Detection engineering
Custom SIEM + Sentinel BAS
A Python SIEM paired with an attack-simulation engine that measures whether each detection actually fires.
Read case study Validation results by technique Source code on GitHub
Detection engineering is where most of my work meets: collecting Windows and Sysmon telemetry, writing detection rules in the Sigma format, mapping each rule to the MITRE ATT&CK technique it is meant to catch, and then running that technique to see whether the rule actually fires.
The projects and write-ups below are that loop in practice: a SIEM built from scratch, an attack-simulation engine that measures its coverage, an Active Directory lab where each attack phase has a matching detection, and the threat detectors inside ARGUS.
Detection engineering
A Python SIEM paired with an attack-simulation engine that measures whether each detection actually fires.
Read case study Validation results by technique Source code on GitHub
Purple team lab
An enterprise Active Directory lab for adversary emulation and SOC detection, mapping attack paths to Windows and Sysmon telemetry and MITRE ATT&CK.
Security engineering
16 MITRE ATT&CK-tagged threat detectors watch the Windows machine ARGUS runs on.
Nine attack phases, nine matching detections — red team and blue team, same person, same lab.
Somewhere in building a SIEM and an Active Directory attack lab separately, I kept hitting the same annoying question: how do I actually know a detection rule works?
Not another “top 10 SIEM tools” listicle — the actual debugging notes from building one.
Three of the rules from my SIEM, each mapped to the ATT&CK technique it detects. The full set is on GitHub.
T1003 OS Credential DumpingCriticalT1059.001 Command and Scripting Interpreter: PowerShellHighT1570 Lateral Tool TransferMedium