proactive-detection▌

Hunt for unusual network connections by analyzing outbound traffic patterns, rare destinations, non-standard ports, and anomalous connection frequencies from endpoints.

Hunt for web shell deployments on internet-facing servers by analyzing file creation in web directories, suspicious process spawning from web servers, and anomalous HTTP patterns.

Detect abuse of service accounts through anomalous interactive logons, privilege escalation, lateral movement, and unauthorized access patterns.

Hunt for supply chain compromise indicators including trojanized software updates, compromised dependencies, unauthorized code modifications, and tampered build artifacts.

Hunt for adversary persistence via Windows Scheduled Tasks by analyzing task creation events, suspicious task actions, and unusual scheduling patterns.

Detect Pass-the-Hash attacks by analyzing NTLM authentication patterns, identifying Type 3 logons with NTLM where Kerberos is expected, and correlating with credential dumping.

Detect Mimikatz execution through command-line patterns, LSASS access signatures, binary indicators, and in-memory detection of known modules.

Build a systematic threat hunt hypothesis framework that transforms threat intelligence, attack patterns, and environmental data into testable hunting hypotheses.

Detect Kerberoasting attacks by monitoring for anomalous Kerberos TGS requests targeting service accounts with SPNs for offline password cracking.

Systematically hunt for adversary persistence mechanisms across Windows endpoints including registry, services, startup folders, and WMI subscriptions.