What Is SIEM? 4 Pillars of SIEM, Pros/Cons and Popular Solutions

How Does SIEM Work? 4 Pillars of SIEM Solutions

1. Log Management

Log management is crucial in SIEM architecture; it involves the collection, aggregation, and storage of log data from various sources within an organization’s IT ecosystem. This process ensures that the data remains accessible and organized for analysis. By managing logs, SIEM solutions help organizations maintain a comprehensive record of activity, which is critical for understanding the context of security incidents and for meeting regulatory compliance requirements.

Efficient log management entails the normalization of log formats, which makes disparate data comparable and analyzable. SIEM systems transform logs from different systems into a common format to streamline the correlation process.

2. Event Correlation and Analytics

Event correlation in SIEM involves linking related records from different log entries to identify potential security incidents. By applying correlation rules, analyzing patterns, and applying machine learning algorithms, SIEM tools automate the detection of activities that might indicate a compromise. For example, multiple failed login attempts followed by a successful login from an unusual location might be combined into a single security event. This helps in distinguishing real threats from false positives, optimizing the alerting process.

Event analytics further enhance SIEM capabilities by employing advanced statistical methods and machine learning techniques to analyze behavior patterns across the network. This proactive approach allows detecting anomalies that could elude traditional detection methods, providing an early warning system for potential security threats.

3. Incident Monitoring and Security Alerts

SIEM systems continuously monitor the network for unusual activities that could suggest security incidents. By leveraging integrated real-time data monitoring and complex event correlation, these systems instantly alert IT security managers about potential threats, enabling swift mitigation actions.

Post detection, SIEM tools also assist in the prioritization and investigation of security alerts. This reduces alert fatigue—a common challenge in security operations centers (SOCs)—and ensures that the most critical threats receive immediate attention. Timely intervention is crucial in managing and mitigating breaches effectively.

4. Compliance Management and Reporting

SIEM facilitates compliance management by automating the collection and reporting of log data required by regulatory standards like HIPAA, PCI-DSS, GDPR, and more. This automation significantly reduces the manual effort and potential human error, ensuring accurate and consistent compliance reporting.

Additionally, SIEM systems generate detailed reports that provide insights into security trends, incident response activities, and audit trails for forensic analysis. These reports are vital for audits and for making informed decisions about security policies and processes. They also offer proof of compliance to regulators and stakeholders, demonstrating an organization’s commitment to security best practices.

Learn more in our detailed guide to SIEM solutions.

SIEM vs. SOC: What Is the Difference?

Security Information and Event Management (SIEM) and Security Operations Centers (SOC) are both crucial elements in modern cybersecurity frameworks. SIEM provides the tools and software solutions for logging and analyzing security data, while SOC refers to the team and facilities dedicated to continuous monitoring and analysis of an organization’s security posture. Essentially, SIEM is a component that facilitates the operational processes of a SOC.

A SOC utilizes SIEM tools to collect, analyze, and respond to cybersecurity incidents. The efficiency of a SOC, therefore, heavily depends on the effectiveness of its SIEM solutions. However, SIEM software can be implemented without a dedicated SOC, and likewise, a SOC might utilize different tools aside from SIEM.

Key Features of SIEM Tools

Central Dashboard

The central dashboard is a central feature of SIEM tools, providing a unified view of all collected security data. This dashboard is crucial for system administrators to quickly assess and interpret a diverse set of information sources, from logs to real-time alerts. The ability to customize dashboards according to specific security or operational needs is one of the major strengths of SIEM systems.

Moreover, the real-time data visualization offered by these dashboards helps in identifying trends and potential threats swiftly. This makes it easier for security teams to act based on current insights, streamlining the decision-making process during a security incident.

User Monitoring

SIEM systems enhance security by monitoring user activities across the network. This monitoring helps in detecting suspicious behaviors, such as unauthorized access attempts or deviations from typical usage patterns, which might indicate a security threat or a breach. By maintaining comprehensive logs of user actions, SIEM aids in forensic analysis and helps determine the scope and impact of a security incident.

Additionally, user activity monitoring is crucial for enforcing corporate policies and regulatory requirements. By ensuring that user actions are consistently recorded and auditable, SIEM systems help organizations uphold security standards and protect sensitive data against internal threats.

Threat Intelligence

Threat intelligence is a key component of SIEM, providing context-rich information about emerging or existing threats. This information typically encompasses details about threat actors, the mechanisms they employ, and the vulnerabilities they exploit. Integrating threat intelligence feeds into SIEM systems enriches the data analysis, enabling more precise detection of sophisticated attacks and offering actionable insights for proactive defense strategies.

Furthermore, regular updates from diverse threat intelligence sources ensure that the SIEM system can adapt to a changing threat landscape, identifying new threat actors and attack patterns.

Advanced Analytics and Machine Learning

Advanced analytics and machine learning are at the forefront of enhancing SIEM solutions by enabling the identification of complex patterns and predictive analysis. Machine learning algorithms can analyze vast amounts of data at high speed, learning from historical trends to identify deviations indicative of potential threats. This capability not only increases the accuracy of threat detection but also reduces false positives.

Applying advanced analytics to SIEM processes involves parsing through and making sense of massive data sets to discern subtle correlations that might elude human analysts. This approach significantly aids in preemptive threat detection and proactive security measures.

Learn more in our detailed guide to siem cyber security.

Pros and Cons of SIEM Systems

Advantages of SIEM systems include:

• Real-time threat recognition: Continuous monitoring and instant analysis of data through SIEM systems enable organizations to catch unusual activity early, drastically reducing the potential impact of security incidents. 

• Detecting advanced and unknown threats: By incorporating advanced analytics and threat intelligence, SIEM enhances the detection capabilities for sophisticated threats, including zero-day exploits and ransomware. SIEM systems can recognize complex patterns and anomalies that indicate the presence of advanced threats.
• Streamlining forensic investigation: SIEM provides the detailed and actionable logs necessary for tracing the footsteps of cyber attackers and understanding the nature of the breach. The depth and breadth of logging, encompassing user activities, system changes, network traffic, and more, equip investigators with the tools to conduct rapid investigations.

Disadvantages of SIEM systems include:

• Complex integration: The complexity of integrating SIEM systems within existing IT infrastructure is a considerable challenge. Compatibility issues, data format discrepancies, and the sheer volume of data require customization and fine-tuning to ensure effective operation.

• Complex to operate: SIEM systems require a skill set that is not universally available in many IT departments, leading to potential reliance on external vendors and added costs for training and management. This dependence can complicate the maintenance and scalability of SIEM solutions, posing long-term operational challenges.

• Rules-based detection: Rules-based detection, the basis of most SIEM solutions, often leads to a high rate of false positives, which can overwhelm security personnel and lead to alert fatigue. However, modern SIEM solutions use threat intelligence and behavioral analysis to address this challenge.
• Lack of contextualized alert validation: SIEM systems sometimes struggle with providing sufficient context for the alerts they generate, leading to challenges in assessing the severity and authenticity of incidents. This can delay response times as teams scramble to understand the implications of each alert.

Learn more in our detailed guide to elastic siem.

Notable SIEM Solutions

1. Splunk Enterprise Security

Splunk Enterprise Security is a SIEM solution that provides deep visibility into security data by ingesting, normalizing, and analyzing information from various sources at scale. It enhances operational efficiency and improves threat detection by integrating assistive AI capabilities, ensuring that security teams can respond to incidents with accurate and context-rich information.

Features of Splunk Enterprise Security include:

• Advanced analytics: Enables rapid monitoring, detection, and investigation of threats across extensive networks.
• Curated detections: Offers over 1,500 ready-to-use detections that align with security frameworks such as MITRE ATT&CK and NIST CSF 2.0, helping to expedite threat identification and remediation.
• Extensive integration capabilities: Supports a network of over 2,200 partners and provides access to more than 2,800 partner and community-built applications via Splunkbase.
• Risk-Based Alerting (RBA): Attributes risk levels to users and systems, integrates alerts with cybersecurity frameworks, and activates alerts when risk thresholds are exceeded to effectively manage alert fatigue.
• Unified Threat Detection, Investigation, and Response (TDIR): Combines workflows across detection, investigation, and response stages with Mission Control, utilizing automated playbooks integrated with threat intelligence.

Source: Splunk

2. IBM QRadar

IBM QRadar Suite is a threat detection and response system with SIEM at its core. It optimizes the detection, investigation, and management of cybersecurity threats using artificial intelligence (AI) and automation. The solution integrates security functionalities such as security information and event management (SIEM), and security orchestration, automation, and response (SOAR).

Features of IBM QRadar Suite include:

• Threat investigation: Uses AI to automatically start investigations, fetching and mining data related to security cases, providing a detailed incident timeline incorporating MITRE ATT&CK tactics.
• Cloud-native flexibility: Delivered as a service on AWS, QRadar provides scalable security observability and log management, optimizing for high-speed analytics and massive data volume handling.
• Open integration ecosystem: Offers over 900 pre-built integrations, creating a flexible environment for security operations.

Source: IBM

3. Elastic Security for SIEM & Security Analytics

Elastic Security for SIEM & Security Analytics is an open SIEM solution that improves security operations by leveraging the big data capabilities of the Elastic Stack. It enhances threat detection, investigation, and response by providing deep visibility across hosts and utilizing generative AI to boost team productivity. It can handle up to petabytes of security data.

Features of Elastic Security for SIEM & Security Analytics include:

• Holistic view: Centralizes environmental activities and contexts, facilitating uniform analysis with the Elastic Common Schema (ECS) and easy integration with new data sources through one-click integrations, community plug-ins, and custom connectors.
• Interactive environment analysis: Allows for the interactive monitoring of historical data, offering built-in trend charts and the ability to correlate relevant data quickly to grasp unfolding attacks, all within a budget-friendly framework.
• Automated detection rules: Utilizes behavior-based rules developed by Elastic Security Labs to automatically detect suspicious activities and prioritize threats, aligned with the MITRE ATT&CK framework.
• Risk assessment with ML and analytics: Features prebuilt machine learning jobs for anomaly detection and provides security analytics to highlight entities at the highest risk, supporting evidence-based threat hunting.

Source: Elastic

4. OpenText ArcSight Enterprise Security Manager

OpenText ArcSight Enterprise Security Manager (ESM) is a scalable SIEM platform designed for real-time threat detection and response. It integrates a robust correlation engine and native Security Orchestration, Automation, and Response (SOAR), enabling security analysts to quickly detect, react, and manage cybersecurity threats. It can be used for large scale environments, with the ability to process over 100,000 events per second.

Features of OpenText ArcSight Enterprise Security Manager include:

• Enterprise-wide event visibility: Collects, normalizes, and enriches data from more than 450 event sources.
• Real-time threat detection: Capable of analyzing over 100,000 events per second, it alerts analysts to correlated threats as they happen.
• Native SOAR solution: Enhances the SOC with automated responses, out-of-the-box playbooks, and robust incident management capabilities.
• Automated threat intelligence: Automatically integrates open-source threat intelligence feeds, keeping the security team informed of the latest threats and enabling proactive defenses.
• Intelligent risk scoring and prioritization: Assesses multiple data points and prioritizes events based on their significance and risk to the network.

Source: OpenText

Best Practices for SIEM Implementation

Understand Implementation Scope

A clear understanding of the implementation scope is vital when deploying SIEM systems. It involves identifying the data sources, security tools, and organizational processes that will integrate with the SIEM. A thorough scope definition helps in designing a focused and effective implementation strategy that addresses specific security needs without overcomplicating the solutions.

Additionally, defining the scope aids in resource allocation, ensuring that the necessary tools and personnel are available to manage the SIEM system effectively. This preliminary step is crucial for setting realistic expectations and achieving measurable security improvements.

Identify Compliance Requirements

Identifying compliance requirements is an essential step in the SIEM implementation process. This involves understanding which regulatory standards apply to your organization and ensuring the SIEM solution can effectively support these requirements through adequate data processing, reporting, and archiving capabilities.

Fine-Tune Correlation Rules

Fine-tuning correlation rules is crucial for the effectiveness of SIEM systems. This process involves customizing the rules according to specific security needs and operational context to improve threat detection accuracy and reduce false positives. Regular reviews and updates of correlation rules in response to evolving security landscapes are necessary to maintain their relevance and effectiveness.

Regularly Tune SIEM Configurations

Regular tuning of SIEM configurations is necessary to adapt to the continuously changing security environment. This includes updating rules, improving alert settings, and refining the system’s response strategies to better align with current threats. Ongoing maintenance ensures that the SIEM system remains effective over time, providing consistent and reliable security monitoring.

Document and Practice Incident Response Plans

Thorough documentation and regular practice of incident response plans enhance an organization’s preparedness for dealing with potential security breaches. SIEM systems play a crucial role in this by providing the necessary logs and insights to inform and refine response strategies. Practicing these plans in simulation exercises helps identify gaps in the response workflow, allowing timely rectifications before real incidents occur.

Security Automation with Cynet

Cynet provides the world’s first Autonomous Breach Protection platform that natively integrates endpoint, network and user attack prevention/detection of XDR with log management and analysis of CLM and automated investigation and remediation capabilities of SOAR, backed by a 24/7 world-class MDR service. 

End to end, fully automated breach protection is now within reach of any organization, regardless of security team size and skill level. Most smaller businesses find that Centralized Log Management (CLM) is fully sufficient for their needs while far more affordable and usable than SIEM solutions. 

XDR Layer: End-to-End Prevention & Detection 

• Endpoint protection—multilayered protection against malware, ransomware, exploits and fileless attacks.
• Network protection—protecting against scanning attacks, MITM, lateral movement and data exfiltration. 
• User protection—preset behavior rules coupled with dynamic behavior profiling to detect malicious anomalies.  
• Deception—wide array of network, user, file decoys to lure advanced attackers into revealing their hidden presence. 

CLM Layer: Log Management and Analysis

• Ingest log data—collect all pertinent log data to uncover threats across your environment
• Data visualization—advanced, targeted data queries pinpoint precursors to cyberattacks and perform forensic analysis
• Threat hunting—uncover evidence of compromised endpoints, systems, and data such as anomalous privileged account activity and anomalous outbound traffic
• Regulatory compliance—generate reports for FISMA, GLBA, HIPAA, SOX, and PCI DSS, for example, that regulatory bodies require.

SOAR Layer: Response Automation 

• Investigation—automated root cause and impact analysis. 
• Findings—actionable conclusions on the attack’s origin and its affected entities.
• Remediation—elimination of malicious presence, activity and infrastructure across user, network and endpoint attacks. 
• Visualization—intuitive flow layout of the attack and the automated response flow. 

MDR Layer: Expert Monitoring and Oversight

• Alert monitoring—First line of defense against incoming alerts, prioritizing and notifying customers on critical events.
• Attack investigation—Detailed analysis reports on the attacks that targeted the customer. 
• Proactive threat hunting—Search for malicious artifacts and IoC within the customer’s environment. 
• Incident response guidance—Remote assistance in isolation and removal of malicious infrastructure, presence and activity.

Simple Deployment

Cynet can be deployed across thousands of endpoints in less than two hours. It can be immediately used to uncover advanced threats and then perform automatic or manual remediation, disrupt malicious activity and minimize damage caused by attacks.

Get a free trial of Cynet and experience the world’s only integrated XDR, CLM, SOAR and MDR solution.

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