---
title: Tutorial: Threat hunting with ES|QL
description: This hands-on tutorial demonstrates advanced threat hunting techniques using the Elasticsearch Query Language (ES|QL). Following a simulated Advanced...
url: https://www.elastic.co/docs/solutions/security/esql-for-security/esql-threat-hunting-tutorial
products:
  - Elastic Security
applies_to:
  - Elastic Cloud Serverless: Generally available
  - Elastic Stack: Generally available
---

# Tutorial: Threat hunting with ES|QL
This hands-on tutorial demonstrates advanced threat hunting techniques using the [Elasticsearch Query Language (ES|QL)](https://www.elastic.co/docs/reference/query-languages/esql).
Following a simulated Advanced Persistent Threat (APT) campaign, we analyze security events across authentication, process execution, and network telemetry to detect:
- Initial compromise via malicious email attachments
- Lateral movement through the network
- Privilege escalation attempts
- Data exfiltration activities

ES|QL enables powerful transformations, filtering, enrichment, and statistical analysis, making it ideal for complex security investigations. This tutorial provides practical examples of how to leverage ES|QL for threat hunting, from identifying suspicious user behavior to building attack timelines.
<admonition title="Requirements">
  You need a running Elasticsearch cluster, together with Kibana to run this tutorial. Refer to [choose your deployment type](/docs/deploy-manage/deploy#choosing-your-deployment-type) for deployment options.
</admonition>


## How to run ES|QL queries

In this tutorial, ES|QL examples are displayed in the following format:
```esql
FROM windows-security-logs
| WHERE event.code == "4624"
| LIMIT 1000
```

You can run these queries using:
- **Interactive interfaces**:
  - [Timeline](/docs/solutions/security/investigate/timeline#esql-in-timeline). Find **Timelines** in the navigation menu or by using the [global search field](https://www.elastic.co/docs/explore-analyze/find-and-organize/find-apps-and-objects).
- [Discover](https://www.elastic.co/docs/explore-analyze/discover/try-esql). Find **Discover** in the navigation menu or by using the [global search field](https://www.elastic.co/docs/explore-analyze/find-and-organize/find-apps-and-objects).
- **REST API** via [Dev Tools Console](https://www.elastic.co/docs/reference/query-languages/esql/esql-rest#esql-kibana-console). This requires additional formatting:
  <dropdown title="View Console syntax for ES|QL">
  ```json

  {
  "query": """
  FROM windows-security-logs
  | WHERE event.code == "4624"
  | LIMIT 1000
  """
  }
  ```
  </dropdown>


## Step 0: Add sample data

To follow along with this tutorial, you need to add sample data to your cluster, using the [Dev Tools Console](https://www.elastic.co/docs/reference/query-languages/esql/esql-rest#esql-kibana-console).
Broadly, there are two types of data:
1. **Core indices**: These are the main security indices that contain the logs and events you want to analyze. We need three core indices: `windows-security-logs`, `process-logs`, and `network-logs`.
2. **Lookup indices**: These are auxiliary indices that provide additional context to your core data. We need three lookup indices: `asset-inventory`, `user-context`, and `threat-intel`.


### Create core indices

First, create the core security indices for our threat hunting scenario:
```json

{
  "mappings": {
    "properties": {
      "@timestamp": {"type": "date"},
      "event": {
        "properties": {
          "code": {"type": "keyword"}, # Event codes like 4624 (successful logon) and 4625 (failed logon) are stored as keywords for exact matching.
          "action": {"type": "keyword"}
        }
      },
      "user": {
        "properties": {
          "name": {"type": "keyword"},
          "domain": {"type": "keyword"}
        }
      },
      "host": {
        "properties": {
          "name": {"type": "keyword"},
          "ip": {"type": "ip"}
        }
      },
      "source": {
        "properties": {
          "ip": {"type": "ip"}
        }
      },
      "logon": {
        "properties": {
          "type": {"type": "keyword"}
        }
      }
    }
  }
}
```

Now let's add some sample data to the `windows-security-logs` index around authentication events, namely failed and successful logins.
```json

{"index":{"_index":"windows-security-logs"}}
{"@timestamp":"2025-05-20T08:15:00Z","event":{"code":"4625","action":"logon_failed"},"user":{"name":"jsmith","domain":"corp"},"host":{"name":"WS-001","ip":"10.1.1.50"},"source":{"ip":"10.1.1.100"}}
{"index":{"_index":"windows-security-logs"}}
{"@timestamp":"2025-05-20T08:17:00Z","event":{"code":"4624","action":"logon_success"},"user":{"name":"jsmith","domain":"corp"},"host":{"name":"WS-001","ip":"10.1.1.50"},"source":{"ip":"10.1.1.100"},"logon":{"type":"3"}}
{"index":{"_index":"windows-security-logs"}}
{"@timestamp":"2025-05-20T09:30:00Z","event":{"code":"4624","action":"logon_success"},"user":{"name":"jsmith","domain":"corp"},"host":{"name":"SRV-001","ip":"10.1.2.10"},"source":{"ip":"10.1.1.50"},"logon":{"type":"3"}}
{"index":{"_index":"windows-security-logs"}}
{"@timestamp":"2025-05-20T10:45:00Z","event":{"code":"4624","action":"logon_success"},"user":{"name":"jsmith","domain":"corp"},"host":{"name":"DB-001","ip":"10.1.3.5"},"source":{"ip":"10.1.2.10"},"logon":{"type":"3"}}
{"index":{"_index":"windows-security-logs"}}
{"@timestamp":"2025-05-20T02:30:00Z","event":{"code":"4624","action":"logon_success"},"user":{"name":"admin","domain":"corp"},"host":{"name":"DC-001","ip":"10.1.4.10"},"source":{"ip":"10.1.3.5"},"logon":{"type":"3"}}
```

Next, create an index for process execution logs.
```json

{
  "mappings": {
    "properties": {
      "@timestamp": {"type": "date"},
      "process": {
        "properties": {
          "name": {"type": "keyword"},
          "command_line": {"type": "text"}, # Command lines are stored as text fields to enable full-text search for suspicious parameters and encoded commands.
          "parent": {
            "properties": {
              "name": {"type": "keyword"}
            }
          }
        }
      },
      "user": {
        "properties": {
          "name": {"type": "keyword"}
        }
      },
      "host": {
        "properties": {
          "name": {"type": "keyword"}
        }
      }
    }
  }
}
```

Add some sample data to the `process-logs` index.
```json

{"index":{"_index":"process-logs"}}
{"@timestamp":"2025-05-20T08:20:00Z","process":{"name":"powershell.exe","command_line":"powershell.exe -enc JABzAD0ATgBlAHcALgBPAGIAagBlAGMAdAAgAFMAeQBzAHQAZQBtAC4ATgBlAHQALgBXAGUAYgBDAGwAaQBlAG4AdAA=","parent":{"name":"winword.exe"}},"user":{"name":"jsmith"},"host":{"name":"WS-001"}}
{"index":{"_index":"process-logs"}}
{"@timestamp":"2025-05-20T09:35:00Z","process":{"name":"net.exe","command_line":"net user /domain","parent":{"name":"cmd.exe"}},"user":{"name":"jsmith"},"host":{"name":"SRV-001"}}
{"index":{"_index":"process-logs"}}
{"@timestamp":"2025-05-20T10:50:00Z","process":{"name":"sqlcmd.exe","command_line":"sqlcmd -S localhost -Q \"SELECT * FROM customers\"","parent":{"name":"powershell.exe"}},"user":{"name":"jsmith"},"host":{"name":"DB-001"}}
{"index":{"_index":"process-logs"}}
{"@timestamp":"2025-05-20T02:35:00Z","process":{"name":"ntdsutil.exe","command_line":"ntdsutil \"ac i ntds\" \"ifm\" \"create full c:\\temp\\ntds\"","parent":{"name":"cmd.exe"}},"user":{"name":"admin"},"host":{"name":"DC-001"}}
{"index":{"_index":"process-logs"}}
{"@timestamp":"2025-05-20T12:15:00Z","process":{"name":"schtasks.exe","command_line":"schtasks.exe /create /tn UpdateCheck /tr c:\\windows\\temp\\update.exe /sc daily","parent":{"name":"cmd.exe"}},"user":{"name":"jsmith"},"host":{"name":"WS-001"}}
{"index":{"_index":"process-logs"}}
{"@timestamp":"2025-05-20T12:30:00Z","process":{"name":"schtasks.exe","command_line":"schtasks.exe /create /tn SystemManager /tr powershell.exe -enc ZQBjAGgAbwAgACIASABlAGwAbABvACIA /sc minute /mo 5","parent":{"name":"powershell.exe"}},"user":{"name":"jsmith"},"host":{"name":"SRV-001"}}
{"index":{"_index":"process-logs"}}
{"@timestamp":"2025-05-20T13:15:00Z","process":{"name":"sc.exe","command_line":"sc.exe create RemoteService binPath= c:\\windows\\temp\\remote.exe","parent":{"name":"cmd.exe"}},"user":{"name":"jsmith"},"host":{"name":"DB-001"}}
{"index":{"_index":"process-logs"}}
{"@timestamp":"2025-05-20T13:20:00Z","process":{"name":"sc.exe","command_line":"sc.exe create BackdoorService binPath= c:\\programdata\\svc.exe","parent":{"name":"powershell.exe"}},"user":{"name":"jsmith"},"host":{"name":"SRV-001"}}
{"index":{"_index":"process-logs"}}
{"@timestamp":"2025-05-20T13:25:00Z","process":{"name":"sc.exe","command_line":"sc.exe create PersistenceService binPath= c:\\windows\\system32\\malicious.exe","parent":{"name":"cmd.exe"}},"user":{"name":"admin"},"host":{"name":"DC-001"}}
```

Next, create an index for network traffic logs.
```json

{
  "mappings": {
    "properties": {
      "@timestamp": {"type": "date"},
      "source": {
        "properties": {
          "ip": {"type": "ip"},
          "port": {"type": "integer"}
        }
      },
      "destination": {
        "properties": {
          "ip": {"type": "ip"},
          "port": {"type": "integer"}
        }
      },
      "network": {
        "properties": {
          "bytes": {"type": "long"},
          "protocol": {"type": "keyword"}
        }
      },
      "host": {
        "properties": {
          "name": {"type": "keyword"}
        }
      }
    }
  }
}
```

Add some sample data to the `network-logs` index.
```json

{"index":{"_index":"network-logs"}}
{"@timestamp":"2025-05-20T08:25:00Z","source":{"ip":"10.1.1.50","port":52341},"destination":{"ip":"185.220.101.45","port":443},"network":{"bytes":2048,"protocol":"tcp"},"host":{"name":"WS-001"}}
{"index":{"_index":"network-logs"}}
{"@timestamp":"2025-05-20T11:15:00Z","source":{"ip":"10.1.3.5","port":54892},"destination":{"ip":"185.220.101.45","port":443},"network":{"bytes":50000000,"protocol":"tcp"},"host":{"name":"DB-001"}}
{"index":{"_index":"network-logs"}}
{"@timestamp":"2025-05-20T02:40:00Z","source":{"ip":"10.1.4.10","port":61234},"destination":{"ip":"185.220.101.45","port":443},"network":{"bytes":500000000,"protocol":"tcp"},"host":{"name":"DC-001"}}
```


### Create lookup indices

The lookup mode enables these indices to be used with [`LOOKUP JOIN`](https://www.elastic.co/docs/reference/query-languages/esql/commands/lookup-join) operations for enriching security events with asset context.
Create the indices we need with the `lookup` index mode.
```json

{
  "mappings": {
    "properties": {
      "host.name": {"type": "keyword"},
      "asset.criticality": {"type": "keyword"},
      "asset.owner": {"type": "keyword"},
      "asset.department": {"type": "keyword"}
    }
  },
  "settings": {
    "index.mode": "lookup" 
  }
}
```

```json

{
  "mappings": {
    "properties": {
      "user.name": {"type": "keyword"},
      "user.role": {"type": "keyword"},
      "user.department": {"type": "keyword"},
      "user.privileged": {"type": "boolean"}
    }
  },
  "settings": {
    "index.mode": "lookup"
  }
}
```

```json

{
  "mappings": {
    "properties": {
      "indicator.value": {"type": "keyword"},
      "indicator.type": {"type": "keyword"},
      "threat.name": {"type": "keyword"},
      "threat.severity": {"type": "keyword"}
    }
  },
  "settings": {
    "index.mode": "lookup"
  }
}
```

Now we can populate the lookup indices with contextual data. This single bulk operation indexes data into the `user-context`, `threat-intel` and `asset-inventory` indices with one request.
```json

{"index":{"_index":"asset-inventory"}}
{"host.name":"WS-001","asset.criticality":"medium","asset.owner":"IT","asset.department":"finance"}
{"index":{"_index":"asset-inventory"}}
{"host.name":"SRV-001","asset.criticality":"high","asset.owner":"IT","asset.department":"operations"}
{"index":{"_index":"asset-inventory"}}
{"host.name":"DB-001","asset.criticality":"critical","asset.owner":"DBA","asset.department":"finance"}
{"index":{"_index":"asset-inventory"}}
{"host.name":"DC-001","asset.criticality":"critical","asset.owner":"IT","asset.department":"infrastructure"}
{"index":{"_index":"user-context"}}
{"user.name":"jsmith","user.role":"analyst","user.department":"finance","user.privileged":false}
{"index":{"_index":"user-context"}}
{"user.name":"admin","user.role":"administrator","user.department":"IT","user.privileged":true}
{"index":{"_index":"threat-intel"}}
{"indicator.value":"185.220.101.45","indicator.type":"ip","threat.name":"APT-29","threat.severity":"high"}
{"index":{"_index":"threat-intel"}}
{"indicator.value":"powershell.exe","indicator.type":"process","threat.name":"Living off the Land","threat.severity":"medium"}
```


## Step 1: Hunt for initial compromise indicators

The first phase of our hunt focuses on identifying the initial compromise. We want to search for suspicious PowerShell execution from Office applications, which is a common initial attack vector.
```esql
FROM process-logs
| WHERE process.name == "powershell.exe" AND process.parent.name LIKE "*word*" 
| LOOKUP JOIN asset-inventory ON host.name 
| LOOKUP JOIN user-context ON user.name 
| EVAL encoded_command = CASE(process.command_line LIKE "*-enc*", true, false) 
| WHERE encoded_command == true 
| STATS count = COUNT(*) BY host.name, user.name, asset.criticality 
| LIMIT 1000
```

**Response**
The response contains a summary of the suspicious PowerShell executions, including the host name, user name, and asset criticality.

| count | host.name | user.name | asset.criticality |
|-------|-----------|-----------|-------------------|
| 1     | WS-001    | jsmith    | medium            |


## Step 2: Detect lateral movement patterns

In this step, we track user authentication across multiple systems. This is important for identifying lateral movement and potential privilege escalation.
This query demonstrates how [`DATE_TRUNC`](https://www.elastic.co/docs/reference/query-languages/esql/functions-operators/date-time-functions#esql-date_trunc) creates time windows for velocity analysis, combining
[`COUNT_DISTINCT`](https://www.elastic.co/docs/reference/query-languages/esql/functions-operators/aggregation-functions#esql-count_distinct) aggregations with [`DATE_DIFF`](https://www.elastic.co/docs/reference/query-languages/esql/functions-operators/date-time-functions#esql-date_diff) calculations to measure both the scope and speed of user movement across network assets.
```esql
FROM windows-security-logs
| WHERE event.code == "4624" AND logon.type == "3" 
| LOOKUP JOIN asset-inventory ON host.name
| EVAL time_bucket = DATE_TRUNC(30 minute, @timestamp) 
| STATS unique_hosts = COUNT_DISTINCT(host.name),
        criticality_levels = COUNT_DISTINCT(asset.criticality),
        active_periods = COUNT_DISTINCT(time_bucket),
        first_login = MIN(@timestamp),
        last_login = MAX(@timestamp) 
BY user.name 
| WHERE unique_hosts > 2
| EVAL time_span_hours = DATE_DIFF("hour", first_login, last_login) 
| EVAL movement_velocity = ROUND(unique_hosts / (time_span_hours + 1), 2)
| EVAL lateral_movement_score = unique_hosts * criticality_levels 
| SORT lateral_movement_score DESC 
| LIMIT 1000
```

**Response**
The response shows users who logged into multiple hosts, their criticality levels, and the velocity of their lateral movement.

| unique_hosts | criticality_levels | active_periods | first_login              | last_login               | user.name | time_span_hours | movement_velocity | lateral_movement_score |
|--------------|--------------------|----------------|--------------------------|--------------------------|-----------|-----------------|-------------------|------------------------|
| 3            | 3                  | 3              | 2025-05-20T08:17:00.000Z | 2025-05-20T10:45:00.000Z | jsmith    | 2               | 1                 | 9                      |


## Step 3: Identify data access and potential exfiltration

Advanced attackers often target sensitive data. We want to hunt for database access and large data transfers to external systems.
```esql
FROM network-logs
| WHERE NOT CIDR_MATCH(destination.ip, "10.0.0.0/8", "192.168.0.0/16") 
| EVAL indicator.value = TO_STRING(destination.ip) 
| LOOKUP JOIN threat-intel ON indicator.value
| LOOKUP JOIN asset-inventory ON host.name
| WHERE threat.name IS NOT NULL
| STATS total_bytes = SUM(network.bytes),
        connection_count = COUNT(*),
        time_span = DATE_DIFF("hour", MIN(@timestamp), MAX(@timestamp)) 
BY host.name, destination.ip, threat.name, asset.criticality
| EVAL mb_transferred = ROUND(total_bytes / 1048576, 2) 
| EVAL risk_score = CASE(
    asset.criticality == "critical" AND mb_transferred > 100, 10,
    asset.criticality == "high" AND mb_transferred > 100, 7,
    mb_transferred > 50, 5,
    3
  ) 
| WHERE total_bytes > 1000000
| SORT risk_score DESC, total_bytes DESC
| LIMIT 1000
```

**Response**
The response shows external data transfers, their risk scores, and the amount of data transferred.

| total_bytes | connection_count | time_span | host.name | destination.ip | threat.name | asset.criticality | mb_transferred | risk_score |
|-------------|------------------|-----------|-----------|----------------|-------------|-------------------|----------------|------------|
| 500000000   | 1                | 0         | DC-001    | 185.220.101.45 | APT-29      | critical          | 476            | 10         |
| 50000000    | 1                | 0         | DB-001    | 185.220.101.45 | APT-29      | critical          | 47             | 3          |


## Step 4: Build an attack timeline and assess impact

To understand the attack progression, we need to build a timeline of events across multiple indices. This helps us correlate actions and identify the attacker's dwell time.
```esql
FROM windows-security-logs, process-logs, network-logs 
| LOOKUP JOIN asset-inventory ON host.name
| LOOKUP JOIN user-context ON user.name
| WHERE user.name == "jsmith" OR user.name == "admin"
| EVAL event_type = CASE(
    event.code IS NOT NULL, "Authentication",
    process.name IS NOT NULL, "Process Execution",
    destination.ip IS NOT NULL, "Network Activity",
    "Unknown") 
| EVAL dest_ip = TO_STRING(destination.ip)
| EVAL attack_stage = CASE(
    process.parent.name LIKE "*word*", "Initial Compromise",
    process.name IN ("net.exe", "nltest.exe"), "Reconnaissance", 
    event.code == "4624" AND logon.type == "3", "Lateral Movement",
    process.name IN ("sqlcmd.exe", "ntdsutil.exe"), "Data Access",
    dest_ip NOT LIKE "10.*", "Exfiltration",
    "Other") 
| SORT @timestamp ASC 
| KEEP @timestamp, event_type, attack_stage, host.name, asset.criticality, user.name, process.name, destination.ip
| LIMIT 1000
```

The response provides a chronological timeline of events, showing the attacker's actions and the impact on the organization.
<dropdown title="View response">
  | @timestamp               | event_type        | attack_stage       | host.name | asset.criticality | user.name | process.name   | destination.ip |
  |--------------------------|-------------------|--------------------|-----------|-------------------|-----------|----------------|----------------|
  | 2025-05-20T02:30:00.000Z | Authentication    | Lateral Movement   | DC-001    | critical          | admin     | null           | null           |
  | 2025-05-20T02:35:00.000Z | Process Execution | Data Access        | DC-001    | critical          | admin     | ntdsutil.exe   | null           |
  | 2025-05-20T08:15:00.000Z | Authentication    | Other              | WS-001    | medium            | jsmith    | null           | null           |
  | 2025-05-20T08:17:00.000Z | Authentication    | Lateral Movement   | WS-001    | medium            | jsmith    | null           | null           |
  | 2025-05-20T08:20:00.000Z | Process Execution | Initial Compromise | WS-001    | medium            | jsmith    | powershell.exe | null           |
  | 2025-05-20T09:30:00.000Z | Authentication    | Lateral Movement   | SRV-001   | high              | jsmith    | null           | null           |
  | 2025-05-20T09:35:00.000Z | Process Execution | Reconnaissance     | SRV-001   | high              | jsmith    | net.exe        | null           |
  | 2025-05-20T10:45:00.000Z | Authentication    | Lateral Movement   | DB-001    | critical          | jsmith    | null           | null           |
  | 2025-05-20T10:50:00.000Z | Process Execution | Data Access        | DB-001    | critical          | jsmith    | sqlcmd.exe     | null           |
  | 2025-05-20T12:15:00.000Z | Process Execution | Other              | WS-001    | medium            | jsmith    | schtasks.exe   | null           |
  | 2025-05-20T12:30:00.000Z | Process Execution | Other              | SRV-001   | high              | jsmith    | schtasks.exe   | null           |
  | 2025-05-20T13:15:00.000Z | Process Execution | Other              | DB-001    | critical          | jsmith    | sc.exe         | null           |
  | 2025-05-20T13:20:00.000Z | Process Execution | Other              | SRV-001   | high              | jsmith    | sc.exe         | null           |
  | 2025-05-20T13:25:00.000Z | Process Execution | Other              | DC-001    | critical          | admin     | sc.exe         | null           |
</dropdown>


## Step 5: Hunt for unusual interpreter usage

This query demonstrates how ES|QL's [COUNT_DISTINCT](https://www.elastic.co/docs/reference/query-languages/esql/functions-operators/aggregation-functions#esql-count_distinct) function and conditional [`CASE`](https://www.elastic.co/docs/reference/query-languages/esql/functions-operators/conditional-functions-and-expressions#esql-case) statements can be used to baseline interpreter usage patterns across users and departments, using aggregation functions to identify anomalous script execution that might indicate compromised accounts or insider threats.
```esql
FROM process-logs
| WHERE process.name IN ("powershell.exe", "cmd.exe", "net.exe", "sqlcmd.exe", "schtasks.exe", "sc.exe") 
| LOOKUP JOIN asset-inventory ON host.name
| LOOKUP JOIN user-context ON user.name
| STATS executions = COUNT(*),
        unique_hosts = COUNT_DISTINCT(host.name),
        unique_commands = COUNT_DISTINCT(process.name) 
BY user.name, user.department
| WHERE executions > 1
| EVAL usage_pattern = CASE(
    executions > 5, "High Usage",
    executions > 3, "Moderate Usage", 
    "Low Usage"
  ) 
| SORT executions DESC
| LIMIT 1000
```

**Response**
The response shows the number of executions, unique hosts, and usage patterns for each user and department.

| executions | unique_hosts | unique_commands | user.name | user.department | usage_pattern |
|------------|--------------|-----------------|-----------|-----------------|---------------|
| 7          | 3            | 5               | jsmith    | finance         | High Usage    |


## Step 6: Hunt for persistence mechanisms

This query showcases how [`DATE_TRUNC`](https://www.elastic.co/docs/reference/query-languages/esql/functions-operators/date-time-functions#esql-date_trunc) enables temporal analysis of persistence mechanisms, using time bucketing and [`COUNT_DISTINCT`](https://www.elastic.co/docs/reference/query-languages/esql/functions-operators/aggregation-functions#esql-count_distinct) to identify suspicious patterns like rapid-fire task creation or persistence establishment across multiple time windows.
```esql
FROM process-logs
| WHERE process.name == "schtasks.exe" AND process.command_line:"/create" 
| LOOKUP JOIN asset-inventory ON host.name
| LOOKUP JOIN user-context ON user.name
| EVAL time_bucket = DATE_TRUNC(1 hour, @timestamp) 
| STATS task_creations = COUNT(*),
        creation_hours = COUNT_DISTINCT(time_bucket) 
BY user.name, host.name, asset.criticality
| WHERE task_creations > 0
| EVAL persistence_pattern = CASE(
    creation_hours > 1, "Multiple Hours",
    task_creations > 1, "Burst Creation",
    "Single Task"
  ) 
| SORT task_creations DESC
| LIMIT 1000
```

**Response**
The response shows the number of task creations, creation hours, and persistence patterns for each user and host.

| task_creations | creation_hours | user.name | host.name | asset.criticality | persistence_pattern |
|----------------|----------------|-----------|-----------|-------------------|---------------------|
| 1              | 1              | jsmith    | WS-001    | medium            | Single Task         |
| 1              | 1              | jsmith    | SRV-001   | high              | Single Task         |


## Additional resources

- Explore a curated collection of threat hunting [queries](https://github.com/elastic/detection-rules/tree/main/hunting) in the `elastic/detection-rules` GitHub repo.
  - The corresponding [blog](https://www.elastic.co/security-labs/elevate-your-threat-hunting) provides more information about how to use them in your threat hunting workflows.
- Explore more threat hunting examples in the following blogs:
  - [Detect and prevent data exfiltration with Elastic Security](https://www.elastic.co/blog/security-exfiltration)
- [Detecting command and scripting interpreter techniques](https://www.elastic.co/blog/detecting-command-scripting-interpreter)
- [Detecting credential dumping with Elastic Security](https://www.elastic.co/blog/elastic-security-detecting-credential-dumping)
- [Detecting covert data exfiltration techniques](https://www.elastic.co/blog/elastic-security-detecting-covert-data-exfiltration)

<tip>
  To learn where you can use ES|QL in Elastic Security contexts, refer to [the overview](/docs/solutions/security/esql-for-security#documentation).
</tip>