Zero to Hero: Deploying Custom Agents in Microsoft Security Copilot – A Technical Deep Dive + Video

By /

Listen to this Post

Featured Image

Introduction:

As organizations rapidly adopt Generative AI to fortify their defenses, Microsoft Security Copilot has emerged as a pivotal tool, acting as a natural language interface for security operations. However, the real power of this platform lies in customization. By deploying “custom agents,” security teams can automate repetitive tasks and embed institutional knowledge directly into the AI. A recent public repository shared by a Microsoft insider provides a goldmine of pre-built, ready-to-deploy agents designed to streamline everything from incident response to threat hunting.

Learning Objectives:

  • Understand the architecture and use cases for custom agents within Microsoft Security Copilot.
  • Learn how to deploy pre-built custom agents from a GitHub repository into a live environment.
  • Explore how to extend these agents using Kusto Query Language (KQL) and API integrations for cloud hardening.

You Should Know:

1. Exploring the “Easy-Ready-to-Go” Agent Repository

The post highlights a GitHub repository (accessible via `https://lnkd.in/dA9mEzcM`) curated by Mario Cuomo, a Cloud Solution Architect at Microsoft. This repository contains YAML-based plugin files that define the behavior of custom agents. Instead of building from scratch, security engineers can leverage these templates to interact with specific data sources, such as Microsoft 365 Defender or custom threat intelligence feeds.

Step‑by‑step guide to accessing and reviewing the agents:

  1. Access the Repo: Navigate to the provided link (or search for “Microsoft Security Copilot Community” on GitHub).
  2. Clone the Repository: Use Git to pull the files locally for inspection. 
    git clone https://github.com/[bash]/SecurityCopilot-CustomAgents.git
cd SecurityCopilot-CustomAgents
  3. Review the Structure: Use the `ls` (Linux/macOS) or `dir` (Windows) command to list directories. You will typically find folders named Investigation, Hunting, or Remediation. 
    Linux/macOS
ls -la

Windows PowerShell
Get-ChildItem

2. Deploying a Custom Agent for Threat Hunting

One of the most valuable agents in the repository is likely one designed for proactive threat hunting. These agents use KQL to query logs across an enterprise. Deploying them bridges the gap between natural language questions (“Show me unusual logins”) and raw log data.

Step‑by‑step guide to deploying a KQL-based agent:

  1. Navigate to the Hunting Folder: Locate an agent file (e.g., Unusual_Logins.yaml).
  2. Understand the Manifest: Open the YAML file. It contains a `descriptor` defining the agent’s name, and `skills` which point to specific KQL queries. 
    Example snippet from a YAML file
name: "Unusual Geo-Location Detector"
skills:</li>
</ol>

- name: "RunKQLQuery"
description: "Detects logins from unusual locations"
kql: "SigninLogs | where Location notin ('US', 'GB')"

    3. Upload to Security Copilot: In the Security Copilot portal, navigate to the “Agents” section. Select “Upload custom agent” and point to the YAML file you reviewed. The platform will parse the instructions and create the agent.

    3. Integrating API Security and Cloud Hardening

    Custom agents are not limited to Microsoft logs; they can interact with external REST APIs. For cloud hardening, an agent can be configured to check for misconfigured cloud storage buckets (like Azure Blob or AWS S3) by querying the cloud provider’s API.

    Step‑by‑step guide to configuring an agent to check for public blob storage:
    1. Create a New Skill: In the Security Copilot studio, create a new skill that uses an HTTP request.
    2. Define the Endpoint: Target the Azure Resource Manager REST API to list storage accounts and check their network ACLs.

     Example API Call (conceptual for the agent to run)
 This is the underlying command the agent would execute
az storage account list --query "[?networkAcls.defaultAction=='Allow']" -o table

    3. Parse the Response: Configure the agent to interpret the JSON response and flag any account set to `Allow` (which implies public access). This automates the validation of cloud security posture.

    4. Automating Incident Response with Playbooks

    Security Copilot custom agents can also trigger automated responses via Logic Apps or Azure Functions. For instance, an agent detecting a ransomware indicator can automatically isolate the machine.

    Step‑by‑step guide to linking an agent to a remediation script:
    1. Locate the Remediation Script: The GitHub repo may contain a script to disable a compromised user account. Review a PowerShell script (e.g., Disable-ADUser.ps1).

     Disable-ADUser.ps1
Param(
[bash]$UserPrincipalName
)
Disable-ADAccount -Identity $UserPrincipalName

    2. Deploy the Script to Azure Automation: Upload this script to an Azure Automation Account.
    3. Create the Agent Skill: Define a skill in Security Copilot that calls the webhook URL of the Azure Automation runbook. Now, an analyst can simply ask the agent to “disable the compromised user,” and the agent handles the backend execution.

    5. Validating Agent Performance via Command Line

    To ensure the data the agent relies on is accurate, security professionals should validate the underlying logs directly. If an agent is misbehaving, checking the source data is the first step.

    Step‑by‑step guide to verifying source logs (Linux/Windows):

    • Linux (Syslog verification): Check if security events are being forwarded correctly. 
      sudo tail -f /var/log/auth.log | grep "Failed password"
    • Windows (Event Log verification): Use `wevtutil` to ensure the required logs (e.g., Security) are enabled and populated, as these feed into Microsoft Sentinel and subsequently Security Copilot. 
      wevtutil get-log Security /enabled
wevtutil qe Security /f:text /q:"[System[(EventID=4625)]]" /c:5

    What Undercode Say:

    • Democratization of AI Security: The release of these pre-built agents lowers the barrier to entry for Security Copilot, allowing small security teams to act like large enterprises.
    • Validation is Key: While these agents are “easy-ready-to-go,” they are only as good as the data they consume. Security engineers must validate the KQL queries and API endpoints against their specific environment to avoid false positives.

    The trend towards community-driven security automation is accelerating. By sharing these YAML configurations and scripts, the industry moves away from siloed tools toward an ecosystem where solutions to complex threats—like zero-day phishing or cloud misconfigurations—are shared and iterated upon globally. This GitHub repository isn’t just a collection of files; it’s a blueprint for the future of collaborative defense, where AI agents become the new standard operating procedure.

    Prediction:

    Within the next year, we will see the emergence of “Agent Marketplaces” where organizations can buy, sell, or share verified custom agents. This will shift the focus of security analysts from writing detection rules to curating and managing fleets of specialized AI agents, fundamentally changing the economics and efficiency of Security Operations Centers (SOCs).

    ▶️ Related Video (78% Match):

    🎯Let’s Practice For Free:

    IT/Security Reporter URL:

    Reported By: Mariocuomo Securitycopilot – Hackers Feeds
    Extra Hub: Undercode MoN
    Basic Verification: Pass ✅

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

    💬 Whatsapp | 💬 Telegram

    📢 Follow UndercodeTesting & Stay Tuned:

    𝕏 formerly Twitter 🐦 | @ Threads | 🔗 Linkedin | 🦋BlueSky

Related Posts: