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You can build a useful SIEM learning project without writing an index, log collector, or dashboard from the ground up. Build and understand the security pipeline instead: collect a small set of events, normalize and enrich them, store and search them, create a detection, and investigate its alert. That teaches the core engineering work while keeping the project small enough to finish.
What “from scratch” should mean for a learning SIEM
For this project, “from scratch” means designing the data path and making its parts work together—not reimplementing every low-level infrastructure component. You can use existing open components or an SIEM platform, then focus on the choices that make monitoring useful: which events to collect, what fields to preserve, what a rule should detect, and how someone would investigate it.
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A dashboard alone is not a SIEM. A practical pipeline connects event collection and processing to central search, detection, alert review, and ongoing operation. Wazuh’s documented architecture illustrates these distinct roles with agents, a manager, an indexer, and a dashboard (Wazuh, “Architecture” and “Components,” accessed October 7, 2026).
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Start with a narrow question your data can answer, such as: “Did an account’s privileges change unexpectedly?” or “Was remote access attempted from an unusual source?” Treat these as example project goals, not ready-made detections. A useful first exercise is to write down:
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- The systems and event sources needed to answer the question.
- Who owns each source and how it can provide events.
- The timestamp, user, host, source, and event details needed to connect records.
- What an analyst should check if the detection fires.
- What benign activity could trigger the same condition.
This scope prevents a common project failure: collecting a large volume of unrelated logs before knowing what the system is meant to find.
How to build the SIEM pipeline
1. Collect events from sources that fit the use case
For endpoints, agents can collect and forward security data. Wazuh documents agent-based collection as well as agentless options for devices such as firewalls, switches, routers, and access points, including Syslog, SSH, and API paths. Choose sources based on the question you picked rather than trying to ingest everything at once.
For instrumented applications, OpenTelemetry provides APIs and SDKs, instrumentation libraries, exporters, resource detectors, and a Collector. Resource attributes can help identify the service or host that produced telemetry. OpenTelemetry describes general observability telemetry; it does not, by itself, supply a complete security SIEM, security-specific event coverage, detection rules, or an investigation workflow (OpenTelemetry, “Components,” last modified February 6, 2025).
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2. Parse, normalize, and enrich the records
Sources often describe similar activity in different formats. Convert records into consistent fields so searches and rules can use them across sources. Make the event time, producer or host, user when available, event type, and relevant source details explicit. Preserve the original record where feasible so you can inspect parsing problems and retain context during an investigation.
For example, a source-specific account-change message might become a normalized record with fields for @timestamp, host.name, user.name, event.action, and event.original. This is an illustrative field mapping, not a prescribed schema or a claim about a particular product’s output. Add context such as asset ownership or threat intelligence only when you have a reliable source for it. Wazuh describes its manager as decoding and enriching agent data, standardizing it, and forwarding processed output to the indexer and other destinations.
3. Store and search centrally
Index processed events in a central store that supports the searches your project needs. Test whether you can find an event by time, host, user, and event type, and whether related activity can be retrieved for investigation. Wazuh identifies its indexer as the central store for alerts and related security data, with near-real-time search and analytics. Elastic describes Elastic Security as a SIEM for centralizing, analyzing, and managing security data from multiple sources.
There is no universal retention period or sizing formula established by those product pages. Decide retention based on event volume, investigative needs, operating constraints, and applicable legal obligations. Measure your own data flow before making capacity claims.
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Begin with a small number of explainable rules. Each rule should name the event fields it depends on, the condition that triggers it, and the context an analyst needs to assess it. Test with representative benign and malicious scenarios before treating an alert as operationally reliable; a rule that searches the wrong field or misses a source’s format will not become effective just because it appears on a dashboard.
In the account-change example, the path is: a source emits an account-change event; collection forwards it; processing extracts the actor, target account, action, and time; the normalized record is indexed; a rule checks for the chosen condition; and a matching record creates an alert for review. The rule’s usefulness depends on the actual event coverage and context in your environment.
Wazuh documents manager-side decoders, rules, and threat-intelligence enrichment. Elastic documents prebuilt and custom detection rules, generated alerts, and investigation features such as Timeline and Cases. Those capabilities can shorten the build, but neither the availability of rules nor a platform’s features prove that a particular rule will work for your data.
5. Build dashboards around operating questions
Use dashboards to answer practical questions: Are expected sources still reporting? Which detections fired? What changed over time? Can an analyst pivot from an alert to related host and user activity? Wazuh’s dashboard documentation describes indexed-data queries and visualization, along with configuration, health, notifications, and alerting integrations.
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A chart is not a substitute for an investigation path. For each alert, make it possible to inspect the underlying event and find relevant surrounding records. Keep the alert’s reason and the next investigative action clear to the person reviewing it.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Compare a component prototype with an SIEM platform
A component-by-component prototype gives you more opportunity to study how collection, processing, storage, rules, and dashboards fit together. An existing platform supplies more integrated capabilities, but you still have to choose sources, validate mappings, and tune detections. Wazuh documents a multi-component platform; Elastic Security documents hosted Elastic Cloud and self-managed deployment options.
| Decision area | Component-by-component prototype | Existing SIEM platform |
|---|---|---|
| Learning versus operating effort | More of the integration work is yours, which can expose pipeline internals. | More capabilities are supplied by the platform; configuration and validation remain necessary. |
| Collection coverage | Depends on the collectors, integrations, APIs, and formats you assemble for your sources. | Depends on available agents, integrations, APIs, and formats for your sources. |
| Normalization and portability | You choose the event fields and how tightly rules depend on your chosen components. | Check how integrations map events and how portable queries and rules are. |
| Detection and investigation | You must provide the rule logic and a workable way to review alerts. | Features may include rule content and investigation workflows; validate them with your events. |
| Deployment and scale | Topology, availability, and maintenance depend on the components and your operating capacity. | Compare the available hosted or self-managed options and the maintenance each requires. |
| Cost and retention | Estimate using your own event rates, storage policy, and component costs. | Check current terms and estimate using your own event rates and retention needs; no directly comparable price is established here. |
Keep the first deployment small, then scale for a reason
For a lab, a single-server, all-in-one setup can make the event path easier to understand. Wazuh describes that topology as suitable for labs and small environments. Its deployment guidance distinguishes this from separate components for medium environments and clustered manager and indexer nodes for larger throughput or fault-tolerance and high-availability needs. These are topology categories, not hardware-sizing promises.
Before expanding, identify the constraint you are addressing: event throughput, availability, fault tolerance, or a need to separate component roles. Hardware requirements, event capacity, and retention depend on the actual environment; the cited documentation does not establish universal CPU, memory, storage, or network specifications.
Design security and operations into the project
Even a learning system handles sensitive records. Treat access policy, protected credentials, storage security, and retention as design decisions rather than dashboard features. Decide who can read raw events and alerts, how long records should remain available, and how you will know if a source stops reporting. Do not infer regulatory compliance from a product’s compliance-oriented views; compliance depends on the applicable requirements and how the whole system is operated.
Keep an operational note for each source and detection: what it covers, what fields it requires, what normal behavior looks like, and what an alert reviewer should do. As the project grows, measure event flow and revisit the deployment topology rather than assuming a lab configuration will meet production availability or staffing needs.
When the learning project is complete
You have a meaningful prototype when you can trace a representative event from its source through parsing and storage to a detection and an investigation view, explain the fields and assumptions behind the rule, and identify what happens when a source or component is unavailable. That is a solid foundation for evaluating a fuller SIEM deployment; it is not, on its own, evidence of production-grade monitoring.
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