Newly Observed High Severity Detection Alert

ELASTIC detection rule by Elastic · Elastic License v2

This rule detects Elastic SIEM high severity detection alerts that are observed for the first time in the previous 5 days of alert history. It highlights low-volume, newly observed alerts tied to a specific detection rule, analysts can use this to prioritize triage and response.

[metadata]
creation_date = "2026/01/07"
maturity = "production"
updated_date = "2026/04/27"

[rule]
author = ["Elastic"]
description = """
This rule detects Elastic SIEM high severity detection alerts that are observed for the first time in the previous 5 days
of alert history. It highlights low-volume, newly observed alerts tied to a specific detection rule, analysts can use this
to prioritize triage and response.
"""
from = "now-7205m"
interval = "5m"
language = "esql"
license = "Elastic License v2"
name = "Newly Observed High Severity Detection Alert"
risk_score = 73
rule_id = "1a3d5b36-b995-4ace-9b85-8a0af429ccf6"
severity = "high"
tags = ["Use Case: Threat Detection", "Rule Type: Higher-Order Rule", "Resources: Investigation Guide"]
timestamp_override = "event.ingested"
type = "esql"

query = '''
FROM .alerts-security.*
| where kibana.alert.rule.name is not null and kibana.alert.risk_score >= 73  and
  not kibana.alert.rule.type in ("threat_match", "machine_learning", "new_terms") and
  not kibana.alert.rule.name like "Deprecated - *" and kibana.alert.rule.name != "My First Rule" and
  // covered by 7306ce7d-5c90-4f42-aa6c-12b0dc2fe3b8
  event.dataset != "endpoint.alerts" and
  not KQL("""kibana.alert.rule.tags : "Rule Type: Higher-Order Rule" """)
| STATS Esql.alerts_count = count(*),
        Esql.first_time_seen = MIN(@timestamp),
        Esql.last_time_seen = MAX(@timestamp),
        Esql.process_executable = VALUES(process.executable),
        Esql.process_command_line_values = VALUES(process.command_line),
        Esql.process_parent_executable_values = VALUES(process.parent.executable),
       Esql.file_path_values = VALUES(file.path),
        Esql.dll_path_values = VALUES(dll.path),
        Esql.user_id_values = VALUES(user.id),
        Esql.user_name_values = VALUES(user.name),
        Esql.agent_id_values = VALUES(agent.id),
        Esql.host_id_values = VALUES(host.id),
        Esql.event_module_values = VALUES(event.module),
        Esql.source_ip_values = VALUES(source.ip), 
        Esql.kibana_alert_rule_name_values = VALUES(kibana.alert.rule.name),
        Esql.agent_id_distinct_count = COUNT_DISTINCT(agent.id) by kibana.alert.rule.name
// fist time seen in the last 5 days - defined in the rule schedule Additional look-back time
| eval Esql.recent = DATE_DIFF("minute", Esql.first_time_seen, now())
// first time seen is within 10m of the rule execution time
| where Esql.recent <= 10 and Esql.agent_id_distinct_count == 1 and Esql.alerts_count <= 10 and (Esql.last_time_seen == Esql.first_time_seen)

// Move single values to their corresponding ECS fields for alerts exclusion
| eval host.id = mv_min(Esql.host_id_values)

| keep host.id, kibana.alert.rule.name, Esql.*
'''
note = """## Triage and analysis

### Investigating Newly Observed High Severity Detection Alert

This rule surfaces newly observed, low-frequency behavior high severity alerts affecting a single agent within the current day.

Because the alert has not been seen previously for this rule and host, it should be prioritized for validation to determine
whether it represents a true compromise or rare benign activity.

### Investigation Steps

- Identify the affected host, user and review the associated rule name to understand the behavior that triggered the alert.
- Validate the user context under which the activity occurred and assess whether it aligns with normal behavior for that account.
- Refer to the specific rule investiguation guide for further actions.

### False Positive Considerations

- Newly deployed or updated software may introduce behavior not previously observed on the host.
- Administrative scripts or automation tools can trigger behavior-based detections when first introduced.
- Security tooling, IT management agents, or EDR integrations may generate new behavior alerts during updates or configuration changes.
- Development or testing environments may produce one-off behaviors that resemble malicious techniques.

### Response and Remediation

- If the activity is confirmed malicious, isolate the affected host to prevent further execution or lateral movement.
- Terminate malicious processes and remove any dropped files or persistence mechanisms.
- Collect forensic artifacts to understand initial access and execution flow.
- Patch or remediate any vulnerabilities or misconfigurations that enabled the behavior.
- If benign, document the finding and consider tuning or exception handling to reduce future noise.
- Continue monitoring the host and environment for recurrence of the behavior or related alerts."""
references = ["https://www.elastic.co/docs/solutions/security/detect-and-alert/about-detection-rules"]

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Executable code, not a declarative rule

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