosing one equipped with UEBA, SOAR, and enhanced cybersecurity features along with the commonly available aspects present in any SIEM software. Request a personalized demo of ManageEngine Log360 today to get started with a comprehensive SIEM tool that comes armed with all these requirements and more.
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