5 Key Components of an Effective SIEM Architecture

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You need to understand how your SIEM works thoroughly to get the most out of it. This includes how it collects and processes data from various sources like networking applications, security systems, and cloud-based systems.

The data collected is normalized and indexed to remove irrelevant information, which reduces the storage space needed for future analysis. A correlation engine combines these events to prioritize high-risk threats for immediate response.

Log Management

In any IT environment, a vast amount of data is generated. SIEM collects and parses logs from different layers, such as servers, firewalls, network routers, databases, cloud systems, and more. These events are then analyzed for traces left behind by attackers to identify and locate security breaches.

This step is critical because it needs a comprehensive view of their environment to identify threats and mitigate risks quickly. Log management also allows them to predict future problems, such as server overload, that may occur when too many requests are made.

Logs are collected from devices using a variety of protocols. For example, Windows-based logs are collected, while it gathers based records. This information is then normalized and pushed into the SIEM system for processing, analysis, and reporting. SIEM solutions can also utilize application programming interfaces (APIs) to collect data remotely from virtualized networks and cloud-based systems. This is an essential capability for enabling newer tools, like machine learning and AI, to detect anomalies in real time.


The core function of SIEM software is to interpret security data meaningfully and present it on informational dashboards for real-time visibility. For instance, a zero-day attack may progress if firewalls detect a large traffic volume or web servers report off-the-chart 404 responses. The correlation rules built into the system will identify this activity.

Correlation combines events collected from different sources and applies the appropriate context based on the device type, data elements, and other factors. This enables analysts to quickly identify and respond to suspicious activities and provide alerts of incoming threats.

Modern SIEM systems are based on data lake technology for fast, scalable storage and processing. Additionally, they can filter and summarize logs to reduce storage needs and processing times. They can also set up a tiered storage process where data used for live monitoring and analysis is stored on high-performance storage.

In addition, the best SIEM tools feature data enrichment, enabling them to store information like real identities and geolocation along with the raw event data. They can also record incident response plans and workflows to ensure teams can quickly handle perceived threats and security incidents.


Cyberattacks have recently been on the rise, breaching the walls of organizations and infiltrating their systems. To prevent this from happening, businesses need a security tool that detects threats as soon as they arise. This is where a SIEM comes in.

Its ability to correlate data enables it to detect patterns that would otherwise go undetected. It also allows analysts to visualize these patterns in custom dashboards for real-time awareness.

However, the massive volume of data logged in environments can be overwhelming. To reduce this, SIEM uses rules to sift through the noise and prioritize what should be investigated for security incidents.

This process, known as normalization, entails transforming event data into valuable security insights. This is done through a filtering procedure that eliminates irrelevant information and only retains pertinent data for future analysis. This is achieved by gathering event data from various sources, such as networking applications, security systems, and cloud systems. This data is then fed into the SIEM architecture. These events are then analyzed for the presence of suspicious activities that may be indicative of an attack.

Detection Alerts

Detection alerts are one of the essential SIEM tools for reducing incident response time. With attackers using more sophisticated techniques, enterprises must detect threats quickly and respond promptly to mitigate the impact and avoid data breaches.

To detect security incidents, the first step is to monitor the network and the associated devices for suspicious activity. This process requires real-time monitoring to identify any unusual events and provide alerts when they occur.

The next step involves the correlation of events and analyzing the collected logs. This is done based on rules which can be predefined for different attacks or created and fine-tuned by the analyst.

To reduce the number of false positives, the system must categorize and normalize logs. This is accomplished by filtering out unnecessary data and converting it into meaningful, actionable security insights for analysts. In addition, it must index and optimize the data to enable analysis. It should also utilize storage sizing methods and policies to control the amount of data stored. It must also be capable of storing historical data for forensics and compliance purposes while retaining a standardized log format.


A top SIEM solution will ingest and collect information from a wide range of sources, including IT devices, servers, firewalls, antivirus software, and more. It should also automatically map the network infrastructure and applications for context and enable threat prioritization to identify high-risk events that require immediate attention and offload low-risk ones to automated response processes.

The next step is to connect the dots between different data sources, accomplished by correlation. This is done using rules that are either provided by the vendor for common attack scenarios or created and fine-tuned by the IT team. It’s important to note that this correlation can also include detection, which can detect unusual behavior, such as large file transfers from unfamiliar IP addresses and unauthorized lateral movements within the IT environment.

Correlation rules are helpful but must be more to detect complex threats, as they can’t capture context or match a pre-existing signature. This is why next-gen SIEM solutions incorporate advanced detection and remediation capabilities such as security orchestration, automation, and response (SOAR). This functionality allows IT teams to quickly take action on detected incidents and reduce the mean time to resolution.

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