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You can build a useful SIEM learning project by connecting a few log sources to a pipeline that collects, normalizes, stores, searches, and detects activity. “From scratch” need not mean writing your own collector, database, query language, and dashboard: the goal is to understand how those parts work together and make deliberate choices about the security questions they answer.
What should a from-scratch SIEM project include?
A SIEM is a workflow for turning security-relevant events into searchable evidence and alerts—not just a dashboard. A practical learning build has six parts: event collection, parsing and normalization, enrichment, central storage and search, detection rules, and an investigation view. It also needs basic operational checks so you can tell whether sources have stopped reporting or detections are producing noise.
Keep the first version narrow. Pick one question, such as whether an unexpected account change occurred or whether remote access came from an unusual source. Then identify the event sources and fields needed to answer it. This is more useful than collecting every available log before you know what you want to detect.
Map the event path
A typical path is source → collector → parser/normalizer → enrichment → index or store → search and detection → alert and investigation. The parts may run together or be split across machines. Wazuh’s documented architecture illustrates this separation with agents and agentless collection, a manager, an indexer, and a dashboard. Its manager processes agent data, standardizes and enriches it, then forwards it to the indexer; the dashboard queries indexed data.
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Define what success looks like
- A chosen event reaches the central store with its timestamp, source, and relevant host or user identity intact.
- A search can find that event and relate it to other activity.
- A rule can identify the intended condition and produce an alert with enough context to investigate.
- You can see whether expected sources are still reporting and determine what to do when an alert fires.
How do you scope the first use case?
Write down the security question before choosing tools. For example: “Did an account gain administrative access outside the expected change process?” The exact records needed depend on the systems in your lab; do not assume that one source alone can establish the full story.
- Events: list the account, identity, endpoint, or server records that could show the change and its context.
- Ownership: identify which system produces each record and who can resolve missing or malformed data.
- Fields: note the timestamp, account or user identifier, host, event type, and any source-specific identifiers needed to connect records.
- Response: decide what an analyst should check if the condition fires, and what evidence would make the alert benign or concerning.
Start with a small number of sources that support this question—perhaps identity, endpoint, server, cloud, or network events as appropriate to your environment. Collection coverage is estate-specific: endpoint agents may be suitable for endpoints, while network equipment may need a different route.
How do you collect logs and telemetry?
Use a collection method that fits each source
For endpoints, Wazuh documents agents that collect and forward security data to its manager. For devices where an agent is impractical, its documentation describes agentless monitoring; network equipment may send Syslog or expose data through SSH or an API. These are examples of collection paths, not a requirement to use one tool for every source.
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For instrumented application telemetry, OpenTelemetry provides APIs and SDKs, instrumentation libraries, exporters, resource detectors, and a Collector. Resource attributes such as service, host, or operating-system identity can help associate telemetry with its producer. OpenTelemetry is an observability framework: its components do not by themselves provide a complete security SIEM, security-specific event coverage, detection content, or an investigation workflow.
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Prioritize useful records over volume
Ingest records that serve the chosen use case, and confirm that each source is actually arriving. More data is not automatically better: irrelevant volume complicates searches and can consume storage without improving the detection. Keep track of source identity and timestamps from the beginning so you can spot gaps and interpret events later.
How should events be normalized and enriched?
Systems often describe similar activity in different formats. Normalization maps those source-specific records into consistent fields so searches and rules can work across sources. Make the event time, producer or host, user identity when available, event type, and source explicit. Preserve the original record where feasible; it can help explain a parsing error or support an investigation that needs details omitted from normalized fields.
Enrichment adds context, such as information that helps identify an asset or interpret an event. Wazuh describes its manager as decoding and enriching incoming data, including with threat-intelligence information, before forwarding processed output. Enrichment should be traceable: distinguish values present in the original event from values added by processing so analysts can judge their reliability.
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Follow one illustrative event
Suppose a source reports that an account was granted elevated access. The collector forwards the source record. A parser identifies the event time, account, affected host or service, and change type. A normalizer places those values into fields that searches can use consistently. Enrichment may attach asset or threat context if your pipeline has an appropriate source for it. The record is indexed, a rule checks whether the change matches your selected condition, and a matching event creates an alert that an analyst can pivot from to related account and host activity.
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This is an illustrative data path, not a prescribed schema or a claim that any particular rule will detect unauthorized changes. The field names, context, and rule logic must match the logs your systems actually produce.
Where should you store events, and how long should you keep them?
Central indexing and search let you query records from multiple sources in one place. Wazuh describes its indexer as a central store for alerts and related security data, with near-real-time search and analytics. Elastic describes its Security SIEM as a platform for centralizing, analyzing, and managing security data from multiple sources. Either way, choose storage and retention around your event flow, investigation needs, operational requirements, and applicable legal obligations.
There is no universal retention period or sizing formula established by these product descriptions. Estimate from your own observed event rates and test the searches and investigations you expect to perform. A short lab retention period may be adequate for learning the pipeline, but it should not be mistaken for a production policy.
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Begin with a few explainable rules tied to the use case and the fields you have confirmed are present. For each rule, document the condition, required data, expected benign matches, and what an analyst should do with an alert. Then test it against representative benign and malicious scenarios before relying on it operationally.
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Wazuh documents manager-side decoders and rules, as well as threat-intelligence enrichment. Elastic documents prebuilt and custom detection rules that search event data and generate alerts when conditions match, along with investigation features such as Timeline and Cases. These are platform capabilities; they do not establish that a particular rule set is accurate for your systems. Tuning and validation remain part of the project.
Make the alert investigable
An alert should help answer what happened, where and when it happened, which account or asset was involved, and which supporting records to inspect next. If a rule fires without enough context to investigate, improve collection or enrichment before adding more rules. Track false positives and missed cases during testing rather than treating a successful match as proof of detection quality.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What should the dashboard show?
Build views around operational questions, not visual decoration. Useful panels show whether expected sources are still reporting, which detections fired, how activity changes over time, and whether an analyst can pivot from an alert to related host and user events. Wazuh’s dashboard is documented as providing indexed-data queries and visualizations, along with configuration, health, notifications, and alerting integrations.
A dashboard view labeled for a compliance framework does not, by itself, make a deployment compliant. Compliance depends on the applicable requirements and the way the system is configured, operated, and governed.
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Should you assemble components or configure a SIEM platform?
For a learning project, the choice is mainly between spending more effort on the component boundaries or using a platform that supplies more integrated capabilities. Neither route is universally cheaper, easier, or more effective; compare them against your sources, learning goals, and ability to operate what you build.
| Decision area | Component-by-component prototype | Existing SIEM platform |
|---|---|---|
| Learning and effort | More opportunity to understand each pipeline layer; more pieces to connect and maintain. | More capabilities may be ready to configure; less of the underlying pipeline may be yours to implement. |
| Collection coverage | Depends on the collectors and formats you select for your actual systems. | Depends on available agents, integrations, APIs, and supported formats for your systems. |
| Normalization and portability | You can shape a common event model, but must build and maintain the mappings. | Assess how fields and rules work across integrations and how tightly they depend on that platform. |
| Detection and investigation | You must provide and validate rules, alert context, and an investigation path. | Capabilities may include prebuilt or custom detections and investigation tools; validate behavior in your environment. |
| Deployment and scale | You choose topology and take responsibility for availability and maintenance. | Deployment may be hosted or self-managed, depending on the platform; check topology and operational requirements. |
| Cost and retention | Measure using your event rates, infrastructure, storage policy, and maintenance effort. | Check applicable license terms and measure using your event rates and retention policy; no comparable price is established here. |
Wazuh documents multiple components and deployment patterns. Elastic documents hosted Elastic Cloud and self-managed deployment options for Elastic Security. These examples illustrate different ways to approach the project, not a universal product ranking.
How should you scale beyond a lab?
First make the pipeline dependable at its current size: verify source health, check parsing failures, watch storage use, and review detection quality. Then scale based on measured event flow and availability needs rather than assumed hardware requirements. Wazuh’s deployment guidance describes an all-in-one server for labs and small environments, separate components for medium environments, and clustered manager and indexer nodes for larger throughput or high availability.
The cited architecture guidance does not establish minimum CPU, memory, storage, or network specifications for a particular event rate. A dedicated server or mini PC can be a plausible lab choice, but select hardware only after setting a workload and validating it. Production readiness also requires decisions about access controls, retention, backups or recovery, maintenance ownership, and who will investigate alerts; a working dashboard alone does not provide those operational controls.
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