Security Information and Event Management (SIEM) systems play a central role in modern cybersecurity operations. They collect logs and events from across an organization’s infrastructure, analyze them for suspicious behavior, and support incident response and compliance. But while most teams understand what a SIEM does in practice—log aggregation, alerting, dashboarding—the architecture behind these capabilities often receives less attention.

The effectiveness of a SIEM system depends not just on the features it offers, but on how those features are built, deployed, and connected. Architecture impacts everything from data throughput and alert fidelity to investigation speed and operational cost. A poorly structured system may introduce silent failures: dropped events, delayed detection, or alert fatigue. A well-designed one supports scalable ingestion, efficient analysis, and real-time response.

In this article, we’ll examine the architectural foundations of a robust SIEM system. We’ll break down its core layers, compare common deployment models, and explore key principles for building systems that scale reliably with business and security demands. Finally, we’ll look at how graph-based analytics, through tools like PuppyGraph, can enhance SIEM functionality by exposing attack patterns that traditional rule engines may miss.

What is SIEM?

SIEM, which stands for Security Information and Event Management, is a cornerstone technology in modern cybersecurity. At its heart, SIEM provides a centralized platform for collecting, analyzing, and managing security-related data from a multitude of sources across an organization's IT infrastructure. Think of it as a digital watchdog that aggregates logs and events from servers, network devices, applications, and security tools like firewalls and intrusion detection systems.

The primary goal of a SIEM system is to provide security teams with comprehensive visibility into their environment, enabling them to detect, analyze, and respond to security threats more effectively. By correlating seemingly disparate events, SIEM can identify patterns and anomalies that might indicate a malicious activity, such as a brute-force attack, malware propagation, or unauthorized data access.

Beyond real-time monitoring and threat detection, SIEM solutions also play a crucial role in compliance and reporting. They can generate reports required by various regulatory frameworks by retaining historical security data and demonstrating adherence to security policies. This historical data is also invaluable for forensic investigations after a security incident.

To achieve these capabilities, a SIEM system relies on a sophisticated underlying architecture. This architecture typically involves components for data collection, normalization, analysis (including correlation and behavioral analytics), alerting, and reporting. The effectiveness of a SIEM in providing actionable security intelligence is directly dependent on how well these architectural components are designed and integrated to handle the volume, velocity, and variety of security data. Understanding this architecture is key to appreciating how SIEM transforms raw logs into meaningful security insights and forms a critical foundation for a robust security posture.

Core Components of a SIEM Architecture

A SIEM system is built on a set of interrelated components that together form the end-to-end pipeline for collecting, processing, analyzing, and acting on security data. Each layer has a distinct role, but they must work in coordination to ensure reliable performance and meaningful outcomes.

The process begins with the data collection layer, which is responsible for ingesting raw logs and telemetry from across the environment. This includes sources like servers, firewalls, endpoints, cloud platforms, identity systems, and network appliances. Collection may rely on agents installed on endpoints, built-in integrations with cloud services, or standard protocols such as Syslog and Windows Event Forwarding. The goal is to ensure a consistent and complete stream of events into the system.

Once collected, data moves into the processing layer, where it is parsed, normalized, and enriched. Log formats vary widely between systems, so normalization is essential for aligning fields into a consistent schema. This allows later stages to apply rules and queries without accounting for source-specific quirks. Enrichment may include adding metadata such as geolocation, asset ownership, or threat intelligence scores. Correlation logic also operates at this layer, connecting related events across systems or time windows to surface suspicious patterns.

The analysis layer focuses on extracting insights from the processed data. This may involve real-time alerting based on detection rules, statistical baselining to flag unusual behavior, or more advanced techniques like user and entity behavior analytics (UEBA). While some SIEMs include built-in models, many rely on well-tuned rules crafted around known risks in the environment. The effectiveness of this layer depends heavily on the quality of earlier stages and the ability to minimize false positives without missing high-impact events.

All collected and processed data must be stored, and that responsibility falls to the storage layer. This includes both short-term storage for immediate querying and long-term storage for compliance or forensic analysis. Hot storage is optimized for speed and used for recent data needed in investigations. Cold or archival storage holds older logs more cost-effectively, though typically with slower access. Compression, encryption, and indexing strategies all influence the performance and cost profile of the storage system.

Finally, the visualization and response layer surfaces information to analysts and integrates with response workflows. Dashboards provide overviews of alert trends, system health, and ongoing investigations. Analysts use search tools to drill into logs and reconstruct incident timelines. Some systems integrate with SOAR platforms to trigger automated responses or open tickets. The usability of this layer often determines how effectively the team can act on what the SIEM discovers.

Taken together, these components form the backbone of a functioning SIEM system. When each layer is carefully designed and tuned to the organization’s needs, the result is a platform that delivers both visibility and actionability—key ingredients for modern security operations.

Deployment Models and Architectural Patterns

How a SIEM system is deployed has a major impact on its scalability, performance, and compliance. Most architectures fall into one of three models:

On-Premises

The SIEM runs entirely within the organization’s infrastructure. It offers full control over data and configuration, often preferred for sensitive environments or strict regulatory requirements. However, scaling and maintenance are fully managed by the internal team, which can increase operational overhead.

Cloud-Native

Delivered as a managed service, a cloud-native SIEM handles ingestion, analysis, and storage without requiring local infrastructure. It scales easily and integrates well with cloud-based systems. Trade-offs include less control over backend configuration and potential concerns around data residency.

Hybrid

Combines local data collection with cloud-based processing and storage. This model balances control with scalability, often used when sensitive data must stay on-premises but teams still want cloud flexibility. It requires careful coordination between local and remote components.

Centralized vs. Federated Collection

SIEMs can use centralized log collection, where all data flows to a central point, or a federated approach, where data is pre-processed locally before partial forwarding. Centralized models simplify correlation; federated models reduce bandwidth and support regional compliance.

Choosing the Right Model

The best deployment model depends on data sensitivity, integration needs, available expertise, and expected scale. Whichever model is chosen, consistency in data format and correlation logic across environments is key to maintaining detection quality.

SIEM Architecture: Design Principles and Best Practices

A SIEM system is only as effective as the architecture that supports it. Good design ensures that detection logic runs reliably, storage stays manageable, and analysts receive timely and accurate alerts. The following principles and practices help guide both the initial design and ongoing operation of a SIEM system.

Align Design with Objectives

Every SIEM deployment should begin with a clear understanding of its goals. Whether the priority is real-time threat detection, compliance reporting, or forensic investigation, the architecture should reflect those needs. Define the use cases you want to support, and let them guide decisions around data collection, correlation logic, and storage retention.

Focus on High-Value Data

Not all logs are equally useful. Prioritize data from systems that reflect critical assets, user activity, and access control. These typically include identity providers, firewalls, endpoints, and cloud management layers. Filtering out low-signal logs at the collection stage reduces noise, storage costs, and alert fatigue.

Normalize Early, Correlate Intelligently

Consistent data is easier to analyze. Apply normalization as close to the source as possible to reduce downstream complexity. Correlation rules should be tuned to reflect the organization’s environment and risk profile, rather than relying only on vendor-provided defaults. Overly broad rules increase false positives and waste analyst time.

Design for Modularity and Scalability

Architect the system in modular layers—collection, processing, storage, analysis, and response—so that each layer can scale or evolve independently. Choose components that support horizontal scaling, especially for ingestion and analysis pipelines, to handle increasing data volume without re-architecture.

Optimize Storage Based on Usage

Recent data needed for investigation and alerting should reside in fast, indexed storage. Older data used for audits or threat hunting can move to slower, cost-efficient storage. A tiered storage strategy balances performance, retention, and cost.

Secure the SIEM Itself

Since the SIEM contains sensitive operational data, it must be treated as a high-value asset. Use strict access controls, monitor configuration changes, and keep infrastructure patched. The SIEM should also log its own activity for accountability and audit purposes.

Plan for Integration

A SIEM rarely operates alone. It should integrate smoothly with other security tools—such as threat intelligence platforms, SOAR systems, and ticketing tools. Open standards and API support help future-proof the system and reduce vendor lock-in.

Scalability, Performance, and Data Management

As log volumes grow and environments become more complex, a SIEM system must scale reliably across ingestion, storage, and analysis. Proper planning ensures that performance remains stable, alerts stay timely, and historical data is accessible when needed.

Estimating Event Velocity

The first step in designing for scalability is understanding how much data the system will process. Event velocity is usually measured in events per second (EPS), but averages alone are not enough. Real-world systems often produce short bursts of much higher volume during peak times.

To estimate velocity, review at least 90 days of historical log data. Identify typical EPS during normal operation and peak EPS during busy periods. Consider how many peaks occur per day and how long they last. This helps calculate daily event totals for both steady and peak periods. It’s also standard practice to include headroom—usually around 10 percent—for unexpected spikes, and another 10 percent to accommodate growth over time. This gives a more realistic sizing target for ingestion pipelines and backend systems.

Planning Storage Requirements

Once you have a velocity estimate, storage planning becomes critical. A typical log event occupies about 300 bytes. At 1,000 EPS, that equates to roughly 26 gigabytes per day. Over a 90-day retention window, this grows to over two terabytes—even before indexing, metadata, or replication overhead is considered.

Recent logs that support real-time detection and investigation should be stored in high-performance systems optimized for search and indexing. Older logs used for compliance or forensic purposes can be archived in slower, lower-cost storage. Many SIEMs support log compression, often achieving significant space savings depending on log format and structure. Encryption and backup also factor into the design. If logs contain sensitive data, encryption at rest and in transit may be required. Likewise, the SIEM’s storage infrastructure should include redundancy and failover to meet uptime expectations.

Extending with Data Lakes

To handle long-term retention and large-scale analysis, many modern SIEM architectures incorporate a data lake. The SIEM handles live correlation and alerting, while the data lake stores historical records for retrospective queries, threat hunting, and trend analysis.

A data lake doesn’t replace the SIEM—it complements it. Platforms like Hadoop, Amazon S3, or Azure Blob Storage offer nearly unlimited storage at low cost, making them ideal for long-term retention. Tools like Spark, Hive, or SQL-based engines enable analysts to explore this data efficiently, even across millions or billions of records. This division of labor allows the SIEM to stay responsive while the data lake provides depth and breadth.

This layered approach—fast storage for live detection, deep storage for historical insight—helps organizations scale their SIEM without sacrificing performance or visibility. As data sources multiply and retention expectations grow, this kind of architecture becomes increasingly essential.

Extending SIEM with Graph-Based Insights

Traditional SIEM systems are optimized for rule-based correlation across individual events or event sequences. This works well for detecting direct, well-defined behaviors—failed logins, port scans, malware signatures. But it often falls short when threats span multiple systems, identities, or time windows. Lateral movement, privilege escalation chains, and infrastructure misuse can all leave subtle traces across different parts of the environment, which may not be visible in isolation.

This is where graph analytics complements a SIEM. Instead of looking at events in isolation, a graph model focuses on how entities—users, IPs, devices, roles, sessions, resources—are connected. Graph queries can follow relationships across multiple hops to reconstruct activity paths, detect policy violations, or identify risky access chains that rule-based correlation would likely miss.

PuppyGraph brings graph querying to existing SIEM data without the need to migrate or transform it. With its zero-ETL architecture, you can define graph models directly over your relational or structured log sources. Data stays in place—PuppyGraph reads it through connectors, applies a dynamic graph schema, and enables real-time querying via Cypher or Gremlin.

This offers several advantages:

• Real-time, multi-hop threat investigation: Traverse from user → session → resource to reveal hidden lateral movement.
  
  
• Flexible graph modeling: Create multiple graph views over the same data, useful for different teams or threat scenarios.
  
  
• No data duplication: Avoid pipeline complexity and storage overhead.
  
  
• Built-in visualization: Analysts can explore alerts and relationships visually, without writing custom dashboards or exporting data.

For example, if your SIEM detects multiple login failures from different IPs, a graph query can reveal that all those sessions map to the same underlying cloud account, which recently accessed a sensitive resource. That kind of connection is easy to miss in a flat event stream but becomes obvious in a relationship graph.

Demo

We also provide a demo for you. Please download the materials from GitHub and follow the instructions in README.md. We use a public dataset of anonymized AWS CloudTrail logs from flaws.cloud, a security training environment created by Scott Piper. It contains 1.9 million events simulating realistic attack scenarios in AWS—ideal for modeling real-world threat investigations. 

After downloading the dataset, we will use Spark to convert the raw JSON data into relational data in Apache Iceberg format. Apache Iceberg is an open table format that enhances data lakes by providing ACID transactions, schema evolution, and time travel capabilities, allowing organizations to efficiently manage large-scale analytical datasets within their data lake architectures. Finally, we use PuppyGraph to connect to the Apache Iceberg data, run graph queries, and view the visualizations.

We omit the detailed instructions here. Here are some screenshots to give you an idea.

Conclusion

A well-architected SIEM system does more than collect logs. It serves as the foundation for detecting threats, supporting investigations, and enabling timely response. The structure of its components, including how data is collected, processed, stored, and analyzed, directly affects the system’s reliability, scalability, and overall effectiveness.

For teams aiming to improve detection accuracy and uncover complex attack paths, graph-based analysis can provide meaningful advantages. By modeling the relationships between users, resources, and events, graph queries reveal patterns that are difficult to detect using isolated event correlation alone.

To see how this approach can enhance your existing SIEM, you can try the forever free Developer Edition of PuppyGraph or schedule a demo with our team.