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Log Analytics and SIEM for Enterprise Security Operations and Threat Hunting

Today’s enterprise networks are heterogeneous, have multiple entry points, integrate with cloud-based applications, offer data center delivered services, include applications that run at the edge of the network, and generate massive amounts of transactional data.

In effect, enterprise networks have become larger, more complex, and more difficult to secure and manage. And as a result, IT operations teams and security analysts seek better ways to deal with the massive influx of information so they can make intelligent choices when it comes to maintaining the cybersecurity posture of their networks.

Network security tools like firewalls, antivirus, and endpoint protection continue to play an important role, but each of these solutions on its own provides just a glimpse into the network’s overall security posture. SecOps teams may attempt to integrate multiple security tools to establish a more complete view, but integrating these tools successfully can prove to be a difficult or insurmountable challenge.

To effectively safeguard complex IT environments, security teams need solutions like Security Information and Event Management (SIEM) tools and Log Analytics tools that can provide comprehensive network observability, integrate huge volumes of data from multiple sources, and correlate network events in real- or near-real time to identify potential cyber threats and vulnerabilities.

This blog post explores how cybersecurity teams can secure complex enterprise IT environments by combining SIEM software solutions and Log Analytics for security operations and threat hunting.

You’ll discover how SIEM and Log Analytics for SecOps work together to satisfy use cases from real-time anomaly and threat detection to compliance management, threat hunting, and forensic analysis.

Download the Solutions Brief: Scalable Log Analytics for Security Operations and Threat Hunting

Log Management and SIEM


Popular Cybersecurity Tools: SIEM and Log Analytics for SecOps

Two of the most popular kinds of tools for cybersecurity analytics in use today fall into two categories: SIEM and Log Analytics. Each of these solutions can help us better understand exactly what is happening across the network and the potential impact of this activity on the company’s security posture.

Although SIEM and Log Analytics tools take different approaches to analysis, using them together can create better visibility into the cyber hygiene and security posture of complex networks.

SIEM and Log Analytics for SecOps overlap in several areas when it comes to achieving visibility. Understanding where these technologies differ and how they complement each other is key to maintaining the cyber hygiene of any enterprise.

Let’s get started!


What is a SIEM Tool?

Security Information and Event Management (SIEM) software tools collect and aggregate log data from network and security devices in real time, then analyze the data to detect correlations that could indicate a potential cybersecurity threat or system vulnerability.

The defining capabilities of a SIEM software solution are:

  • Logs, Metrics, and Event Data Collection - SIEM tools can monitor networks in real time by collecting and centralizing log and event data from network devices, security tools, and other applications.
  • Data Analysis and Event Correlation - SIEM tools analyze aggregated logs and event data, searching for events with common attributes that could indicate malicious activity on the network. SIEM tools can have thousands of correlation rules informed by the latest enterprise threat intelligence.
  • Notifications and Alerting - When a correlation is detected that indicates a possible security breach or incident, SIEM tools can generate security alerts and send notifications to SecOps teams.
  • Automated Security Incident Response - SIEM tools can be configured to automatically respond to security incidents by consolidating relevant data and initiating actions on third-party systems. This feature helps eliminate slow, manual processes and reduces mean-time-to-resolution (MTTR) for security events.
  • Visualization, Dashboards, and Reports - SIEM tools offer visualization and dashboarding capabilities that make it easier for SecOps teams to consume data. They also offer reporting capabilities that categorize security-related events such as failed logins, potential malware activity, and potential data exfiltration.

SIEM tools have many strengths that make them effective security solutions for enterprise SecOps teams. They are effective at delivering real-time network observability and threat detection, and most solutions can work with numerous data sources and include advanced automation tools. Some SIEM tools even use machine learning to strengthen their anomaly and outlier detection capabilities over time.

But despite their strong performance in threat detection, SIEM solutions are not a panacea when it comes to securing complex IT environments.


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Where are the Performance Gaps in SIEM Solutions?

SIEM tools are optimized for real-time network observability and alerting on security threats, but those optimizations come at a cost. Here’s what those trade-offs look like for SecOps teams deploying a SIEM solution:

  • Limited Data Sources - SIEM tools are often optimized for gathering logs from security appliances only, meaning other devices on the network may not be included in the analysis. Many SIEM vendors charge per data source, giving users a budgetary incentive to limit integrations and restrict the scope of their data collection and monitoring.
  • Limited Data Retention - Most SIEM tools offer limited retention periods for log data with high costs for long-term data storage. As a result, they’re good for active threat-hunting but inefficient at analyzing historical trend data to discover long-term or persistent threats.
  • Limits on Reporting - SIEM tools often have predefined reports that focus purely on security events, limiting their applicability for more in-depth forensic analysis.
  • Costly Integration Challenges - SIEM tools frequently require custom integration to work with cloud or on-premise security appliances. If an appliance is not natively supported, a custom-coded solution may be required to capture data and include it in security analytics.
  • False Positives - The complexity of SIEM tools, along with the predictable integration challenges, can lead to missed security events or generate false positives. Without the full context of a security event, SIEM tools can deliver misleading reports that are time-consuming to investigate.
  • Complexity - Effectively using a SIEM may require extensive training and hiring additional cybersecurity staff.

SIEM tools require considerable integration, customizations, and the right expertise to be effective - and they still won’t satisfy every single cybersecurity use case.

Thankfully, SecOps teams can supplement their SIEM tool with a complementary log analytics solution that covers those key performance gaps, resulting in a more effective enterprise security strategy.


Log Management for Enterprise Cybersecurity


What is Log Analytics for SecOps?

Log Analytics software solutions are used to collect, aggregate, analyze, and visualize computer-generated log data from sources throughout the IT environment.

The defining capabilities of log analytics solutions are:

  • Log Data Collection and Aggregation - Just like SIEM tools, log analytics solutions also collect, aggregate, and centralize log data for analysis. But while SIEM tools are often only integrated with security appliances, log analytics solutions gather log data from a broader spectrum of sources that includes operating systems, network infrastructure, applications, and endpoint devices.
  • Log Data Normalization - Various data sources tend to format their logs in different ways, so most log analytics solutions offer a means of normalizing log data such that a single unified index can be used for analysis.
  • Log Indexing, Storage, and Retention - Normalized log data must be indexed for rapid retrieval before it can be searched, queried, and analyzed. While log analytics solutions don’t offer the same level of real-time monitoring that SIEM tools do, they may offer more cost-effective long-term data storage.
  • Querying and Analytics - Log analytics tools allow security operations teams to run queries and perform security log analysis on indexed data to discover potential security threats and vulnerabilities.
  • Visualization and Dashboarding - Just like with SIEM tools, log analytics solutions offer visualization and dashboarding features that make it easier for SecOps teams to consume data or report on the results of log analytics operations.

Enterprise SecOps teams are increasingly adopting log analytics for security operations and threat hunting applications. A log analytics solution brings together security and event data from throughout the network, giving SecOps teams increased visibility of potential threats and vulnerabilities.

Log analytics platforms are especially useful for forensic analysis and understanding how data moves across the network. Cybersecurity professionals can use these platforms to delve into events that may have happened days, weeks, or even months ago.


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SIEM vs. Log Analytics for SecOps Use Cases

SIEM tools and Log Analytics solutions have some different use cases and are actually complementary to each other when it comes to the critical function of enterprise cybersecurity.

For each of the following use cases, we’ll review the benefits of each option and how teams can deploy both SIEM and Log Analytics for SecOps use cases.


Network Observability

In a cybersecurity context, network observability allows SecOps teams to assess the security posture of the enterprise network based on metrics, traces, and log data.

  • SIEM tools collect logs, metrics, and traces, then aggregate the data and correlate events in real time to deliver up-to-the-second observability into active threats and the network’s overall security posture.
  • Log analytics solutions collect, aggregate, and normalize log files before placing the data into a searchable index. This process delivers enhanced observability of retroactive log data that can be queried and analyzed by SecOps teams to recreate a security incident or hunt for long-term threats.


Anomaly and Threat Detection

Detecting cyber threats and identifying anomalous events on the network are among the most important capabilities for enterprise cybersecurity teams.

  • SIEM tools are optimized for active threat detection and may even be used to detect polymorphic code attack and zero-day vulnerabilities.
  • Log analytics solutions are optimized for retroactive threat detection. Their ability to retain data for longer periods makes them better suited for detecting long-term and persistent threats.


Compliance Management

Organizations who operate in highly regulated industries may be required to comply with data security and privacy regulations. These regulations create specific requirements for retaining and securely storing certain types of records and sensitive data.

  • SIEM tools can be customized to generate compliance reports that demonstrate adherence to regulatory compliance requirements and remediative steps to correct compliance failures.
  • Log analytics solutions provide forensic data that demonstrates a historical view of actual events related to compliance.


Threat Hunting

Threat hunting is the process of proactively searching for cyber threats within the network that may have avoided detection by traditional security tools.

  • SIEM tools alert SecOps teams about active security threats and may point to Indicators of Compromise (IoCs) - but they’re usually not the most cost-effective way to parse retroactive log data for latent threats.
  • Deploying Log analytics for security operations and threat hunting enables SecOps teams to proactively search through archival data, uncover early threat indicators, and successfully mitigate cyberattacks - even before a truly malicious payload is delivered.


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Forensic Analysis

Forensic analysis refers to the investigative process conducted by SecOps teams to uncover and document the course, culprits, causes, and consequences of a cyber security incident.

  • Most SIEM tools are cost-optimized for less than 30 days of data retention. SecOps teams who wish to retain data for longer periods of time to support forensic analysis are likely to face prohibitive costs.
  • Log analytics solutions like ChaosSearch allow SecOps teams to fully index and search log data in long-term storage, making them the ideal solution for forensic analysis applications. We use proprietary indexing technology to massively compress your log files, then transform your Amazon S3 into a fully activated data lake with full querying capabilities and cost-effective unlimited data retention.


Using SIEM and Log Analytics for SecOps

Enterprise SecOps teams can benefit from deploying both a SIEM tool and log analytics for security operations and threat hunting. These technologies play complementary roles when it comes to securing enterprise networks against cyber threats.

SIEM tools are optimized for monitoring the here and now - they deliver real-time observability and alerting on network events, giving SecOps teams the ability to rapidly detect and respond to IoCs and active threats.

Log analytics solutions are optimized for monitoring data from the past - they deliver a more cost-effective choice for exploring historical trends, hunting down persistent threat attacks, or conducting a forensic analysis.

Organizations who use SIEM tools to detect and respond to threats in the present, and log analytics to uncover trends from the past, will successfully safeguard their IT infrastructure into the future.

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Additional Resources

About the Author, Dave Armlin

Dave Armlin
Dave Armlin is the VP Customer Success of ChaosSearch. In this role, he works closely with new customers to ensure successful deployments, as well as with established customers to help streamline integrating new workloads into the ChaosSearch platform. Dave has extensive experience in big data and customer success from prior roles at Hubspot, Deep Information Sciences, Verizon, and more. Dave loves technology and balances his addiction to coffee with quality time with his wife, daughter, and son as they attack whatever sport is in season. He holds a Bachelor of Science in Computer Science from Northeastern University. More posts by Dave Armlin