LLM-Generated Detections Are Failing: Here’s Why Your AI SOC Strategy Needs a Hard Reset + Video

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Introduction:

The promise of generative AI in detection engineering is seductive: feed threat intelligence into an LLM and receive ready-to-deploy SIEM rules. But as security professionals now observe, this approach often yields “vanilla detection code, often below average,” because LLMs lack context about your unique data sources, infrastructure, and risk appetite. The real value lies not in having AI create detections from scratch, but in having it automate the deployment, testing, and maintenance of detections that you already validate.

Learning Objectives:

  • Distinguish between effective and ineffective use of LLMs for detection engineering.
  • Implement a workflow where AI assists with deployment and maintenance, not greenfield rule generation.
  • Apply continuous scoring and automated testing to prevent detection drift and broken rules.

You Should Know:

  1. Why “Build Me a Detection from CTI” Produces Poor Results
    Most LLMs generate detections based on public patterns, vendor templates, and community queries—not your actual log formats, field names, or infrastructure quirks. The result is a rule that may be syntactically correct but operationally useless or even dangerous (e.g., false positives flooding your SOC).

Step‑by‑step to test a generic LLM detection:

  1. Generate a detection using an LLM with a prompt like:
    `“Create a Sigma rule to detect Log4j exploitation in Windows event logs.”`

2. Export sample logs from your environment (sanitized):

  • Windows: `Get-WinEvent -LogName Application -MaxEvents 50 | Export-Csv -Path sample_logs.csv`
  • Linux: `journalctl -n 100 > sample_logs.txt`
    3. Run the rule against your logs using a Sigma CLI or SIEM test framework:

`sigmac -t splunk my_rule.yml –backend-option log_source=sample_logs.csv`

  1. Observe mismatches – missing fields, wrong event IDs, or no matches at all.

Most teams find the rule hits 0% of actual malicious activity while alerting on benign software updates. This is the “13% of SIEM rules broken” phenomenon at scale.

  1. The Right Approach: Agent-Assisted Deployment of Your Own Detection Code
    Instead of asking AI to build logic, feed it your validated detection code (e.g., a KQL query, Sigma rule, or Python script) and let it handle the “boring things”: deployment, formatting, scheduling, and documentation.

Step‑by‑step for agent‑assisted deployment (Linux / Windows):

  1. Write your detection in a standard format (e.g., Sigma). Save as detect_log4j.yml: 
    title: Log4j JNDI Exploit Attempt
logsource:
product: windows
service: sysmon
detection:
selection:
EventID: 22
Image|contains: 'java'
CommandLine|contains: 'jndi:ldap'
condition: selection

2. Craft an LLM prompt for deployment:

`“Convert this Sigma rule to a Splunk search, schedule it every 15 minutes, and suppress duplicate alerts for 1 hour.”`
3. Automate conversion with a script using the LLM API (Python example):

import openai
sigma_rule = open('detect_log4j.yml').read()
response = openai.ChatCompletion.create(
model="gpt-4",
messages=[{"role": "user", "content": f"Convert to Splunk SPL:\n{sigma_rule}"}]
)
splunk_query = response.choices[bash].message.content
 Deploy via Splunk REST API
import requests
requests.post('https://splunk:8089/services/saved/searches', data={'search': splunk_query})

4. Verify deployment – check your SIEM for the new scheduled search.

3. Continuous Detection Maintenance with AI Scoring

Detections drift as log sources change, applications update, and attacker techniques evolve. AI should re-score detection quality weekly and flag anomalies.

Step‑by‑step to implement automated detection scoring:

  1. Collect telemetry on each detection daily: number of hits, false positive rate, unique hosts triggered.
  2. Feed metrics into a scoring model (simple Python): 
    def detection_score(hits, fps, total_events):
coverage = hits / total_events if total_events else 0
precision = hits / (hits + fps) if (hits + fps) else 0
return (coverage  0.4) + (precision  0.6)  weighted score
  3. Use LLM to analyze score drops – prompt:
    `“Detection score fell from 87 to 40. Log field ‘CommandLine’ is now ‘ProcessCommandLine’. Suggest updated rule.”`
    4. Automatically generate a pull request with the suggested change, requiring SOC analyst approval.

This turns AI from a rule generator into a rule gardener – pruning and repotting as needed.

  1. Practical Lab: Building a Detection Pipeline with CI/CD
    Treat detection code like application code – version control, testing, and automated deployment.

Step‑by‑step for a Linux-based detection pipeline:

1. Initialize a Git repo for your rules:

mkdir detection_repo && cd detection_repo
git init
mkdir sigma splunk kql

2. Write a unit test for a Sigma rule using pysigma:

from sigma.rule import SigmaRule
rule = SigmaRule.from_yaml('sigma/log4j.yml')
sample_event = {'EventID': 22, 'Image': 'C:\java.exe', 'CommandLine': 'jndi:ldap://evil'}
assert rule.match(sample_event) is True

3. Set up a GitHub Action to test rules on push:

name: Test Detections
on: [bash]
jobs:
test:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v2
- run: pip install pysigma
- run: python -m pytest tests/

4. Add a deployment step that calls your SIEM’s API (e.g., Splunk, Sentinel) using an LLM to format the query correctly.
5. Monitor detection health via a dashboard (Grafana + Prometheus) that pulls scores from your scoring model.

  1. Linux/Windows Commands for Detection Testing & Log Analysis
    Use these commands to validate detections before handing them to an AI agent.

Linux (journald, auditd, grep):

  • Extract last 24h of auth logs: `journalctl –since “24 hours ago” -u sshd > auth_samples.log`
    – Search for brute-force patterns: `grep “Failed password” auth_samples.log | awk ‘{print $11}’ | sort | uniq -c`
    – Real‑time sysmon‑like monitoring with auditd:

    auditctl -w /usr/bin/java -p x -k java_exec
ausearch -k java_exec --format raw | grep jndi

Windows (PowerShell, Get-WinEvent, Sysmon):

  • Export Sysmon event 22 (DNS queries) to CSV: 
    Get-WinEvent -FilterHashtable @{LogName='Microsoft-Windows-Sysmon/Operational'; ID=22} | Export-Csv dns_queries.csv
  • Test a detection query against live logs: 
    $events = Get-WinEvent -LogName 'Security' | Where-Object { $_.Message -match 'jndi:ldap' }
if ($events.Count -gt 0) { Write-Host "Detection would trigger" }
  • Schedule a detection test every hour: 
    $action = New-ScheduledTaskAction -Execute 'powershell.exe' -Argument '-File C:\detections\test_log4j.ps1'
Register-ScheduledTask -TaskName "TestDetection" -Action $action -Trigger (New-ScheduledTaskTrigger -Daily -At "00:00")
  1. API Security & Cloud Hardening for Detection Pipelines
    When you let AI agents interact with your SIEM, cloud logs, or ticketing systems, secure those APIs.

Step‑by‑step to harden AI‑agent API access:

  1. Use short‑lived tokens (e.g., AWS STS, Azure Managed Identity) instead of static keys.
  2. Implement least privilege – the AI agent should only be able to read existing detection templates and write to a staging area, not production.
  3. Audit all agent actions – send every API call to a separate log bucket: 
    On Linux, proxy agent calls via a script that logs to syslog
function call_siem_api() {
logger "AI agent called SIEM API with payload: $1"
curl -X POST --header "Authorization: Bearer $TOKEN" --data "$1" https://siem.internal/api/rules
}
  4. Rate limit – use `ulimit` or a gateway like KrakenD to prevent the agent from flooding your SIEM.

What Undercode Say:

  • AI is a deployment and maintenance assistant, not a detection author. The best results come from feeding it your own proven logic.
  • Continuous scoring and version control prevent “rule rot.” Without automated health checks, 13% of your rules will silently break within six months.

The industry is waking up to the fact that LLMs lack the operational context to build high‑fidelity detections from threat intel alone. Organizations that succeed will treat detection code as software – with CI/CD, unit tests, and AI‑assisted maintenance – while those chasing “build me a detection” buttons will drown in false positives and broken rules. The future of AI in the SOC is not generation, but orchestration.

Prediction:

Within 18 months, SIEM vendors will pivot from “AI rule generators” to “AI detection maintainers” – products that continuously re‑score, test, and suggest fixes for existing rules. This shift will reduce false positive rates by over 40% in mature SOCs. However, organizations that fail to invest in detection engineering fundamentals (logging quality, data normalization, version control) will see little benefit, because AI cannot fix garbage input. The gap between “LLM hype” and “operational reality” will widen, creating a new market for detection pipeline tooling and training courses focused on hybrid human‑AI workflows.

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