performing-log-source-onboarding-in-siem

mukul975/Anthropic-Cybersecurity-Skills · updated May 25, 2026

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$npx skills install mukul975/Anthropic-Cybersecurity-Skills/performing-log-source-onboarding-in-siem
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summary

Perform structured log source onboarding into SIEM platforms by configuring collectors, parsers, normalization, and validation for complete security visibility.

skill.md
name
performing-log-source-onboarding-in-siem
description
Perform structured log source onboarding into SIEM platforms by configuring collectors, parsers, normalization, and validation for complete security visibility.
domain
cybersecurity
subdomain
soc-operations
tags
- siem - log-onboarding - log-management - data-ingestion - parsing - normalization - soc
version
'1.0'
author
mahipal
license
Apache-2.0
nist_csf
- DE.CM-01 - DE.AE-02 - RS.MA-01 - DE.AE-06

Performing Log Source Onboarding in SIEM

Overview

Log source onboarding is the systematic process of integrating new data sources into a SIEM platform to enable security monitoring and detection. Proper onboarding requires planning data sources, configuring collection agents, building parsers, normalizing fields to a common schema, and validating data quality. According to the UK NCSC, onboarding should prioritize log sources that provide the highest security value relative to their ingestion cost.

When to Use

  • When conducting security assessments that involve performing log source onboarding in siem
  • When following incident response procedures for related security events
  • When performing scheduled security testing or auditing activities
  • When validating security controls through hands-on testing

Prerequisites

  • SIEM platform deployed (Splunk, Elastic, Sentinel, QRadar, or similar)
  • Network access from source systems to SIEM collectors
  • Administrative access on source systems for agent installation
  • Common Information Model (CIM) or equivalent schema documentation
  • Change management approval for production system modifications

Log Source Priority Framework

Tier 1 - Critical (Onboard First)

SourceLog TypeSecurity Value
Active DirectorySecurity Event LogsAuthentication, privilege escalation
FirewallsTraffic logsNetwork access, C2 detection
EDR/AVEndpoint alertsMalware, process execution
VPN/Remote AccessConnection logsUnauthorized access
DNS ServersQuery logsC2 beaconing, data exfiltration
Email GatewayEmail security logsPhishing, BEC

Tier 2 - High Priority

SourceLog TypeSecurity Value
Web ProxyHTTP/HTTPS logsWeb-based attacks, data exfiltration
Cloud platforms (AWS/Azure/GCP)Audit logsCloud security posture
Database serversAudit/query logsData access, SQL injection
DHCP/IPAMAddress allocationAsset tracking
File serversAccess logsData access monitoring

Tier 3 - Standard

SourceLog TypeSecurity Value
Application serversApp logsApplication-level attacks
Print serversPrint logsData loss prevention
Badge/physical accessAccess logsPhysical security correlation
Network devices (switches/routers)SyslogNetwork anomalies

Onboarding Process

Step 1: Discovery and Assessment

1. Identify the log source:
   - System type and version
   - Log format (syslog, CEF, JSON, Windows Events, etc.)
   - Log volume estimate (EPS - events per second)
   - Network location and firewall requirements

2. Assess security value:
   - What threats can this source help detect?
   - Which MITRE ATT&CK techniques does it cover?
   - Is there an existing SIEM parser?

3. Estimate ingestion cost:
   - Daily volume in GB
   - License impact (per-GB or per-EPS pricing)
   - Storage retention requirements

Step 2: Configure Log Collection

Syslog-Based Collection (Firewalls, Network Devices)

# rsyslog configuration for receiving syslog
# /etc/rsyslog.d/10-siem-collection.conf

# UDP reception
module(load="imudp")
input(type="imudp" port="514" ruleset="siem_forwarding")

# TCP reception
module(load="imtcp")
input(type="imtcp" port="514" ruleset="siem_forwarding")

# TLS reception
module(load="imtcp" StreamDriver.AuthMode="x509/name"
       StreamDriver.Mode="1" StreamDriver.Name="gtls")
input(type="imtcp" port="6514" ruleset="siem_forwarding")

ruleset(name="siem_forwarding") {
    # Forward to SIEM
    action(type="omfwd" target="siem.company.com" port="9514"
           protocol="tcp" queue.type="LinkedList"
           queue.filename="siem_fwd" queue.maxdiskspace="1g"
           queue.saveonshutdown="on" action.resumeRetryCount="-1")
}

Windows Event Log Collection (Splunk Universal Forwarder)

# inputs.conf on Splunk Universal Forwarder
[WinEventLog://Security]
disabled = 0
index = wineventlog
sourcetype = WinEventLog:Security
evt_resolve_ad_obj = 1
checkpointInterval = 5

[WinEventLog://System]
disabled = 0
index = wineventlog
sourcetype = WinEventLog:System

[WinEventLog://Microsoft-Windows-Sysmon/Operational]
disabled = 0
index = wineventlog
sourcetype = XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
renderXml = true

[WinEventLog://Microsoft-Windows-PowerShell/Operational]
disabled = 0
index = wineventlog
sourcetype = XmlWinEventLog:Microsoft-Windows-PowerShell/Operational

Cloud Log Collection (AWS CloudTrail)

{
  "AWSTemplateFormatVersion": "2010-09-09",
  "Resources": {
    "CloudTrailToSIEM": {
      "Type": "AWS::CloudTrail::Trail",
      "Properties": {
        "TrailName": "siem-cloudtrail",
        "S3BucketName": "company-cloudtrail-logs",
        "IsLogging": true,
        "IsMultiRegionTrail": true,
        "IncludeGlobalServiceEvents": true,
        "EnableLogFileValidation": true,
        "EventSelectors": [
          {
            "ReadWriteType": "All",
            "IncludeManagementEvents": true,
            "DataResources": [
              {
                "Type": "AWS::S3::Object",
                "Values": ["arn:aws:s3"]
              }
            ]
          }
        ]
      }
    }
  }
}

Step 3: Parse and Normalize

Custom Parser Example (Splunk props.conf/transforms.conf)

# props.conf
[custom:firewall:logs]
SHOULD_LINEMERGE = false
LINE_BREAKER = ([\r\n]+)
TIME_PREFIX = ^
TIME_FORMAT = %Y-%m-%dT%H:%M:%S%z
MAX_TIMESTAMP_LOOKAHEAD = 30
TRANSFORMS-firewall = firewall_extract_fields
FIELDALIAS-src = src_addr AS src_ip
FIELDALIAS-dst = dst_addr AS dest_ip
EVAL-action = case(fw_action=="allow", "allowed", fw_action=="deny", "blocked", true(), "unknown")
EVAL-vendor_product = "Custom Firewall"
LOOKUP-geo = geo_ip_lookup ip AS dest_ip OUTPUT country, city, latitude, longitude

# transforms.conf
[firewall_extract_fields]
REGEX = ^(\S+)\s+(\S+)\s+action=(\w+)\s+src=(\S+):(\d+)\s+dst=(\S+):(\d+)\s+proto=(\w+)\s+bytes=(\d+)
FORMAT = timestamp::$1 hostname::$2 fw_action::$3 src_addr::$4 src_port::$5 dst_addr::$6 dst_port::$7 protocol::$8 bytes::$9

CIM Field Mapping

Raw FieldCIM FieldData Model
src_addrsrc_ipNetwork_Traffic
dst_addrdest_ipNetwork_Traffic
dst_portdest_portNetwork_Traffic
fw_actionactionNetwork_Traffic
bytes_sent + bytes_recvbytesNetwork_Traffic
user_nameuserAuthentication
login_resultactionAuthentication
process_pathprocessEndpoint

Step 4: Validate Data Quality

# Verify events are arriving
index=new_source earliest=-1h
| stats count by sourcetype, host, source

# Check field extraction quality
index=new_source earliest=-1h
| stats count(src_ip) as has_src count(dest_ip) as has_dest count(action) as has_action count by sourcetype
| eval src_coverage=round(has_src/count*100,1)
| eval dest_coverage=round(has_dest/count*100,1)
| eval action_coverage=round(has_action/count*100,1)

# Verify CIM compliance
| datamodel Network_Traffic search
| search sourcetype=new_sourcetype
| stats count by source, sourcetype

# Check for timestamp parsing issues
index=new_source earliest=-1h
| eval time_diff=abs(_time - _indextime)
| stats avg(time_diff) as avg_lag max(time_diff) as max_lag by host
| where avg_lag > 300

Step 5: Enable Detection Coverage

# Verify existing correlation searches work with new source
index=new_source sourcetype=new_sourcetype
| tstats count from datamodel=Authentication by _time span=1h
| timechart span=1h count

# Create source-specific detection rule
[New Source - Authentication Anomaly]
search = index=new_source sourcetype=new_sourcetype action=failure \
| stats count by src_ip, user \
| where count > 10

Onboarding Checklist

  • Log source assessed and approved
  • Network connectivity verified
  • Collection agent/method configured
  • Log forwarding confirmed
  • Parser/field extraction configured
  • CIM compliance validated
  • Data model acceleration enabled
  • Volume within license budget
  • Retention policy configured
  • Detection rules enabled/created
  • Dashboard updated
  • Documentation completed
  • SOC team notified

References

how to use performing-log-source-onboarding-in-siem

How to use performing-log-source-onboarding-in-siem on Cursor

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1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add performing-log-source-onboarding-in-siem
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills install mukul975/Anthropic-Cybersecurity-Skills/performing-log-source-onboarding-in-siem

The skills CLI fetches performing-log-source-onboarding-in-siem from GitHub repository mukul975/Anthropic-Cybersecurity-Skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/performing-log-source-onboarding-in-siem

Reload or restart Cursor to activate performing-log-source-onboarding-in-siem. Access the skill through slash commands (e.g., /performing-log-source-onboarding-in-siem) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

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Use Cases

Task Automation & Efficiency

Automate repetitive workflows and reduce manual effort

Example

Generate reports, summarize documents, draft communications

Save 3-5 hours per week on routine tasks

Knowledge Enhancement

Learn new skills, understand complex topics, get expert guidance

Example

Explain concepts, provide examples, suggest learning resources

Accelerate learning and skill development by 2x

Quality Improvement

Enhance output quality through reviews, suggestions, and refinements

Example

Review drafts, suggest improvements, catch errors

Improve work quality by 30-40% with less effort

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client with skill support
  • Clear understanding of task or problem to solve
  • Willingness to iterate and refine outputs

Time Estimate

15-45 minutes depending on use case complexity

Installation Steps

  1. 1.Install skill using provided installation command
  2. 2.Test with simple use case relevant to your work
  3. 3.Evaluate output quality and relevance
  4. 4.Iterate on prompts to improve results
  5. 5.Integrate into regular workflow if valuable

Common Pitfalls

  • Expecting perfect results without iteration
  • Not providing enough context in prompts
  • Using skill for tasks outside its intended scope
  • Accepting outputs without review and validation

Best Practices

✓ Do

  • +Start with clear, specific prompts
  • +Provide relevant context and constraints
  • +Review and refine all outputs before using
  • +Iterate to improve output quality
  • +Document successful prompt patterns

✗ Don't

  • Don't use without understanding skill limitations
  • Don't skip validation of outputs
  • Don't share sensitive information in prompts
  • Don't expect skill to replace human judgment

💡 Pro Tips

  • Be specific about desired format and style
  • Ask for multiple options to choose from
  • Request explanations to understand reasoning
  • Combine AI efficiency with human expertise

When to Use This

✓ Use When

Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.

✗ Avoid When

Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.

Learning Path

  1. 1Familiarize yourself with skill capabilities and limitations
  2. 2Start with low-risk, non-critical tasks
  3. 3Progress to more complex and valuable use cases
  4. 4Build expertise through regular use and experimentation

Discussion

Product Hunt–style comments (not star reviews)
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general reviews

Ratings

4.838 reviews
  • Valentina Li· Dec 12, 2024

    Solid pick for teams standardizing on skills: performing-log-source-onboarding-in-siem is focused, and the summary matches what you get after install.

  • Diego Torres· Nov 27, 2024

    Useful defaults in performing-log-source-onboarding-in-siem — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Aditi Lopez· Nov 3, 2024

    performing-log-source-onboarding-in-siem has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Diego Flores· Oct 22, 2024

    Keeps context tight: performing-log-source-onboarding-in-siem is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Valentina Huang· Oct 18, 2024

    performing-log-source-onboarding-in-siem is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Rahul Santra· Sep 25, 2024

    Keeps context tight: performing-log-source-onboarding-in-siem is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Lucas Jackson· Sep 25, 2024

    performing-log-source-onboarding-in-siem reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Ava Robinson· Sep 1, 2024

    I recommend performing-log-source-onboarding-in-siem for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Valentina Anderson· Sep 1, 2024

    performing-log-source-onboarding-in-siem has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • William Garcia· Aug 20, 2024

    performing-log-source-onboarding-in-siem reduced setup friction for our internal harness; good balance of opinion and flexibility.

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