Alerts in Different ATT&CK Tactics by Host

  1[metadata]
  2creation_date = "2025/11/19"
  3maturity = "production"
  4updated_date = "2026/07/21"
  5
  6[rule]
  7author = ["Elastic"]
  8description = """
  9This rule correlates medium-or-higher severity alerts involving the same host from at least two distinct detection rules
 10mapped to three or more ATT&CK tactics. Analysts can use this to prioritize triage and response, as this combination may
 11indicate host compromise.
 12"""
 13from = "now-8h"
 14interval = "1h"
 15language = "esql"
 16license = "Elastic License v2"
 17name = "Alerts in Different ATT&CK Tactics by Host"
 18risk_score = 73
 19rule_id = "29531d20-0e80-41d4-9ec6-d6b58e4a475c"
 20severity = "high"
 21tags = ["Use Case: Threat Detection", "Rule Type: Higher-Order Rule", "Resources: Investigation Guide"]
 22timestamp_override = "event.ingested"
 23type = "esql"
 24
 25query = '''
 26from .alerts-security.*  metadata _id
 27
 28// filter for medium-or-higher severity alerts, excluding threat_match, machine_learning, and deprecated rules.
 29| where kibana.alert.risk_score > 21 and
 30        kibana.alert.rule.name IS NOT NULL and kibana.alert.rule.rule_id IS NOT NULL and
 31        host.id is not null and event.dataset is not null and
 32        kibana.alert.rule.type not in ("threat_match", "machine_learning") and
 33        // Exclude a deprecated rule whose alert name does not carry the standard prefix
 34        kibana.alert.rule.name != "Potential PrintNightmare File Modification" and
 35        not kibana.alert.rule.name like "Deprecated - *" and
 36        not KQL("""kibana.alert.rule.tags : "Rule Type: Higher-Order Rule" """)
 37
 38// extract unique counts and values by host.id
 39| stats Esql.alerts_count = COUNT(*),
 40        Esql.kibana_alert_rule_name_distinct_count = COUNT_DISTINCT(kibana.alert.rule.name),
 41        Esql.kibana_alert_rule_id_distinct_count = COUNT_DISTINCT(kibana.alert.rule.rule_id),
 42        Esql.event_module_values = VALUES(event.module),
 43        Esql.host_name_values = VALUES(host.name),
 44        Esql.kibana_alert_rule_name_values = VALUES(kibana.alert.rule.name),
 45        Esql.kibana_alert_rule_id_values = VALUES(kibana.alert.rule.rule_id),
 46        Esql.threat_tactic_id_distinct_count = COUNT_DISTINCT(kibana.alert.rule.threat.tactic.id),
 47        Esql.threat_tactic_name_values = VALUES(kibana.alert.rule.threat.tactic.name),
 48        Esql.process_executable_values = VALUES(process.executable),
 49        Esql.process_parent_executable_values = VALUES(process.parent.executable),
 50        Esql.process_command_line_values = VALUES(process.command_line),
 51        Esql.process_entity_id_distinct_count = COUNT_DISTINCT(process.entity_id) by host.id
 52
 53// filter for risky hosts with multiple distinct rules across multiple tactics
 54// Distinct rule IDs prevent one rule mapped to multiple tactics from satisfying the correlation.
 55| where Esql.kibana_alert_rule_name_distinct_count >= 2 and
 56        Esql.kibana_alert_rule_id_distinct_count >= 2 and
 57        Esql.threat_tactic_id_distinct_count >= 3
 58
 59// Populate the native host name for alert triage without changing the host.id correlation key.
 60| eval host.name = MV_FIRST(Esql.host_name_values)
 61
 62// fields populated in the resulting alert
 63| keep host.id,
 64       host.name,
 65       Esql.alerts_count,
 66       Esql.kibana_alert_rule_name_distinct_count,
 67       Esql.kibana_alert_rule_id_distinct_count,
 68       Esql.process_entity_id_distinct_count,
 69       Esql.event_module_values,
 70       Esql.host_name_values,
 71       Esql.kibana_alert_rule_name_values,
 72       Esql.kibana_alert_rule_id_values,
 73       Esql.threat_tactic_name_values,
 74       Esql.process_executable_values,
 75       Esql.process_parent_executable_values,
 76       Esql.process_command_line_values
 77'''
 78note = """## Triage and analysis
 79
 80> **Disclaimer**:
 81> This investigation guide was created using generative AI technology and has been reviewed to improve its accuracy and relevance. While every effort has been made to ensure its quality, we recommend validating the content and adapting it to suit your specific environment and operational needs.
 82
 83### Investigating Alerts in Different ATT&CK Tactics by Host
 84
 85The rule identifies hosts with alerts across multiple ATT&CK tactics, which may indicate compromise. It helps analysts focus on high-risk hosts by correlating diverse alerts. The resulting alert is grouped, so the `Esql.*_values` fields summarize contributing alerts without preserving event order or relationships between values.
 86
 87### Possible investigation steps
 88
 89- Review the alert details to identify the specific host involved and the different ATT&CK tactics that triggered the alerts.
 90- Examine the timeline of the alerts to understand the sequence of events and determine if there is a pattern or progression in the tactics used.
 91- Correlate the alert data with other logs and telemetry from the host, such as process creation, network connections, and file modifications, to gather additional context.
 92- Investigate any known vulnerabilities or misconfigurations on the host that could have been exploited by the adversary.
 93- Check for any indicators of compromise (IOCs) associated with the alerts, such as suspicious IP addresses, domains, or file hashes, and search for these across the network.
 94- Assess the impact and scope of the potential compromise by determining if other hosts or systems have similar alerts or related activity.
 95
 96### False positive analysis
 97
 98- Alerts from routine administrative tasks may trigger multiple tactics. Review and exclude known benign activities such as scheduled software updates or system maintenance.
 99- Security tools running on the host might generate alerts across different tactics. Identify and exclude alerts from trusted security applications to reduce noise.
100- Automated scripts or batch processes can mimic adversarial behavior. Analyze and whitelist these processes if they are verified as non-threatening.
101- Frequent alerts from development or testing environments can be misleading. Consider excluding these environments from the rule or applying a different risk score.
102- User behavior anomalies, such as accessing multiple systems or applications, might trigger alerts. Implement user behavior baselines to differentiate between normal and suspicious activities.
103
104### Response and remediation
105
106- Isolate the affected host from the network immediately to prevent further lateral movement by the adversary.
107- Conduct a thorough forensic analysis of the host to identify the specific vulnerabilities exploited and gather evidence of the attack phases involved.
108- Remove any identified malicious software or unauthorized access tools from the host, ensuring all persistence mechanisms are eradicated.
109- Apply security patches and updates to the host to address any exploited vulnerabilities and prevent similar attacks.
110- Restore the host from a known good backup if necessary, ensuring that the backup is free from compromise.
111- Monitor the host and network for any signs of re-infection or further suspicious activity, using enhanced logging and alerting based on the identified attack patterns.
112- Escalate the incident to the appropriate internal or external cybersecurity teams for further investigation and potential legal action if the attack is part of a larger campaign."""
113
114[rule.investigation_fields]
115field_names = [
116    "host.name",
117    "host.id",
118    "Esql.threat_tactic_name_values",
119    "Esql.kibana_alert_rule_name_values",
120    "Esql.alerts_count",
121]