Implementing Threat Modeling with MITRE ATT&CK

When to Use

Use this skill when:

• SOC teams need to assess detection coverage against relevant threat actors and their TTPs
• Security leadership requires threat-informed defense prioritization
• New environments (cloud migration, OT integration) need detection strategy planning
• Purple team exercises require structured adversary emulation based on threat models
• Annual risk assessments need ATT&CK-based threat landscape analysis

Do not use as a one-time exercise — threat models must be continuously updated as adversary TTPs evolve and organizational attack surface changes.

Prerequisites

• MITRE ATT&CK framework knowledge (Enterprise, ICS, Mobile, or Cloud matrices)
• ATT&CK Navigator tool (web or local) for layer visualization
• Current detection rule inventory mapped to ATT&CK technique IDs
• Threat intelligence on adversary groups targeting your sector
• Organizational asset inventory with criticality classifications

Workflow

Step 1: Identify Relevant Threat Actors

Research adversary groups targeting your sector using MITRE ATT&CK Groups:

import requests
import json

# Download ATT&CK STIX data
response = requests.get(
    "https://raw.githubusercontent.com/mitre/cti/master/enterprise-attack/enterprise-attack.json"
)
attack_data = response.json()

# Extract groups and their techniques
groups = {}
for obj in attack_data["objects"]:
    if obj["type"] == "intrusion-set":
        group_name = obj["name"]
        aliases = obj.get("aliases", [])
        description = obj.get("description", "")
        groups[group_name] = {
            "aliases": aliases,
            "description": description[:200],
            "techniques": []
        }

# Map techniques to groups via relationships
relationships = [obj for obj in attack_data["objects"] if obj["type"] == "relationship"]
techniques = {obj["id"]: obj for obj in attack_data["objects"]
              if obj["type"] == "attack-pattern"}

for rel in relationships:
    if rel["relationship_type"] == "uses":
        source = rel["source_ref"]
        target = rel["target_ref"]
        for group_name, group_data in groups.items():
            if source == group_data.get("id") and target in techniques:
                tech = techniques[target]
                ext_refs = tech.get("external_references", [])
                for ref in ext_refs:
                    if ref.get("source_name") == "mitre-attack":
                        group_data["techniques"].append(ref["external_id"])

# Example: Financial sector threat actors
financial_actors = ["FIN7", "FIN8", "Carbanak", "APT38", "Lazarus Group"]
for actor in financial_actors:
    if actor in groups:
        print(f"{actor}: {len(groups[actor]['techniques'])} techniques")
        print(f"  Top techniques: {groups[actor]['techniques'][:10]}")

Step 2: Build Threat Actor TTP Profile

Create ATT&CK Navigator layers for priority threat actors:

import json

def create_attack_layer(actor_name, techniques, color="#ff6666"):
    """Generate ATT&CK Navigator JSON layer for a threat actor"""
    layer = {
        "name": f"{actor_name} TTP Profile",
        "versions": {
            "attack": "15",
            "navigator": "5.0",
            "layer": "4.5"
        },
        "domain": "enterprise-attack",
        "description": f"Techniques associated with {actor_name}",
        "techniques": [
            {
                "techniqueID": tech_id,
                "tactic": "",
                "color": color,
                "comment": f"Used by {actor_name}",
                "enabled": True,
                "score": 1
            }
            for tech_id in techniques
        ],
        "gradient": {
            "colors": ["#ffffff", color],
            "minValue": 0,
            "maxValue": 1
        }
    }
    return layer

# Create layers for top threat actors
fin7_techniques = ["T1566.001", "T1059.001", "T1053.005", "T1547.001",
                    "T1078", "T1021.001", "T1003", "T1071.001", "T1041"]
layer = create_attack_layer("FIN7", fin7_techniques, "#ff6666")

with open("fin7_layer.json", "w") as f:
    json.dump(layer, f, indent=2)

Step 3: Map Current Detection Coverage

Export current detection rules mapped to ATT&CK:

--- Extract ATT&CK technique mappings from Splunk ES correlation searches
| rest /services/saved/searches
  splunk_server=local
| where match(title, "^(COR|ESCU|RBA):")

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