Performing Alert Triage with Elastic SIEM

Overview

Alert triage in Elastic Security is the systematic process of reviewing, classifying, and prioritizing security alerts to determine which represent genuine threats. Elastic's AI-driven Attack Discovery feature can triage hundreds of alerts down to discrete attack chains, but skilled analyst triage remains essential. A structured triage workflow typically takes 5-10 minutes per alert cluster using Elastic's built-in tools.

When to Use

• When conducting security assessments that involve performing alert triage with elastic siem
• When following incident response procedures for related security events
• When performing scheduled security testing or auditing activities
• When validating security controls through hands-on testing

Prerequisites

• Elastic Security deployed (version 8.x or later)
• Elastic Agent or Beats configured for endpoint and network data collection
• Detection rules enabled and generating alerts
• Elastic Common Schema (ECS) compliance across data sources
• Analyst access to Kibana Security app with appropriate privileges

Alert Triage Workflow

Step 1: Initial Alert Assessment (2 minutes)

When viewing an alert in Elastic Security, review the alert details panel:

Alert Details Panel:
- Rule Name and Description
- Severity and Risk Score
- MITRE ATT&CK Mapping
- Host and User Context
- Process Tree (for endpoint alerts)
- Timeline of related events

Key Fields to Examine First

| Field | Purpose | ECS Field | |---|---|---| | Rule severity | Initial priority assessment | kibana.alert.severity | | Risk score | Quantified threat level | kibana.alert.risk_score | | Host name | Affected system | host.name | | User name | Affected identity | user.name | | Process name | Executing process | process.name | | Source IP | Origin of activity | source.ip | | Destination IP | Target of activity | destination.ip | | MITRE tactic | Attack stage | threat.tactic.name |

Step 2: Context Gathering (3 minutes)

Query Related Events with ES|QL

esql
FROM logs-endpoint.events.*
| WHERE host.name == "affected-host" AND @timestamp > NOW() - 1 HOUR
| STATS count = COUNT(*) BY event.category, event.action
| SORT count DESC

Find All Activity from Suspicious User

esql
FROM logs-*
| WHERE user.name == "suspicious-user" AND @timestamp > NOW() - 24 HOURS
| STATS count = COUNT(*), unique_hosts = COUNT_DISTINCT(host.name) BY event.category
| SORT count DESC

Check for Related Alerts from Same Source

esql
FROM .alerts-security.alerts-default
| WHERE source.ip == "10.0.0.50" AND @timestamp > NOW() - 24 HOURS
| STATS alert_count = COUNT(*) BY kibana.alert.rule.name, kibana.alert.severity
| SORT alert_count DESC

Investigate Lateral Movement from Same IP

esql
FROM logs-system.auth-*
| WHERE source.ip == "10.0.0.50" AND event.outcome == "success"
| STATS login_count = COUNT(*), hosts = COUNT_DISTINCT(host.name) BY user.name
| WHERE hosts > 3

Step 3: Threat Intelligence Enrichment (2 minutes)

Check indicators against threat intelligence:

esql
FROM logs-ti_*
| WHERE threat.indicator.ip == "203.0.113.50"
| KEEP threat.indicator.type, threat.indicator.provider, threat.indicator.confidence, threat.feed.name

Check File Hash Against Known Threats

esql
FROM logs-endpoint.events.file-*
| WHERE file.hash.sha256 == "abc123..."
| STATS occurrences = COUNT(*) BY host.name, file.path, user.name

Step 4: Classification Decision (2 minutes)

| Classification | Criteria | Action | |---|---|---| | True Positive | Confirmed malicious activity | Escalate to incident, begin containment | | Benign True Positive | Expected behavior matching rule | Document in alert notes, acknowledge | | False Positive | Rule triggered on benign activity | Mark as false positive, create tuning task | | Needs Investigation | Insufficient data for determination | Assign for deeper investigation |

Step 5: Documentation and Escalation (1 minute)

For each triaged alert, document:

• Classification decision with rationale
• Evidence artifacts examined
• Related alerts or investigations
• Recommended next steps

Detection Rules for Triage

Pre-Built Detection Rules

Elastic Security includes 1000+ pre-built detection rules organized by:

• MITRE ATT&CK Tactic: Initial Access, Execution, Persistence, etc.
• Platform: Windows, Linux, macOS, Cloud
• Data Source: Endpoint, Network, Cloud, Identity

Custom Alert Correlation Rule

json
{
  "name": "Multiple Failed Logins Followed by Success",
  "type": "threshold",
  "query": "event.category:authentication AND event.outcome:failure",
  "threshold": {
    "field": ["source.ip", "user.name"],
    "value": 5,
    "cardinality": [
      {
        "field": "user.name",
        "value": 3
      }
    ]
  },
  "severity": "high",
  "risk_score": 73,
  "threat": [
    {
      "framework": "MITRE ATT&CK",
      "tactic": {
        "id": "TA0006",
        "name": "Credential Access"
      },
      "technique": [
        {
          "id": "T1110",
          "name": "Brute Force"
        }
      ]
    }
  ]
}

AI-Assisted Triage

Elastic AI Assistant Integration

1. Open alert in Elastic Security
2. Click AI Assistant panel
3. Use quick prompts:

• "Summarize this alert" - Get initial assessment
• "Generate ES|QL query to find related activity" - Expand investigation
• "What are the recommended response actions?" - Get playbook guidance
• "Is this likely a false positive?" - Get AI confidence assessment

Attack Discovery

Elastic's Attack Discovery automatically:

• Groups related alerts into attack chains
• Maps alerts to MITRE ATT&CK kill chain stages
• Filters false positives using ML models
• Prioritizes based on business impact
• Provides narrative summary of the attack

Triage Prioritization Matrix

| Risk Score | Severity | Asset Criticality | Response SLA | |---|---|---|---| | 90-100 | Critical | High | 15 minutes | | 70-89 | High | High | 30 minutes | | 70-89 | High | Medium | 1 hour | | 50-69 | Medium | Any | 4 hours | | 21-49 | Low | Any | 8 hours | | 1-20 | Informational | Any | 24 hours |

Triage Metrics and KPIs

| Metric | Target | Measurement | |---|---|---| | Mean Time to Triage (MTTT) | < 10 minutes | Time from alert creation to classification | | False Positive Rate | < 30% | False positives / total alerts | | Escalation Rate | 10-20% | Escalated alerts / total alerts | | Alert Coverage | > 80% | Triaged alerts / generated alerts per shift | | Reclassification Rate | < 5% | Changed classifications / total classified |

References

• Elastic Security - Triage Alerts Documentation
• SOC Analyst's Guide to Triage with Elastic
• Elastic Blog - AI and 2025 SIEM Landscape
• Reducing False Positives with Elastic and Tines