🛡️ SecOps Sentinel Suite

AI-Augmented Detection & Response (Microsoft Sentinel + Defender for Endpoint)

Detection engineering + SOC automation
Built to simulate real-world SecOps workflows with AI-assisted tuning.

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📌 Overview

SecOps Sentinel Suite is a hands-on security project showcasing proactive detection engineering and security automation using:

• Microsoft Sentinel (SIEM) for KQL detections + analytics
• Microsoft Defender for Endpoint (EDR) for endpoint telemetry + response actions
• AI-assisted optimization to reduce noise and improve detection signal

Think: “SOC analyst workflows—compressed into a portfolio-ready project.” 🚀

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🎯 Objectives

This project is designed to:

• ✅ Build and tune custom KQL detection rules
• ✅ Reduce false positives (less noise, more signal)
• ✅ Automate containment / response logic
• ✅ Visualize attack patterns and security trends via dashboards

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🔍 Use Cases Implemented

Current detection scenarios include:

• Brute Force Detection (failed sign-in bursts, threshold logic)
• Impossible Travel (geo-velocity / location anomaly patterns)
• Suspicious Process Execution (unusual parent-child behavior)
• Data Exfiltration Indicators (spikes, unusual destinations, abnormal volume)

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🧠 AI-Augmented Workflow (The Fun Part)

To make detections stronger and cleaner, I used AI to help:

• 🧩 Refactor KQL logic for clarity + efficiency
• 🎯 Adjust thresholds to reduce alert fatigue
• 🧪 Validate detection assumptions with edge cases
• 🧼 Improve signal quality (fewer “meh” alerts, more “oh wow” alerts)

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📈 Security Impact (Simulated)

Key outcomes from the implemented workflows:

• 🔒 Reduced simulated brute-force success paths to near zero
• ⚡ Automated isolation triggers for high-confidence endpoint events
• 📊 Faster visibility into threats using dashboards + structured alerting
• 📝 Cleaner documentation for repeatable SOC playbooks

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🗂️ Repo Structure

SecOps-Sentinel-Suite/
├── screenshots/
├── kql/
├── playbooks/
└── README.md