Every device on an enterprise network generates log data. Log data provides exhaustive information about what is happening. It records events such as start-ups and shutdowns, commands executed, login and logoff information, established connections, and a lot more. This long trail of activities is the perfect reason to call log data the footprints of a network.
Log data, regardless of where it is generated, is usually categorised into system, error, warning, and critical events. Each of these categories provides a wide range of information. For instance, system events help network operations centers be more efficient by reducing the troubleshooting cycle, whereas warning, critical, and error log types help organisations audit and secure their network from attacks. With so much at stake, it is important for enterprises to collect, manage, and analyse log data from devices across their network.
Unfortunately, log management is not always that easy. Businesses face two significant challenges if they want to understand the nature of device activity on their networks through log analysis. The first challenge in managing log data is coping with the continuous deluge of log data from connected devices. With so many logs generated each day, the second challenge is pulling meaningful information out of collected logs.
To add to this complexity, compliance mandates establish strict requirements for collecting and analysing log data. Since log data contains information on every event that occurs in a network, there are often additional requirements for archiving log data for a specific period of time. Compliance mandates also outline the provisions for conducting forensic analysis of archived log data, which is used to assess the impact of a security breach or to contain an ongoing attack.
“This long trail of activities is the perfect reason to call log data the footprints of a network”
“With so many logs generated each day the challenge is pulling meaningful information out of collected logs”
Stringent compliance and security requirements compel enterprises to look for solutions that automate the log management process. A comprehensive log management solution should automate log collection, analysis, search, and archival processes, thereby reducing the need for manual intervention. An immediate benefit of automation is reduced lead-time and faster remediation.
The perfect log management solution provides better visibility and visualisation into network events in the form of intuitive reports and dashboards. Reports should provide exhaustive information on who did what activity, when, and where. The solution’s capability to discover anomalies instantly, and send real-time alerts to notify the user, greatly improves the operational efficiency of an enterprise’s network and security operation centers. A key component of any log management solution is the capability to implement artificial intelligence, which simplifies log management by downplaying false alarms or false positives and only flagging critical incidents.
Log management solutions should be able to connect the dots and predict security attack patterns. The ideal solution should also offer a high-speed search engine that can help in backtracking security attacks and conducting forensic analysis of archived log data. In addition, many organisations now want their log management solutions to extend their monitoring capabilities from on-premises into the cloud. Besides monitoring physical and virtual environments, enterprises now expect log management solutions to monitor cloud deployments.
An ideal log management solution should possess capabilities to process any human readable log format, intelligence to detect threats, and identify security incidents at ease by conducting efficient forensic analysis.
For IT administrators smooth generation and collection of log data is critical explains Subhalakshmi Ganapathy at ManageEngine.
