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.

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

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

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

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

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:

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

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

{
  "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

Verification Criteria

Confirm successful execution by validating:

• [ ] All prerequisite tools and access requirements are satisfied
• [ ] Each workflow step completed without errors
• [ ] Output matches expected format and contains expected data
• [ ] No security warnings or misconfigurations detected
• [ ] Results are documented and evidence is preserved for audit

Compliance Framework Mapping

This skill supports compliance evidence collection across multiple frameworks:

• SOC 2: CC7.1 (Monitoring), CC7.2 (Anomaly Detection), CC7.3 (Incident Identification)
• ISO 27001: A.12.4 (Logging & Monitoring), A.16.1 (Security Incident Management)
• NIST 800-53: AU-6 (Audit Review), SI-4 (System Monitoring), IR-5 (Incident Monitoring)
• NIST CSF: DE.AE (Anomalies & Events), DE.CM (Continuous Monitoring)

Claw GRC Tip: When this skill is executed by a registered agent, compliance evidence is automatically captured and mapped to the relevant controls in your active frameworks.

Deploying This Skill with Claw GRC

Agent Execution

Register this skill with your Claw GRC agent for automated execution:

# Install via CLI
npx claw-grc skills add performing-alert-triage-with-elastic-siem

# Or load dynamically via MCP
grc.load_skill("performing-alert-triage-with-elastic-siem")

Audit Trail Integration

When executed through Claw GRC, every step of this skill generates tamper-evident audit records:

• SHA-256 chain hashing ensures no step can be modified after execution
• Evidence artifacts (configs, scan results, logs) are automatically attached to relevant controls
• Trust score impact — successful execution increases your agent's trust score

Continuous Compliance

Schedule this skill for recurring execution to maintain continuous compliance posture. Claw GRC monitors for drift and alerts when re-execution is needed.

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