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Introduction:
Microsoft Secure Score provides a critical measurement of your organization’s security posture, while Security Copilot offers AI-powered security analysis. This article explores how to bridge these powerful tools through custom plugin development, enabling security teams to leverage AI for comprehensive security assessment and root cause analysis.
Learning Objectives:
- Understand Microsoft Secure Score data structure and API endpoints
- Master Security Copilot plugin development and deployment
- Implement secure API integration with Microsoft Graph
- Optimize token usage and manage API limitations
- Leverage AI for security posture analysis and incident investigation
You Should Know:
1. Microsoft Graph API Authentication Setup
Connect to Microsoft Graph Connect-MgGraph -Scopes "SecurityEvents.Read.All" Verify authentication context Get-MgContext Test Secure Score access Invoke-MgGraphRequest -Method GET "https://graph.microsoft.com/v1.0/security/secureScores"
This PowerShell script establishes connection to Microsoft Graph with the necessary permissions. First, install the Microsoft.Graph module using Install-Module Microsoft.Graph. The `Connect-MgGraph` cmdlet authenticates your session, while `SecurityEvents.Read.All` scope grants read access to security data. Always verify your context with `Get-MgContext` before proceeding to API calls.
2. Secure Score API Endpoint Configuration
curl -X GET "https://graph.microsoft.com/v1.0/security/secureScores" \ -H "Authorization: Bearer $token" \ -H "Content-Type: application/json"
This curl command retrieves your organization’s Secure Score data. Replace `$token` with your actual access token obtained through OAuth2 flow. The API returns JSON containing current score, max possible score, and control assessments. Use jq for parsing: `curl … | jq ‘.value
.currentScore'` to extract specific metrics.
<h2 style="color: yellow;">3. Security Copilot Plugin Development</h2>
[bash]
from typing import List
from security_copilot_sdk import Skill, SkillResult
class SecureScoreSkill(Skill):
async def execute(self) -> SkillResult:
graph_url = "https://graph.microsoft.com/v1.0/security/secureScores"
headers = {"Authorization": f"Bearer {self.get_token()}"}
response = await self.http_client.get(graph_url, headers=headers)
return SkillResult(data=response.json())
This Python code demonstrates a basic Security Copilot plugin structure. The Skill class inherits from Security Copilot’s base Skill class, with the execute method handling the API call to Microsoft Graph. Ensure proper error handling and token management for production deployment.
4. API Response Processing and Filtering
import json
def process_secure_score(response_json):
data = json.loads(response_json)
score_data = data['value'][bash]
return {
'current_score': score_data['currentScore'],
'max_score': score_data['maxScore'],
'controls': [
{
'name': control['controlName'],
'score': control['score'],
'status': control['status']
}
for control in score_data['controlScores']
]
}
Since the Secure Score endpoint doesn’t support `$select` filtering, this Python function processes the full API response to extract essential fields. It structures the data for easier consumption by Security Copilot, focusing on current score, maximum score, and individual control assessments.
5. Cost Optimization and SCU Management
Monitor SCU usage az monitor log-analytics query \ --workspace "SecurityCopilotWorkspace" \ --query "SecurityCopilotUsage_CL | where TimeGenerated >= ago(24h)" \ --output table
This Azure CLI command monitors Security Copilot Unit (SCU) consumption. Since the Secure Score plugin averages 1 SCU per execution, track usage patterns to optimize costs. Implement caching mechanisms to reduce frequent API calls and consider batching requests during off-peak hours.
6. Security Control Assessment Integration
$secureScore = Invoke-MgGraphRequest -Method GET "https://graph.microsoft.com/v1.0/security/secureScores"
$controls = $secureScore.value[bash].controlScores
$weakControls = $controls | Where-Object {$_.score -lt 0.5}
$weakControls | Format-Table controlName, score, description
This PowerShell script identifies security controls scoring below 50%. It extracts control scores from the Secure Score response and filters for weak areas. Use this data to prioritize remediation efforts and focus on controls with the most significant impact on your overall security posture.
7. Incident Root Cause Analysis Automation
async def analyze_incident_root_cause(incident_data, secure_score_data):
affected_controls = []
for control in secure_score_data['controls']:
if control['status'] == 'Unhealthy' and matches_incident_pattern(control, incident_data):
affected_controls.append(control)
return {
'incident_id': incident_data['id'],
'related_controls': affected_controls,
'posture_impact': calculate_impact(affected_controls)
}
This Python function demonstrates how to correlate incident data with Secure Score controls for root cause analysis. It identifies unhealthy controls that match incident patterns, helping security teams understand how control failures contribute to security incidents and where to focus remediation efforts.
What Undercode Say:
- The Secure Score API limitation of no `$select` support significantly impacts performance and cost efficiency
- Proper SCU management becomes critical as organization scale their Security Copilot usage
- Custom plugins bridge critical gaps but require ongoing maintenance with platform updates
The integration of Microsoft Secure Score with Security Copilot represents a significant step toward AI-driven security management. However, the current implementation reveals important limitations, particularly around API efficiency and cost management. Organizations must weigh the benefits of real-time posture analysis against the operational costs of frequent API calls. The inability to filter response payloads via `$select` forces developers to process large datasets client-side, increasing computational overhead and potentially impacting Security Copilot performance. As Microsoft continues to evolve both Secure Score and Security Copilot, we expect these integration patterns to become more streamlined, but for now, custom plugins provide essential connectivity at the cost of additional development and maintenance overhead.
Prediction:
Within two years, Microsoft will natively integrate Secure Score into Security Copilot, rendering custom plugins obsolete but establishing new patterns for extensibility. The current plugin development approach will evolve into a standardized framework for security tool integration, enabling more sophisticated AI-driven security orchestration. Organizations investing in these integration patterns today will gain valuable experience that positions them to leverage future AI security capabilities more effectively, while those waiting for native integration may face catch-up challenges in a rapidly evolving AI security landscape.
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IT/Security Reporter URL:
Reported By: Jaimeguimera Securitycopilot – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
