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Enterprise Active Directory Security Monitoring Lab

Project Overview

This project simulates an enterprise Windows security environment designed for centralized monitoring, endpoint visibility, and attack detection.

The lab combines Active Directory, endpoint telemetry, and SIEM monitoring to demonstrate how security events can be collected, analyzed, and investigated in a controlled domain environment.

The objective is to build a practical security monitoring environment aligned with enterprise security operations and selected ISO/IEC 27001 security controls.


Core Technologies

  • Active Directory Domain Services
  • Windows Server
  • Sysmon
  • Splunk Enterprise
  • Splunk Universal Forwarder
  • Kali Linux

Lab Architecture

Kali Linux
   ↓
Windows Workstations (GICHINGA / MWAURA)
   ↓
Sysmon + Windows Event Logs
   ↓
Splunk Universal Forwarder
   ↓
Splunk Enterprise on MAXWELL

Infrastructure

Domain Controller

  • MAXWELL

Workstations

  • GICHINGA
  • MWAURA

Attacker Machine

  • Kali Linux

Domain

corp.local

Project Components

Active Directory Deployment

Includes:

  • Domain creation
  • Organizational Unit design
  • Group Policy enforcement
  • Domain joining

Documentation:

lab_setup/active_directory_setup.md

Sysmon Deployment

Includes:

  • Endpoint telemetry collection
  • Process monitoring
  • Network event logging
  • Registry visibility

Documentation:

lab_setup/sysmon_installation.md

Splunk SIEM Deployment

Includes:

  • Centralized log ingestion
  • Index creation
  • Search validation

Documentation:

lab_setup/splunk_installation.md

Universal Forwarder Configuration

Includes:

  • Windows event forwarding
  • Sysmon log forwarding
  • Endpoint log centralization

Documentation:

lab_setup/forwarder_configuration.md

Detection Engineering

Custom Splunk detections were created for common attack patterns.

Failed Login Detection

detection_queries/failed_logins.spl

Detects:

  • brute-force attempts
  • password spraying

PowerShell Detection

detection_queries/powershell_detection.spl

Detects:

  • suspicious PowerShell execution
  • encoded command usage

Lateral Movement Detection

detection_queries/lateral_movement.spl

Detects:

  • remote network logons
  • administrative pivoting

Attack Simulation

Controlled attack simulations were performed from Kali Linux to validate detection coverage.

Documentation:

reports/incident_simulation.md

Simulated scenarios:

  • failed authentication attempts
  • PowerShell execution
  • remote logon activity

Security Monitoring Value

This lab demonstrates:

  • endpoint visibility
  • centralized detection
  • event investigation workflow
  • enterprise monitoring fundamentals

ISO/IEC 27001 Alignment

The environment supports selected security control areas:

Access Control

  • centralized authentication
  • domain policy enforcement

Logging and Monitoring

  • event collection
  • SIEM ingestion

Operational Security

  • detection validation
  • incident visibility

Repository Structure

Enterprise-AD-Security-Lab/
├── README.md
├── lab_setup/
│   ├── active_directory_setup.md
│   ├── sysmon_installation.md
│   ├── splunk_installation.md
│   ├── forwarder_configuration.md
│
├── detection_queries/
│   ├── failed_logins.spl
│   ├── powershell_detection.spl
│   ├── lateral_movement.spl
│
├── reports/
│   ├── incident_simulation.md
│
├── screenshots/

Screenshots

The screenshots folder contains evidence of:

  • Active Directory configuration
  • Sysmon events
  • Splunk searches
  • Detection results

Future Improvements

Planned expansion:

  • MITRE ATT&CK mapping
  • Splunk dashboards
  • executive incident reporting
  • additional attack simulations

Security Focus

This project demonstrates practical SOC-oriented monitoring using enterprise Windows infrastructure.

About

Active Directory Detection Lab: A full-scale simulation of the Cyber Attack Lifecycle. Built a Windows Server AD environment from scratch, integrated Sysmon and Splunk for deep telemetry, and executed targeted Kali Linux attacks (Kerberoasting, SMB Exfil, SAM Dumping) to create high-fidelity detection rules and dashboards.

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