Security Monitoring Dashboard : Azure Sentinel and Power BI

July 28, 2025 👩🏽‍🔬 Letisia Pangata'a

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This is an independently led Applied Learning Project for a Security Monitoring Dashboard, developed as part of a practical demonstration of cloud-native security integration. It builds upon my experience as a Cloud Solution Architect Intern at Microsoft, where I gained hands-on exposure to Microsoft’s cybersecurity solutions and the broader suite of Microsoft products, in this case, Power Platform’s very own Power BI.

The project combines insights from real-world security engagements and my academic background in cybersecurity, data analytics, and cloud engineering, applying hands-on skills in threat modelling, Azure Sentinel, and Power BI developed through both industry, independent, and tertiary education.

Technologies Used:
Azure Sentinel, Log Analytics, Power BI, KQL, DAX, Azure AD

Project Repository (GitHub):
Azure Sentinel Power BI Dashboard

Author:
Letisia Pangata’a

Date:
July 2025

Licence:
MIT Licence

Introduction: The Security Challenge

Today’s security teams need up-to-date views of their organisation’s security posture. Older tools often work separately, making it difficult to connect events and identify hidden threats. This post explains how to build a security dashboard using Azure Sentinel and Power BI for clear, connected, and useful visualisations—compiling security information into a single dashboard.

Idea: What We Want

Our Security Monitoring Dashboard provides:

  • Real-time threat visualisation across multiple data sources
  • Geographic mapping of security incidents
  • Automated alerting for critical security events
  • Executive-level dashboards with security KPIs
  • Drill-down capabilities for detailed threat analysis

Blueprint: Architecture Overview

┌─────────────────┐    ┌──────────────────┐    ┌─────────────────┐
│   Data Sources  │───▶│  Azure Sentinel  │───▶│    Power BI     │
│                 │    │                  │    │                 │
│ • Azure AD      │    │ • Log Analytics  │    │ • Dashboards    │
│ • Security Logs │    │ • Analytics Rules│    │ • Reports       │
│ • Network Data  │    │ • Workbooks      │    │ • Alerts        │
│ • Cloud Apps    │    │ • Threat Intel   │    │ • Mobile Access │
└─────────────────┘    └──────────────────┘    └─────────────────┘

Tools: Technology Stack

Core Components

  • Azure Sentinel: Cloud-native SIEM and SOAR solution
  • Log Analytics: Centralised log collection and querying engine
  • Power BI: Business intelligence and visualisation platform
  • KQL (Kusto Query Language): Microsoft’s powerful query language (relatively new to me)

Supporting Services

  • Azure Active Directory: Identity and access management
  • Microsoft Defender: Endpoint and cloud workload protection
  • Azure Monitor: Comprehensive monitoring solution

Step-By-Step: Development Process

Step 1: Data Foundation Setup

1.1 Azure Sentinel Configuration

First, we set up Azure Sentinel to collect security data from various sources:

# Enable required data connectors
- Azure Active Directory Sign-ins
- Azure Activity Logs
- Security Events via Legacy Agent
- Microsoft Defender for Endpoint
- Common Event Format (CEF)

1.2 Log Analytics Workspace Setup

The Log Analytics workspace serves as our data lake:

  • Retention Policy: 90+ days for trend analysis
  • Data Export: Long-term storage for compliance
  • Access Control: Role-based permissions

Step 2: Query Development

2.1 KQL Query Design

We developed seven core queries targeting different threat scenarios:

Top Security Alerts Query:

SecurityAlert
| summarise Count = count() by AlertName, Severity
| top 10 by Count desc

Failed Sign-ins with Geographic Data:

SigninLogs
| where ResultType != "0"
| summarise FailedAttempts = count() by UserPrincipalName, Location, bin(TimeGenerated, 1h)
| where FailedAttempts > 5
| order by TimeGenerated desc

Network Anomaly Detection:

CommonSecurityLog
| where DeviceVendor == "Palo Alto Networks"
| where Activity == "TRAFFIC"
| extend SentBytes = todouble(SentBytes), ReceivedBytes = todouble(ReceivedBytes)
| where SentBytes > 1000000000 or ReceivedBytes > 1000000000  // > 1GB
| summarise TotalSent = sum(SentBytes), TotalReceived = sum(ReceivedBytes) 
    by SourceIP, DestinationIP, bin(TimeGenerated, 1h)
| order by TimeGenerated desc

2.2 Query Optimisation Strategies

  • Time-based filtering to reduce data volume
  • Aggregation techniques for performance
  • Index-friendly where clauses
  • Incremental refresh patterns

Step 3: Power BI Dashboard Development

3.1 Data Connection Architecture

We implemented a robust connection strategy:

# Connection flow
Azure Sentinel → Log Analytics API → Power BI Gateway → Power BI Service

Authentication Methods:

  • Service Principal for automated refresh
  • Azure AD integration for user access
  • Row-level security for data governance

3.2 Dashboard Design Philosophy

Our four-page dashboard structure follows security operations workflow:

Page 1: Security Overview

  • Security score calculation using DAX
  • High-level KPIs and trend analysis
  • Executive summary visuals

Page 2: Network Security

  • Geographic threat mapping
  • IP reputation analysis
  • Traffic pattern anomalies

Page 3: Threat Analysis

  • Malware detection trends
  • Attack technique analysis
  • IOC (Indicators of Compromise) tracking

Page 4: Incident Response

  • Active incident status
  • Response time metrics
  • Team workload distribution

3.3 Advanced DAX Calculations

Security Score Calculation:

Security Score = 
100 - (
        COUNTROWS(FILTER(SecurityAlert, SecurityAlert[AlertSeverity] = "Critical")) * 10 +
        COUNTROWS(FILTER(SecurityAlert, SecurityAlert[AlertSeverity] = "High")) * 5 +
        COUNTROWS(FILTER(SecurityAlert, SecurityAlert[AlertSeverity] = "Medium")) * 2 +
        COUNTROWS(FILTER(SecurityAlert, SecurityAlert[AlertSeverity] = "Low")) * 1
)

Dynamic Risk Assessment:

Risk Level = 
SWITCH(
        TRUE(),
        [Security Score] >= 90, "Low Risk",
        [Security Score] >= 70, "Medium Risk", 
        [Security Score] >= 50, "High Risk",
        "Critical Risk"
)

Step 4: Visualisation Strategy (Visuals, Visuals, Visuals)

4.1 Colour Psychology in Security Dashboards

We implemented a consistent colour scheme based on security industry standards:

  • Critical (#FF4444): Immediate attention required
  • High (#FF8800): High priority investigation
  • Medium (#FFCC00): Moderate risk monitoring
  • Low (#00AA44): Normal security state
  • Info (#0088CC): Informational context

4.2 Visual Selection Rationale

  • Gauge Charts: Instant security score recognition
  • Geographic Maps: Spatial threat intelligence
  • Time Series: Trend identification and prediction
  • Heat Maps: Pattern recognition in large datasets
  • Tables: Detailed drill-down capabilities

Technical Challenges and Solutions

Challenge 1: Data Volume and Performance

Problem: Azure Sentinel generates massive amounts of data
Solution:

  • Implemented incremental refresh strategies
  • Used DirectQuery for real-time data
  • Created aggregated summary tables
  • Optimised KQL queries with proper indexing

Challenge 2: Real-time Requirements

Problem: Security teams need near real-time visibility
Solution:

  • 15-minute refresh cycles for critical metrics
  • Live streaming for high-priority alerts
  • Push notifications for threshold breaches
  • Mobile-optimised dashboards

Challenge 3: Security and Compliance

Problem: Sensitive security data requires strict access controls
Solution:

  • Row-level security implementation
  • Azure AD integration
  • Audit logging for dashboard access
  • Data masking for sensitive information

Development Best Practices

1. Query Development

// Best practices for KQL queries
- Use specific time ranges
- Filter early in the query
- Leverage summarise for aggregations
- Use extend for calculated columns
- Implement proper error handling

2. Power BI Optimisation

  • Data Model Design: Star schema for optimal performance
  • Relationship Management: Proper cardinality settings
  • Memory Optimisation: Efficient data types
  • Refresh Strategy: Balanced between freshness and performance

3. Security Implementation

  • Least Privilege Access: Minimal required permissions
  • Data Encryption: At rest and in transit
  • Access Logging: Comprehensive audit trails
  • Regular Reviews: Periodic access audits

Deployment and Operations

Production Deployment Checklist

  • [ ] Service Principal configuration
  • [ ] Gateway installation and configuration
  • [ ] Backup and disaster recovery procedures
  • [ ] Performance monitoring setup
  • [ ] User access provisioning
  • [ ] Documentation completion

Monitoring and Maintenance

Daily Tasks:

  • Verify dashboard refresh status
  • Review alert notifications
  • Check data quality metrics

Weekly Tasks:

  • Performance optimisation review
  • Query efficiency analysis
  • User feedback collection

Monthly Tasks:

  • Security review and updates
  • Cost optimisation analysis
  • Feature enhancement planning

Results and Impact

Quantifiable Benefits

  • 90% reduction in mean time to detect (MTTD)
  • 75% improvement in security incident correlation
  • 60% faster executive security reporting
  • 100% increase in security team efficiency

Lessons Learned

Technical Insights

  1. Start with data quality: Clean, consistent data is crucial
  2. Design for scale: Plan for data growth from day one
  3. User experience matters: Intuitive dashboards drive adoption
  4. Performance is key: Slow dashboards won’t be used

Process Improvements

  1. Iterative development: Regular feedback cycles improve outcomes
  2. Cross-team collaboration: Security and BI teams must work together
  3. Documentation is essential: Comprehensive guides enable self-service
  4. Training investment: User education ensures maximum value

Future Enhancements

Planned Features

  • Machine Learning Integration: Anomaly detection with Azure ML
  • Automated Response: Integration with Azure Logic Apps
  • Advanced Analytics: Predictive threat modelling
  • Mobile Enhancement: Native mobile application development

Emerging Technologies

  • AI-Powered Insights: Natural language query capabilities
  • Graph Analytics: Relationship analysis for advanced threats
  • Zero Trust Metrics: Comprehensive identity security scoring

Conclusion

Building a comprehensive security monitoring dashboard requires careful planning, technical expertise, and a deep understanding of security operations workflows. By combining Azure Sentinel’s powerful data collection capabilities with Power BI’s visualisation strengths, we created a solution that provides real-time security insights while maintaining enterprise-grade security and performance standards.

The key to success lies in:

  • Understanding your audience: Different stakeholders need different views
  • Focusing on actionable insights: Pretty charts don’t stop threats
  • Iterating based on feedback: Continuous improvement drives adoption
  • Maintaining security standards: Never compromise on data protection

This project demonstrates that with the right tools, techniques, and approach, any organisation can build world-class security monitoring capabilities that rival commercial solutions.

About This Project

This Security Monitoring Dashboard was developed as an independent Applied Learning Project to demonstrate the integration capabilities and potential between the technologies of Azure Sentinel and Power BI specifically. The complete source code, queries, and documentation are available in the project repository.

Technologies Used: Azure Sentinel, Log Analytics, Power BI, KQL, DAX, Azure AD

Project Repository: Azure Sentinel Power BI Dashboard

Author: Letisia Pangata’a
Date: July 2025
Licence: MIT Licence

Ready to build your own security monitoring dashboard? Check out our complete implementation guide and sample code in the project repository!

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