Designing and Implementing a SIEM: Part 1
This four part series explains how to design and operate a SIEM programme from strategy and build out through day to day delivery. It clarifies what a SIEM is and is not, outlines the implementation choices that determine outcomes (such as log source selection, parsing and normalisation, retention, and deployment model selection) and provides practical ways to assess whether the SIEM is delivering measurable value.
The series then shifts into practical application. It covers threat hunting and threat intelligence integration using a realistic organisational scenario, developing actionable detection content including Sigma and YARA rules, and concludes with a hands on network investigation workflow using packet captures and common network security tools to support incident response.
Detection engineering starts with log engineering
A SIEM, or Security Information and Event Management platform, centralises security relevant telemetry and supports the analysis of events generated by applications, endpoints, and network and security devices. In practice, it collects logs into a central repository, parses and normalises them into a consistent structure, and enables searching and correlation so defenders can detect, investigate, and respond more efficiently.
Centralisation also strengthens operational resilience. Logs stored centrally are easier to back up and archive to support audit and compliance requirements, and they are more difficult to tamper with than logs left distributed across individual source systems.
Visibility, correlation, and alerting
A SIEM improves visibility by consolidating logs from servers, firewalls, routers, applications, and endpoints into a single dataset that can be searched and analysed consistently. Dashboards give SOC analysts a consolidated view of current activity and key health indicators across the environment, reducing the time spent pivoting between separate consoles and log stores.
Correlation is where SIEMs typically differentiate from basic log management. By linking events across multiple sources, such as identity, endpoint, network, and application logs, correlation can reveal multi stage activity that may appear benign when each event is reviewed in isolation. This is particularly valuable for identifying attacker workflows that rely on small, distributed signals rather than a single obvious indicator.
Real time monitoring is typically delivered through continuous evaluation of incoming events against defined rules and analytic logic. When a defined condition is met, or when behaviour deviates materially from an expected baseline, the SIEM generates an alert for analyst triage and investigation. In practice, some detections run in near real time or on a scheduled cadence because the underlying query is computationally expensive or must scan high volumes of data.
Limitations you must design around
SIEMs are not "set and forget" platforms. High ingestion volumes and complex analytics can introduce latency, and even well run deployments can miss sophisticated attacks due to evasion, novel techniques, or gaps in available telemetry. Excessive false positives also drive alert fatigue, so detections require continual tuning, refinement, and periodic review as the environment and threat landscape change.
The value profile will vary by organisation. Smaller or less distributed environments commonly gain the most from dependable log aggregation, baseline alerting, and compliance reporting. In larger or hybrid estates, where data is spread across multiple identity systems, cloud services, and network segments, the emphasis shifts toward enrichment with asset and identity context, higher fidelity correlation, and behavioural analytics across the diverse sources. The practical goal in both cases is the same: align the SIEM configuration and detection approach to the organisation’s risk, telemetry coverage, and operational capacity.
Measuring SIEM effectiveness: metrics that matter
Operationally useful SIEM metrics include:
- False positive rate: the proportion of alerts later assessed as benign. A primary driver of analyst workload.
- Alert volume vs. rule quality: the relationship between alert throughput and detection fidelity, where poorly scoped rules create noise and well tuned rules reduce it.
- MTTD (Mean Time to Detect):: the elapsed time from event occurrence and ingestion to alert generation and analyst visibility.
- MTTR (Mean Time to Respond):: the elapsed time from alert creation to containment and remediation. Often reduced through automation and orchestration when applied appropriately.
These metrics can be used as a feedback loop to improve parsing quality, enrichment, and detection content.
Distinguishing factors of a production SIEM
Capabilities that directly improve SIEM outcomes in production environments include:
- Event correlation across diverse sources: linking activity across IAM platforms, including identity providers and access control systems, firewalls, IDS or IPS, servers, and endpoints to surface multi-stage behaviour that is difficult to detect from any single feed.
- Threat intelligence integration: enriching events with known bad indicators such as IP addresses, domains, and file hashes, and adding technique context to support prioritisation and faster triage.
- Network transparency: using flow data such as NetFlow and baseline protocol usage patterns to support anomaly detection, hunting, and validation of hypotheses during investigations.
- Compliance reporting: maintaining audit trails and integrity controls, defining retention workflows, and producing reliable reporting outputs that meet regulatory and internal governance needs.
- SOAR integration: orchestrating investigation and response workflows and automating repetitive tasks to reduce cycle time and improve consistency.
These features also highlight how a SIEM differs from related tools. Log management platforms typically prioritise collection, storage, and search, while IDS and IPS tools focus on network based detection at key choke points. Working separately, existing tools would lack the same depth of archival, reporting, and operational workflow support that a SIEM deployment provides.
Log sources, parsing, and normalisation
Comprehensive SIEM coverage typically spans several telemetry domains. At a minimum this includes network infrastructure and security controls such as routers, switches, next generation firewalls, and IDS/IPS; operating system logs from servers and endpoints; application logs from web services, databases, and line of business systems; endpoint security telemetry from antivirus and EDR; and identity sources such as directory services and authentication systems. In many environments this also extends to cloud control plane and service logs across providers such as AWS and Azure, and, where relevant, operational technology sources including IoT and SCADA.
Enrichment further improves investigation quality and triage speed by adding context that is not present in the original log. Common enrichments include geolocation, asset inventory and criticality, user and role attributes, vulnerability context, and threat intelligence matches. Applied consistently, enrichment reduces analyst pivot time and supports more reliable prioritisation, because alerts can be assessed in terms of who, what, where, and how important rather than raw event volume alone.
Market options and cost model
A SIEM’s costs are best understood by separating upfront investment from ongoing run costs.
- CapEx: licensing, integration and onboarding work, detection content development, SOC training, and the infrastructure required for compute, storage, and long term archival.
- OpEx: staffing and analyst coverage, cloud consumption or hosting fees, routine maintenance, upgrades, and continuous tuning to manage false positives and data quality.
Open source platforms such as Wazuh and OSSIM can reduce licensing spend, but they often shift the burden into configuration, engineering effort, and sustained upkeep. Commercial platforms typically offer several pricing approaches. The practical objective is to align the price levers, such as ingest volume, workload tier, or entity count, with how the environment being monitored actually behaves, and to stress those assumptions against expected growth rather than current day volumes alone.
Automation with SOAR and UEBA
SOAR (Security Orchestration, Automation, and Response) automates repeatable response actions and coordinates workflows across multiple tools. It typically ingests alert context from the SIEM and executes playbooks that handle common steps such as enrichment, ticket creation, notification, containment actions, and evidence collection. Used well, SOAR improves consistency and reduces manual workload. It does, however, require disciplined control design, including approval gates for disruptive actions, clear rollback procedures, and well defined automation boundaries to prevent unintended impact.
UEBA (User and Entity Behaviour Analytics) applies behavioural analytics, often supported by machine learning, to model normal patterns of activity and surface deviations from an established baseline. It can be effective for detecting compromised accounts, insider risk signals, and lateral movement patterns that do not map cleanly to static rules. Results depend heavily on telemetry quality, baseline stability and careful tuning, particularly in dynamic environments with frequent organisational or system change. UEBA can reduce noise in some deployments but it is not a guarantee and should be treated as an augmentation to analyst judgement rather than a replacement.
Where automated response is used, such as blocking indicators, disabling accounts, or isolating endpoints, actions should be policy driven and appropriately gated to avoid disruption from erroneous detections or incomplete context.
Implementation planning: the practical checklist
From an implementation perspective, the main considerations are:
- Infrastructure: choose on-premises, cloud, or hybrid deployment based on compute and storage requirements, expected ingestion volume, and the predictability of growth. Ensure network capacity and routing can support reliable log transport at scale, including peak-traffic times, and design for buffering when links degrade or collectors are unavailable.
- Staffing and training:: decide whether SIEM monitoring expands the SOC remit or requires additional headcount for coverage, content engineering, and platform administration. Plan for structured training, whether vendor led or third party, particularly for complex platforms and open source stacks where operational knowledge is not packaged by default.
- Integration:: confirm compatibility with existing firewalls, IDS/IPS, EDR, and threat intelligence feeds, and plan the integration effort realistically. Parsing and normalisation across vendor formats is often the critical path and should be treated as engineering work rather than a checkbox.
- Compliance outputs: ensure the SIEM can produce the audit trails, retention evidence, dashboards, and reporting outputs required by relevant regulatory and internal standards, and that the outputs can be reproduced consistently for audit periods.
- Scalability: start with the highest value systems and telemetry, but validate that the architecture can scale with increasing log volume, additional sources, and more complex detection content without degrading search performance or alert latency.
Storage and retention design
For hot data used in active analytics, performance typically improves with fast storage, efficient indexing, and minimal compression that would add processing overhead during ingestion and query execution. For longer term retention, cold data can be compressed and placed on slower media or secure cloud storage, provided the retrieval time and cost are understood and incorporated into investigative workflows and service level expectations.
Agent-based vs agentless collection
Agent-based collection deploys a lightweight forwarder on endpoints to gather logs and telemetry locally and transmit them to the SIEM. This approach can reduce bandwidth by filtering or aggregating data before it leaves the host. It often enables richer context, better delivery guarantees, and more consistent parsing across operating systems.
Agentless collection typically relies on remote retrieval using existing protocols and APIs, such as SNMP, WMI, syslog, REST endpoints, or vendor specific APIs. Many of these integrations operate on a pull model, where a collector polls endpoints or services on a schedule, although some sources still push events using standard mechanisms such as syslog from network devices.
Transport security is a further consideration. Some legacy protocols do not provide encryption by default, while many agent forwarders support TLS and mutual authentication to protect data in transit and reduce the risk of interception or tampering.
On-premesis, cloud, and hybrid
On-prem SIEM deployments provide direct control over compute/storage and can reduce dependency on third parties. Cloud SIEMs can reduce initial deployment burden and improve elasticity, but require close cost management and careful integration planning. Hybrid approaches often keep near-real-time processing close to source systems while using cloud storage for longer-term retention and historical analytics.
When using a cloud SIEM, treat log ingestion as processing of personal data whenever events contain identifiers such as usernames, IP addresses, device IDs, email addresses, or other attributes that can relate to an individual. Design for data minimisation from the outset: collect what you need for detection and investigations, reduce unnecessary fields where possible, and apply redaction or pseudonymisation for high risk data elements. Pair this with strong access controls, audit logging, encryption in transit and at rest, and retention policies that are aligned to investigative needs and regulatory obligations rather than defaulting to indefinite storage.
Integrating SIEM with firewalls, IDS/IPS, and EDR
Integrating a SIEM with firewalls and IDS or IPS provides visibility into network events and security detections, while integration with EDR adds endpoint state, runtime telemetry, and host based alerts. Correlating these sources improves investigation quality by connecting network activity to identity and endpoint context. For example, a firewall deny or IDS signature hit becomes more actionable when it can be linked to authentication failures, a suspicious process tree, or a host level detection on the same asset.
The main challenges are practical rather than conceptual. Multi vendor integrations increase parsing effort, field normalisation is required for consistent correlation and reporting, and event volumes can scale quickly. Without careful content design and prioritisation, high volume sources can generate noise that overwhelms analysts and obscures the signals the SIEM is intended to surface.