building-detection-rule-with-splunk-spl
Lets your agent create rules to find security threats. It uses a special language called SPL to analyze data and identify suspicious activities.
What it does
This feature helps you create security threat detection rules using Splunk's query language (SPL). It allows you to identify suspicious activities in your systems to protect your network.
When to use it
- ✓When you want to find unusual activities in your systems.
- ✓When setting up a security system to alert about dangers.
- ✓To improve the detection of cyber threats.
Example of what to ask your AI
“Create a rule to detect mass failed login attempts from a single computer.”
Want more? Open the sections below.
How to use
The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.
Skill link
Ready-to-paste message
How it works
It works by writing specific commands (SPL) that analyze your system's event logs. These commands look for patterns that indicate an attack or suspicious behavior.
Installation
Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.
In the terminal (Claude Code, Codex, Cursor), in your project folder:
npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill building-detection-rule-with-splunk-splOr download just the file:
curl -L https://raw.githubusercontent.com/mukul975/Anthropic-Cybersecurity-Skills/main/skills/building-detection-rule-with-splunk-spl/SKILL.md -o SKILL.mdTechnical content (original SKILL.md)
Original text by the author, straight from GitHub.
Building Detection Rules with Splunk SPL
Overview
Splunk Search Processing Language (SPL) is the primary query language used in Splunk Enterprise Security for building correlation searches that detect suspicious events and patterns. A well-crafted detection rule aggregates, correlates, and enriches security events to generate actionable notable events for SOC analysts. Enterprise SIEMs on average cover only 21% of MITRE ATT&CK techniques, making skilled SPL rule writing essential for closing detection gaps.
When to Use
- When deploying or configuring building detection rule with splunk spl capabilities in your environment
- When establishing security controls aligned to compliance requirements
- When building or improving security architecture for this domain
- When conducting security assessments that require this implementation
Prerequisites
- Splunk Enterprise Security (ES) deployed and configured
- Access to Splunk Search & Reporting app with appropriate roles
- Understanding of Common Information Model (CIM) data models
- Familiarity with MITRE ATT&CK framework techniques
- Knowledge of the organization's log sources and data flows
Core SPL Detection Rule Patterns
1. Threshold-Based Detection
Detects events exceeding a defined count within a time window.
index=wineventlog sourcetype=WinEventLog:Security EventCode=4625
| stats count as failed_logins dc(TargetUserName) as unique_users by src_ip
| where failed_logins > 10 AND unique_users > 3
| eval severity="high"
| eval description="Brute force attack detected from ".src_ip." with ".failed_logins." failed logins across ".unique_users." accounts"
2. Sequence-Based Detection (Failed Login Followed by Success)
Correlates a sequence of events indicating a successful brute force attack.
index=wineventlog sourcetype=WinEventLog:Security (EventCode=4625 OR EventCode=4624)
| eval login_status=case(EventCode=4625, "failure", EventCode=4624, "success")
| stats count(eval(login_status="failure")) as failures count(eval(login_status="success")) as successes latest(_time) as last_event by src_ip, TargetUserName
| where failures > 5 AND successes > 0
| eval description="Account ".TargetUserName." compromised via brute force from ".src_ip
| eval urgency="critical"
3. Anomaly Detection with Baseline Comparison
Compares current activity against a baseline period to detect spikes.
index=proxy sourcetype=squid
| bin _time span=1h
| stats count as current_count by src_ip, _time
| join src_ip type=left [
search index=proxy sourcetype=squid earliest=-7d@d latest=-1d@d
| stats avg(count) as avg_count stdev(count) as stdev_count by src_ip
]
| eval threshold=avg_count + (3 * stdev_count)
| where current_count > threshold
| eval deviation=round((current_count - avg_count) / stdev_count, 2)
| eval description="Anomalous web traffic from ".src_ip." - ".deviation." standard deviations above baseline"
4. Lateral Movement Detection
Identifies potential lateral movement using Windows logon events.
index=wineventlog sourcetype=WinEventLog:Security EventCode=4624 Logon_Type=3
| where NOT match(TargetUserName, ".*\$$")
| stats dc(dest) as unique_hosts values(dest) as hosts by src_ip, TargetUserName
| where unique_hosts > 5
| eval severity=case(unique_hosts > 20, "critical", unique_hosts > 10, "high", true(), "medium")
| eval description=TargetUserName." accessed ".unique_hosts." unique hosts from ".src_ip." via network logon"
5. Data Exfiltration Detection
Monitors for large outbound data transfers.
index=firewall sourcetype=pan:traffic action=allowed direction=outbound
| stats sum(bytes_out) as total_bytes_out dc(dest_ip) as unique_destinations by src_ip, user
| eval total_mb=round(total_bytes_out/1048576, 2)
| where total_mb > 500 OR unique_destinations > 50
| lookup asset_lookup ip as src_ip OUTPUT asset_category, asset_owner
| eval severity=case(total_mb > 2000, "critical", total_mb > 1000, "high", true(), "medium")
| eval description=user." transferred ".total_mb."MB to ".unique_destinations." unique destinations"
6. PowerShell Suspicious Execution Detection
Detects encoded or obfuscated PowerShell commands.
index=wineventlog sourcetype=WinEventLog:Security EventCode=4104
| where match(ScriptBlockText, "(?i)(encodedcommand|invoke-expression|iex|downloadstring|frombase64string|net\.webclient|invoke-webrequest|bitstransfer|invoke-mimikatz|invoke-shellcode)")
…
FAQ
What is Splunk SPL?
SPL (Search Processing Language) is the language you use to ask questions of data in Splunk, like searching for specific events or patterns.
What are 'detection rules'?
These are instructions that tell the security system to watch out for certain types of activities that could be dangerous.
Do I need to be a security expert to use this?
It's helpful to have some understanding of security and how systems work, but the goal is to make creating these rules more accessible.
Details
Original description by the author
Build effective detection rules using Splunk Search Processing Language (SPL) correlation searches to identify security threats in SOC environments.
Popularity
- GitHub stars
- 35k
- Ranking position
- #5532
- License
- Apache-2.0
- Updated
- 8/31/2026
Comments
- …
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