Threat actors are not relying on slow, manual reconnaissance anymore. They are using AI-assisted tooling to speed up Active Directory enumeration and test endpoint defenses at a pace that is hard to match by hand.
If your detection strategy still depends on static rule sets built around human-paced attacks, you are leaving gaps. This guide brings together ten detection engineering queries, in both Sigma and KQL, that are meant to catch the behavioral traces AI-assisted intrusion campaigns tend to leave behind.
Any digital environment that handles sensitive user data, whether it is an enterprise network or a consumer platform, faces the same basic problem: telling normal activity apart from hostile probing. Security teams protecting corporate infrastructure and platform operators managing large user populations both benefit from layered behavioral detection.
In the consumer entertainment space, for example, use behavioral analytics to flag unusual account activity at scale. That approach closely mirrors how security operations centers detect bursts of suspicious enumeration in Active Directory. The idea is simple: establish a baseline, spot meaningful deviation, and respond in proportion to the risk.
Why AI-Assisted Recon Changes the Detection Problem
Traditional AD recon signatures were built around human behavior. When an attacker manually runs net user /domain or Get-ADUser, the event stream is usually recognizable and relatively slow. AI-assisted tooling changes that rhythm completely.
Sophos X-Ops researchers have documented a threat actor actively using AI technologies to probe and bypass endpoint detection and response controls, which shows that AI-assisted evasion is no longer just a red-team thought exercise. It is part of real-world adversary behavior. That means detection logic has to focus more on velocity, sequencing, and contextual anomalies instead of depending only on isolated command signatures.
The wider threat landscape around AI-assisted Active Directory attacks and EDR evasion makes the weakness of static detection rules clear. AI agents can cycle through evasive techniques faster than most analysts can revise and redeploy query libraries.
Ten Queries Covering the Core Attack Surface
The following detection logic focuses on the behaviors that are most consistently visible in AI-assisted AD recon and EDR evasion activity.
1. High-Velocity LDAP Query Bursts
Sigma rule targeting EventID 1644 with a threshold of more than 30 LDAP queries from a single source within 60 seconds. KQL equivalent uses SecurityEvent filtered on LdapQuery with a summarize count() by Account, bin(TimeGenerated, 1m) threshold above the expected baseline.
2. Automated User Enumeration via Net Commands
Sigma rule matching net.exe or net1.exe with user /domain arguments, especially when the commands appear in a tight 30-second window from a non-admin workstation.
3. DCSync Simulation Detection
KQL query against SecurityEvent for EventID 4662 with AccessMask 0x100 on DS-Replication-Get-Changes-All, filtered to exclude legitimate domain controller source accounts.
4. Kerberoasting Ticket Request Spikes
Sigma rule on EventID 4769 with TicketEncryptionType 0x17 (RC4), grouped by requesting account with a count threshold above five within two minutes.
5. Suspicious SAMR Enumeration
KQL targeting MicrosoftWindowsSecurityAuditing for EventID 4661 with object type SAM_USER, grouped by SubjectUserName with a sliding-window count to surface repeated lookups.
6. EDR Process Injection Attempts
Sigma rule covering CreateRemoteThread calls from unusual parent processes into lsass.exe or security product processes, using Sysmon EventID 8.
7. Credential Access via LSASS Memory Read
KQL on Defender DeviceEvents filtering ProcessName for lsass.exe with ActionType of OpenProcessApiCall from unsigned binaries.
8. Rapid Service Binary Modification
Sigma rule on EventID 7045 combined with file write events to System32, where the service binary path points to a temp or other user-writable directory.
9. Systematic Security Tool Enumeration
KQL query targeting DeviceProcessEvents for sequential queries to sc.exe query or tasklist /svc against known EDR process names within a 90-second window.
10. Token Impersonation via SeDebugPrivilege Abuse
Sigma rule on EventID 4703 where EnabledPrivilegeList contains SeDebugPrivilege and the process is not a recognized administrative tool.
Tuning for AI-Paced Adversaries
The gap between threat intelligence and production-ready detection logic is shrinking as AI agents capable of iteratively refining KQL queries and producing validated Sigma rules move from research into day-to-day security operations. Teams should treat these ten queries as a strong starting point, not a finished detection program.
Key tuning recommendations include:
- Establish per-environment baselines before setting count thresholds, because AD environments vary widely in normal LDAP query volume
- Chain related events across queries instead of evaluating each one in isolation, because AI-assisted campaigns usually create correlated multi-stage activity
- Version-control all Sigma rules in a repository with change history so teams can roll back quickly if tuning creates false-positive spikes
- Test against purple team simulations that mirror AI-paced enumeration tempo, not just traditional human-paced red team exercises
Detection engineering for AI-assisted adversaries is iterative by nature. The ten queries above cover some of the highest-signal behavioral patterns currently seen in the wild, but the most durable detection programs will keep refining their logic as attacker tooling changes. Velocity-aware, context-sensitive rules provide the foundation. Everything else builds on that.
