# Elastic SIEM
Elastic SIEM is a security analytics platform built on the Elastic Stack, combining Elasticsearch, Kibana, Beats, and Logstash into a unified environment for threat detection, investigation, and response. It exists because security operations teams need a scalable, cost-effective way to ingest enormous volumes of security data and extract actionable intelligence from it without paying per-event licensing fees that punish organizations for collecting more data. The core problem it solves is the fragmentation of security telemetry: logs arrive from dozens of different sources in dozens of different formats, making correlation nearly impossible without a normalization layer. Elastic SIEM addresses this through the Elastic Common Schema, giving analysts a consistent data model that makes cross-source queries practical rather than aspirational. It sits at the center of modern detection engineering workflows and serves as both a data repository and an active analysis environment.
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Elastic SIEM is the security-specific feature set within Elastic Security, which is itself a product suite built on top of the Elastic Stack. The platform provides SIEM capabilities including detection rule management, alert triage, investigation workflows, and case management, alongside endpoint protection through the Elastic Agent and Elastic Defend integration. It is available as a self-managed deployment, as a managed service on Elastic Cloud, and increasingly as part of cloud-native architectures on AWS, GCP, and Azure.
Elastic SIEM is not a standalone product. It is a configuration and capability layer that presupposes an operational Elastic Stack. Organizations that attempt to treat it as a plug-and-play appliance often encounter friction because the platform requires deliberate data onboarding, schema mapping, and rule tuning before it produces high-fidelity alerts. This is an important distinction from commercial SIEMs that arrive with pre-configured connectors and dashboards designed to reduce time-to-value at the cost of architectural flexibility.
Elastic SIEM is also not a replacement for a dedicated data lake or long-term retention platform in every context. While Elasticsearch can store petabytes of data, cost and performance considerations often lead organizations to implement tiered storage strategies, keeping hot data in Elasticsearch for active investigation while archiving cold data elsewhere.
Variants of Elastic SIEM deployments include: on-premises clusters for organizations with strict data residency requirements; Elastic Cloud deployments managed by Elastic's infrastructure team; and hybrid architectures where ingest nodes are distributed across geographic locations while a central cluster handles search and analytics. The Elastic Security for SIEM solution should be distinguished from Elastic Observability, which addresses performance monitoring and application reliability rather than adversary detection, even though both products share the same underlying stack.
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Data Ingest and Normalization
The first stage of any Elastic SIEM deployment is data collection. Elastic Agent serves as the primary collection mechanism, deploying on endpoints and servers to gather operating system logs, process telemetry, network connections, file system events, and authentication records. For infrastructure that cannot run an agent, Beats modules (Filebeat, Winlogbeat, Packetbeat, Auditbeat) collect logs from specific sources including Windows Event Logs, syslog streams, network packet captures, and cloud provider APIs. Logstash handles transformation and enrichment for data sources that require complex parsing or conditional field mapping before indexing.
All incoming data is normalized to the Elastic Common Schema (ECS), a structured field naming convention that ensures a source IP address is always called source.ip whether it came from a Windows firewall log, a Palo Alto firewall, or an AWS VPC flow log. This normalization is what makes cross-source correlation practical. Without ECS, a rule looking for a source IP appearing in both authentication logs and DNS query logs would require source-specific field names for every log type, making rules fragile and maintenance-intensive.
Detection Rule Engine
Once data is indexed and normalized, the detection rule engine evaluates incoming events against configured rules on a scheduled basis. Rules are written in Event Query Language (EQL), Kibana Query Language (KQL), or as machine learning anomaly detection jobs. EQL is particularly suited for multi-event sequence detection. An EQL sequence rule can identify a pattern like: a process spawning a command shell, followed by that shell making an outbound network connection to an external IP, followed by a new registry run key being created, all within a five-minute window. This type of sequence detection is what distinguishes behavioral detection from simple signature matching.
Elastic's open-source detection rules repository on GitHub contains hundreds of pre-built rules mapped to MITRE ATT&CK techniques. These rules cover common attacker behaviors including credential dumping (T1003), lateral movement via pass-the-hash (T1550.002), persistence through scheduled tasks (T1053.005), and command-and-control communication over DNS (T1071.004). Organizations can import these rules directly, modify them for their environment, or suppress specific conditions that generate false positives in their infrastructure.
Machine Learning Integration
Elastic SIEM includes built-in machine learning jobs that establish behavioral baselines for users, hosts, and network entities. The authentication anomaly detection job, for example, monitors login patterns per user and flags events that deviate significantly from that user's historical behavior, such as a first-time login from a foreign country or an unusual login hour. These jobs run continuously against indexed data, producing anomaly scores that can feed into detection rules or be surfaced directly in the Security app for analyst review.
A Concrete Scenario: Detecting a Lateral Movement Chain
Consider a scenario where an attacker has compromised a developer workstation through a phishing email and is attempting to move laterally to a build server. The following chain of events unfolds:
process.parent.name: WINWORD.EXE).An EQL sequence rule correlating these four events across two hosts generates a single alert with a high severity score. The alert is enriched with threat intelligence from the configured feeds, flagging the external IP as a known command-and-control indicator. The analyst opens the Timeline workspace, where all four events are automatically pinned in chronological order, creating a visual narrative of the intrusion chain without manual reconstruction.
Investigation and Response
The Timeline workspace is Elastic SIEM's investigation environment. Analysts add events to a timeline by querying across indices, filtering by host, user, or time window, and annotating events with their findings. Timelines can be saved, shared with team members, and attached to cases. The Cases feature tracks the full investigation lifecycle, linking alerts, evidence, timelines, and analyst notes into a structured record that satisfies incident response documentation requirements. Elastic SIEM integrates with SOAR platforms through its native connector framework, allowing response actions such as host isolation, IP blocking, and ticket creation to be triggered directly from an alert.
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Security operations teams that lack a centralized SIEM operate reactively, reconstructing attacks after the fact from logs scattered across individual systems, cloud consoles, and network devices. The time required to manually correlate data across disparate sources means that attackers who move quickly can complete their objectives before defenders even recognize an intrusion has occurred. A properly deployed Elastic SIEM compresses the detection-to-response timeline by bringing all telemetry into a single queryable environment.
The cost structure of Elastic SIEM is a significant operational factor. Many commercial SIEM platforms charge based on daily ingest volume, creating a perverse incentive for security teams to reduce log collection to manage costs. Teams that turn off verbose logging to avoid licensing overages are systematically blind to the detailed process and network telemetry that behavioral detection requires. Elastic's model, which charges based on compute and storage rather than event volume, removes this incentive and allows organizations to collect as much data as their infrastructure can generate.
The consequences of inadequate SIEM capability are well-documented. In the 2020 SolarWinds supply chain compromise, affected organizations that lacked comprehensive endpoint telemetry and cross-source correlation were unable to identify the attacker's lateral movement and data exfiltration activity despite the intrusion persisting for months. Organizations with mature SIEM deployments that ingested both network flow data and endpoint process telemetry had a higher likelihood of detecting anomalous behaviors associated with the SUNBURST backdoor, such as unusual DNS lookups to Avsvmcloud[.]com domains and atypical process execution chains on SolarWinds Orion servers.
A common misconception about Elastic SIEM is that deploying the platform is equivalent to having a functional SIEM. The platform provides the infrastructure and tooling, but detection capability is a product of rule quality, data completeness, and analyst expertise. An Elastic SIEM deployment without tuned rules, comprehensive log coverage, and trained analysts produces the same outcome as no SIEM at all: a system that generates either silence or noise, neither of which is useful.
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The Cyber Defense Alliance approaches Elastic SIEM through the Threat Intelligence and Detection (TID) domain of the Planetary Defense Model, with the operational methodology of Predictive Defense Intelligence: see the threat before it sees you. Within this framework, Elastic SIEM is not treated as an alerting system. It is treated as a threat hypothesis engine.
CDA's operational approach to Elastic SIEM deployment begins with defining the threat model before configuring data sources. Most organizations deploy a SIEM and then decide what to look for. CDA works in the opposite direction: analysts identify the adversary profiles most relevant to the client's sector, map those adversaries' documented techniques to MITRE ATT&CK, and then configure data sources and detection rules specifically designed to surface those technique patterns. This means a financial services client's Elastic SIEM is tuned differently from a healthcare client's, even if both deployments use the same platform.
CDA detection engineers build and maintain custom EQL rules that go beyond the standard open-source rule library. These custom rules encode knowledge about attacker behavior specific to the client's environment: known administrative tools that would appear suspicious in a non-administrative context, internal service account behaviors that define a baseline, and network communication patterns that are normal for the organization's business applications but would be anomalous in any other context. This level of specificity is what separates a SIEM that catches known malware signatures from one that catches skilled human adversaries operating with legitimate credentials and tools.
The SPH (Security Program Health) domain perspective adds a governance layer to CDA's Elastic SIEM engagements. CDA conducts periodic detection coverage assessments that map the client's active detection rules against the full MITRE ATT&CK matrix, identifying technique gaps where an adversary could operate without generating any alert. These gap reports drive remediation priorities and inform decisions about additional data source onboarding. The result is a SIEM deployment that improves measurably over time rather than remaining static after initial configuration.
CDA also applies Elastic's machine learning capabilities to establish behavioral baselines during the first 30 days of a new deployment, using anomaly scores as a tuning signal rather than an operational alert, until the model has sufficient data to produce actionable output. This disciplined approach to ML integration prevents the alert fatigue that commonly results from enabling anomaly detection on a sparse or incomplete dataset.
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