AD 2026 Enumeration Series. Prologue


AD with AI 2026 Enumeration Series. Prologue: From Manual Recon to AI-Assisted Tradecraft.



Active Directory (AD) is still the crown jewel of most enterprise environments. Despite years of hardening, it remains one of the most targeted—and most misunderstood—attack surfaces in internal penetration testing. But in 2026, enumeration is no longer just about running tools like BloodHound and dumping LDAP data.

We’ve entered the era of AI-assisted offensive security — where automation doesn’t just collect data, it interprets, prioritizes, and guides our attack path. Literally new era of cybersecurity.

Here we’ll walk through a modern AD enumeration scenario and how red teamers are combining traditional techniques with AI-driven workflows.

Common Scenario: Initial Foothold in a Corporate Network

You’ve gained access via:

  • Phishing (user-level access)
  • VPN credentials
  • Initial shell on a domain-joined workstation

Your objectives:

  1. Map the domain
  2. Identify privilege escalation paths
  3. Locate high-value targets (Domain Admins, servers, trust relationships)
  4. Stay stealthy

Phase I: Passive & Low-Noise Enumeration. Before touching anything noisy, modern operators start with context gathering.

Key Data Sources

  • Environment variables
  • Cached credentials
  • Logged-in users
  • Domain info via built-in commands
whoami /all
set
nltest /dsgetdc:<domain>

Living-off-the-Land Enumeration

Avoid dropping tools early. Use native binaries:

  • net user /domain
  • net group "Domain Admins" /domain
  • net config workstation

Phase II: LDAP & AD Graph Enumeration. This is where things used to get noisy. In 2026, it’s more controlled.

Traditional approach

  • BloodHound (SharpHound collector)
  • LDAP queries
  • Kerberoasting scans

Up to date approach 

Instead of dumping everything, operators now:

  • Perform targeted LDAP queries
  • Use adaptive collection strategies
  • Feed results into AI-assisted analysis pipelines

Phase III: AI-Assisted Enumeration. Here’s where things change significantly.

How AI could help?

  • Parsing massive AD datasets
  • Highlighting attack paths
  • Suggesting privilege escalation strategies
  • Detecting misconfigurations faster than manual review

Example Workflow

  1. Collect minimal AD data:

    • Users
    • Groups
    • ACLs (selective)
    • SPNs

  2. Feed into an AI model (local or secured environment)

  3. Ask:

    • “Show shortest path to Domain Admin”
    • “Find accounts vulnerable to Kerberoasting”
    • “Identify shadow admin privileges”

Example Insight Output

Instead of manually digging through graphs, AI might return:

  • User svc_backup has GenericAll over IT_Admins
  • IT_Admins is nested in Domain Admins
  • Password last set 3 years ago
  • SPN present → Kerberoastable

Suggested attack path: Kerberoast → Crack → Privilege escalation → DA

Phase 4: Modern Enumeration Techniques

1. Kerberos Abuse Enumeration

  • Identify SPNs
  • AS-REP roasting candidates
  • Delegation misconfigurations

2. ACL & Permission Analysis

  • GenericAll / GenericWrite
  • WriteDACL
  • Resource-Based Constrained Delegation (RBCD)

3. Session & Lateral Movement Mapping

  • Logged-on users
  • Admin sessions
  • Workstation relationships

Phase 5: Stealth & Evasion Considerations

Detection has improved significantly.

Modern defensive tools:

  • UEBA systems
  • EDR with behavioral analytics
  • AD auditing enhancements

Red Team Adjustments

  • Limit query volume
  • Randomize enumeration timing
  • Use “human-like” query patterns
  • Prefer API/LDAP over noisy collectors

Phase 6: Automation Pipelines

In 2026, mature operators don’t run tools manually — they build pipelines.

Example Stack

  • Data Collection: Lightweight LDAP scripts
  • Processing: Python + graph analysis
  • AI Layer: Local LLM / secure inference engine
  • Output: Attack path prioritization

Example Python Concept:


data = collect_ldap(minimal=True)

analysis = ai_model.analyze(data, goals=[
    "privilege_escalation",
    "kerberoast_targets",
    "lateral_movement"
])
print(analysis["top_attack_paths"])
  • AI enhances decision-making, not exploitation
  • Stealth is now as important as coverage

From:

“Run BloodHound and click around”

To:

“Collect minimal data, let AI highlight the path, execute with precision”.

If you’re still doing full-domain dumps by default, you’re not just outdated — you’re noisy! And in 2026, noisy gets you caught. 

What should We learn next If U want to go deeper?

DM ME...


Tags
Pentest IT

Posted by Pentest IT