SIEM-log - definition & overview

Security Information and Event Management (SIEM) and log management are two examples of software tools that allow IT organizations to monitor their security posture using log files, detect and respond to Indicators of Compromise (IoC) and conduct forensic data analysis and investigations into network events and possible attacks.

The key difference between SIEM vs log management systems is in their treatment and functions with respect to event logs or log files.

A log file is a file that contains records of events that occurred in an operating system, application, server, or from a variety of other sources. Log files are a valuable tool for security analysts, as they create a documented trail of all communications to and from each source. When a cyber-attack occurs, log files can be used to investigate and analyze where the attack came from and what effects it had on the IT infrastructure.

Log parsing is a powerful tool used by SIEM to extract data elements from raw log data. Log parsing in SIEM allows you to correlate data across systems and conduct analysis to understand each and every incident.Log source for SIEM includes log and event files leveraged by SIEM including logs from events that occur in an operating system, application, server, or other sources.

A Log Management System (LMS) is a software system that aggregates and stores log files from multiple network endpoints and systems in a single location. LMS applications allow IT organizations to centralize all of their log data from disparate systems into a single place where they can be viewed and correlated by an IT security analyst.

A SIEM software system incorporates the features of three types of security tools into a single application.

1. Security Event Management (SEM) tools are very similar to LMS. They include functionality for aggregating log files from multiple systems and hosts, but they are geared toward the needs of IT security analysts instead of system administrators.

2. Security Information Management (SIM) software tools are used to collect, monitor and analyze data from computer event logs. They typically include automated features and alerts triggered by predetermined conditions that might indicate that the network is compromised. SIM tools help security analysts automate the incident response process, reduce false positives and generate accurate reports on the organization's security posture.

3. Security Event Correlation (SEC) software is used to sift through massive quantities of event logs and discover correlations and connections between events that could indicate a security issue.

SIEM tools combine all of these functionalities into one application that acts as a layer of management above existing security controls. SIEM tools collect and aggregate log data from across the IT infrastructure into a centralized platform where it can be reviewed by security analysts. They also deliver SIM features, such as automation and alerts, and the correlative capabilities of SEC tools.

Today's SIEM tools are leveraging modern technologies such as machine learning and big data analysis to further streamline the process of investigating, detecting and responding to security threats.

SIEM monitoring differs from log management in the treatment of log files and focuses on monitoring event logs. With a focus on monitoring and analysis, SIEM monitoring leverages features such as automated alerts, reporting, and improving your incident response processes.

Log management systems are very similar to SIEM tools, except that while SIEM tools were purpose-built for cyber security applications, LMS tools are more geared towards the needs of someone in a systems analyst role who might be reviewing log files for a purpose besides maintaining security.

If your sole requirement is to aggregate log files from a variety of sources into one place, a log management system might be the simplest and most effective solution for you. If your job is to maintain the security of a complex and disparate IT infrastructure using the most cutting-edge security monitoring tools available, you should be looking at SIEM software.

We can describe the difference between SIEM vs log management tools in terms of the core features offered by each application. Log management tools are characterized by:

1. Log data collection - LMS aggregates event logs from all operating systems and applications within a given network.

2. Efficient retention of data - Large networks produce massive volumes of data. LMS tools incorporate features that support efficient retention of high data volumes for required lengths of time.

3. Log indexing and search function - Large networks produce millions of event logs. LMS systems have tools like filtering, sorting and searching that helps analysts find the information they need.

4. Reporting - The most sophisticated LMS tools can use data from event logs to automate reports on the IT organization's operational, compliance or security status or performance.

SIEM tools typically have all of the same features as LMS tools, along with:

1. Threat detection alerts - SIEM tools can identify suspicious event log activity, such as repeated failed login attempts, excessive CPU usage, and large data transfers and immediate alert IT security analysts when a possible IoC is detected.

2. Event correlation - SIEM tools can use machine learning or rules-based algorithms to draw connections between events in different systems.

3. Dashboarding - SIEM tools include dash-boarding features that enable real-time monitoring, dashboards can often be customized to feature the most important or relevant data, increasing the overall visibility of the network and enabling live monitoring in real-time by a human operator.

Sumo Logic offers cutting-edge security analytics functionality, helping organizations secure their hybrid cloud environments with incident response and threat detection capabilities and enhanced forensic investigations. Sumo Logic can replace your legacy SIEM tool, or complement it with features like machine learning and big data analysis.