+---------------------------+
                    |   BloodHound JSON Export   |
                    |   (users, groups, ACLs)    |
                    +-------------+-------------+
                                  |
                                  v
                    +-------------+-------------+
                    |     Graph Analyzer         |
                    |  - Parse BH v3/v4 format   |
                    |  - Build NetworkX DiGraph   |
                    |  - Detect attack paths      |
                    |  - Identify Tier 0/1/2      |
                    +-------------+-------------+
                                  |
                    +------+------+------+
                    |      |             |
                    v      v             v
            +-------+  +--+------+  +---+--------+
            | Risk   |  | LLM    |  | Report     |
            | Scorer |  | Advisor|  | Generator  |
            +---+----+  +---+----+  +---+--------+
                |            |          |
                v            v          v
            [Scores]   [MITRE Map]  [HTML/JSON]
                |            |          |
                +------+-----+-----+---+
                       |
                       v
              +--------+---------+
              |   CLI Interface   |
              |  analyze | paths  |
              |  score   | report |
              +------------------+

git clone https://github.com/ayinedjimi/ADBloodHound-AI.git
cd ADBloodHound-AI
pip install -e ".[dev]"

# Optional: OpenAI-API-Schlüssel für KI-Funktionen setzen
export OPENAI_API_KEY="sk-..."

# Vollständige Analyse mit HTML-Bericht
adbloodhound-ai analyze bloodhound_export.json -d CORP.LOCAL -o report.html

# Pfade von einem bestimmten Benutzer finden
adbloodhound-ai paths bloodhound_export.json -s "[email protected]"

# Alle Knoten bewerten und Top-20-Risiken erhalten
adbloodhound-ai score bloodhound_export.json -n 20

# Vollständigen Bericht mit LLM-Analyse generieren
adbloodhound-ai report bloodhound_export.json -d CORP.LOCAL --llm

[+] 15423 Objekte geladen
[+] Graph: 15423 Knoten, 47891 Kanten
[+] Kerberoastable Konten: 23
[+] AS-REP Roastable Konten: 5
[+] DCSync-Prinzipale: 3
[+] 156 Angriffspfade zu Domain Admin gefunden
[+] Gesamtrisikowert der Domäne: 78.5/100

[!] Top 5 risikoreichste Pfade:
    1. [95.2] SVC_BACKUP -> DOMAIN ADMINS (2 Hops)
    2. [89.1] HELPDESK1 -> DOMAIN ADMINS (3 Hops)
    3. [84.7] SVC_SQL -> DOMAIN ADMINS (2 Hops)
    4. [76.3] IT_ADMIN2 -> ENTERPRISE ADMINS (4 Hops)
    5. [71.8] WS042.CORP.LOCAL -> DOMAIN ADMINS (5 Hops)

git clone https://github.com/ayinedjimi/ADBloodHound-AI.git
cd ADBloodHound-AI
pip install -e ".[dev]"

# Analyse complete avec rapport HTML
adbloodhound-ai analyze export_bloodhound.json -d CORP.LOCAL -o rapport.html

# Trouver les chemins depuis un utilisateur specifique
adbloodhound-ai paths export.json -s "[email protected]"

# Scorer tous les noeuds
adbloodhound-ai score export.json -n 20

# Rapport complet avec analyse LLM
adbloodhound-ai report export.json -d CORP.LOCAL --llm

pytest tests/ -v --cov=adbloodhound_ai