SIEM Ate SOAR, Now AI SOC Is The Main Dish: Why Agent Builders Will Redefine SecOps + Video

Introduction:

Security Information and Event Management (SIEM) platforms have progressively absorbed Security Orchestration, Automation, and Response (SOAR) capabilities, either through acquisitions or native builds, creating a unified but often fragmented automation layer. The next frontier is AI-driven Security Operations Centers (SOC), where generative AI agents promise to replace manual triage and response, but the real disruption lies in native “Agent Builder” frameworks that could either break vendor lock-in or reinforce it.

Learning Objectives:

Understand how SIEM vendors have integrated SOAR (acquisition vs. native) and the resulting automation tax.
Evaluate the emerging AI SOC landscape and its dependency on centralized data ingestion.
Learn to build and deploy custom security automation agents using open-source tools and API-first architectures.

You Should Know:

The SOAR Entree: From Acquisition to Native Build – A Step-by-Step Integration Guide

Most large SIEMs now ship with built-in SOAR, but the user experience varies dramatically. Below is a practical guide to integrating a standalone SOAR (like TheHive or Shuffle) with a SIEM using API calls and webhooks – mirroring how Elastic Workflows or Datadog’s orchestration works under the hood.

Step-by-step: Bridge SIEM Alerts to a SOAR Webhook

Extract a SIEM alert payload (example using Elastic’s API):

curl -X GET "https://your-elastic-cluster:9200/.siem-signals-/_search" -H "kbn-xsrf: true" -u username:password -d '{"query":{"match_all":{}}}' | jq '.hits.hits[bash]._source'

Create a SOAR webhook receiver (using Shuffle open-source):
```
webhook_receiver.py
from flask import Flask, request
import requests</li>
</ol>

app = Flask(name)

@app.route('/webhook', methods=['POST'])
def handle_alert():
alert = request.json
 Forward to case management system
requests.post('https://your-thehive/case', json=alert)
return 'OK', 200

if name == 'main':
app.run(host='0.0.0.0', port=5000)
```
1. Configure SIEM alert action (e.g., Elastic Watcher or Splunk alert) to POST to `http://your-soar:5000/webhook`.
 This eliminates the “SOAR automation tax” by using lightweight, vendor-agnostic glue.
4. Test with a sample alert:
```
curl -X POST http://localhost:5000/webhook -H "Content-Type: application/json" -d '{"alert_id":"123","severity":"high"}'
```
Why this matters: Native SIEM SOAR often locks you into the vendor’s ecosystem. The above approach keeps automation portable.
1. AI SOC Main Dish: Implementing AI-Driven Security Analytics Without Vendor Lock-in
AI SOC features (enrichment, summarization, investigation) are being baked into SIEMs, but you can build your own AI layer using LLMs and log pipelines.

Step-by-step: Add AI Enrichment to SIEM Logs
1. Set up a local LLM (Ollama + Mistral) for lightweight enrichment:
```
curl -fsSL https://ollama.com/install.sh | sh
ollama pull mistral
```
2. Create a Python script that reads a suspicious log line and asks the LLM for context:
```
import requests
import json</li>
</ol>

def enrich_with_ai(log_line):
response = requests.post('http://localhost:11434/api/generate', 
json={"model": "mistral", "prompt": f"Explain this security alert: {log_line}", "stream": False})
return response.json()['response']

sample_log = "Failed login from 5.5.5.5 on root 10 times in 1 minute"
print(enrich_with_ai(sample_log))
```
 1. Integrate with SIEM – use a custom parser (e.g., Logstash filter) to call this script on high-severity events.
 This gives you AI SOC functionality without paying per-ingestion fees.
 Linux command to monitor SIEM index and trigger AI enrichment:
```
 Real-time tail of SIEM logs (assuming JSON logs)
tail -f /var/log/siem/alerts.log | while read line; do python3 enrich.py "$line"; done
```
 1. Agent Builder Dessert: Building a Custom Security Agent Framework
 Vendors like CrowdStrike and Databricks now offer agent builders – but they often only work inside their stack. Below is a vendor-agnostic agent that can query multiple security tools via APIs.
 
 Step-by-step: Build a Simple Agent that Queries SIEM, EDR, and CloudTrail
 1. Define the agent’s tools (Python + LangChain style):
```
from langchain.tools import tool</li>
</ol>

@tool
def query_siem(query: str) -> str:
 Replace with actual SIEM API call
return f"SIEM results for {query}"

@tool
def query_edr(hostname: str) -> str:
 Example: CrowdStrike Falcon API
return f"EDR data for {hostname}"

@tool
def query_cloudtrail(account_id: str) -> str:
 AWS CLI via subprocess
import subprocess
result = subprocess.run(['aws', 'cloudtrail', 'lookup-events'], capture_output=True)
return result.stdout.decode()
```
 2. Create a reasoning loop (pseudo-agent):
```
def agent_decision(alert):
if "malware" in alert:
return query_edr(alert['host'])
elif "anomalous API call" in alert:
return query_cloudtrail(alert['account'])
else:
return query_siem(alert['raw'])
```
 3. Deploy as a microservice (Docker):
```
FROM python:3.10
COPY agent.py /app/
RUN pip install langchain flask
CMD ["python", "/app/agent.py"]
```
 1. Expose as a webhook – now your SIEM can call this agent instead of a rigid playbook.
 Windows equivalent (PowerShell REST API for agent):
```
 agent.ps1
$body = @{ alert = "Suspicious process" } | ConvertTo-Json
Invoke-RestMethod -Uri http://localhost:5000/agent -Method POST -Body $body -ContentType "application/json"
```
 4. Breaking Vendor Lock-in: API-Centric Security Automation
 
 The post highlights a core problem: agents and SOAR that only work within one stack. Here’s how to enforce interoperability.
 
 Step-by-step: Create an OpenAPI spec for your security tools
 
 1. Define a unified schema for alerts (JSON):
```
{
"alert_id": "string",
"timestamp": "ISO8601",
"source": "SIEM|EDR|Cloud",
"severity": "1-5",
"indicators": ["IP", "hash", "domain"]
}
```
 1. Use a gateway like KrakenD or Apache APISIX to normalize API calls across vendors:
```
apisix route example
routes:</li>
</ol>

- uri: /siem/
upstream: http://elastic:9200
- uri: /edr/
upstream: http://crowdstrike:443
```
 1. Write a translation layer in Python that converts vendor-specific responses to the unified schema.
 Linux command to test API interoperability:
```
 Send same alert to three different SOAR endpoints
for endpoint in soar1 soar2 soar3; do curl -X POST http://$endpoint/api/alert -d @alert.json; done
```
 1. Hardening Your Agent Builder Against Poisoning & Abuse
 AI agents introduce new attack surfaces. Implement these mitigations.
 
 Step-by-step: Secure Agent Execution
 1. Run agents in isolated containers with read-only filesystems:
 docker run --read-only --tmpfs /tmp --cap-drop ALL agent-image
 2. Validate all LLM outputs using a deterministic allowlist:
 
 ALLOWED_ACTIONS = ["query_siem", "query_edr", "notify"] if agent_decision not in ALLOWED_ACTIONS: raise SecurityException("Agent attempted forbidden action")
 3. Set rate limits on agent API calls (Linux `tc` or cloud WAF):
 
 Limit agent to 10 requests per second tc qdisc add dev eth0 root handle 1: htb default 30 tc class add dev eth0 parent 1: classid 1:1 htb rate 10bps
 Windows PowerShell command for execution policy:
```
Set-ExecutionPolicy Restricted -Scope Process Block untrusted agent scripts
```
 1. Predicting the Future: Agent Wars & Open Standards
 The post predicts that SIEM vendors will acquire agent builders just as they did SOAR, leading to fragmented “dessert” that only works within one ecosystem. To avoid this, push for open standards like OASIS OpenC2 or STIX-Shifter.
 
 What Undercode Say:
 - Key Takeaway 1: Native SIEM SOAR and AI SOC features are convenient but create ingestion-centric lock-in – build portable automation using webhooks and open APIs instead.
 - Key Takeaway 2: Agent builders will succeed only if they support multi-vendor orchestration; standalone platforms (e.g., BlinkOps, Tines) currently have the edge in complexity and flexibility.
 Analysis (10 lines): The post correctly identifies a cyclical pattern: SIEMs absorb adjacent technologies (SOAR, now AI SOC, next agent builders) but often deliver half-baked, siloed implementations. For defenders, this means short-term convenience but long-term technical debt. The real innovation is happening outside legacy vendors – in open-source agent frameworks (LangChain, AutoGPT) and automation-first platforms. As AI agents become capable of reasoning across data silos, the need to centralize logs into a single SIEM diminishes. We predict a shift toward “agent mesh” architectures where each security product exposes a standardized agent interface, and a lightweight orchestrator routes alerts without moving all data. This will challenge the SIEM business model more than any feature bake-in.
 
 Prediction:
 
 Within 24 months, major SIEM vendors will announce “agent stores” similar to SOAR app marketplaces, but adoption will stall due to poor cross-vendor interoperability. Meanwhile, open-source agent frameworks will mature, enabling SOCs to build custom agents that query SIEM, EDR, cloud, and ticketing systems in one workflow – effectively creating a virtual SOAR/AI layer that lives above the SIEM. The winning vendors will be those that embrace API-first agent execution, not those that try to own the entire dessert plate.
 
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Listen to this Post

Introduction:

Learning Objectives:

You Should Know:

Step-by-step: Bridge SIEM Alerts to a SOAR Webhook

4. Test with a sample alert:

Step-by-step: Add AI Enrichment to SIEM Logs

2. Create a reasoning loop (pseudo-agent):

3. Deploy as a microservice (Docker):

Windows equivalent (PowerShell REST API for agent):

4. Breaking Vendor Lock-in: API-Centric Security Automation

1. Define a unified schema for alerts (JSON):

Linux command to test API interoperability:

Step-by-step: Secure Agent Execution

Windows PowerShell command for execution policy:

What Undercode Say:

Prediction:

▶️ Related Video (76% Match):

🎯Let’s Practice For Free:

IT/Security Reporter URL:

🔐JOIN OUR CYBER WORLD [ CVE News • HackMonitor • UndercodeNews ]

📢 Follow UndercodeTesting & Stay Tuned:

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