micsapp Mapping MITRE ATT&CK Techniques
When to Use
Use this skill when:
- Generating an ATT&CK coverage heatmap to show which techniques your detection stack addresses
- Tagging existing SIEM use cases or Sigma rules with ATT&CK technique IDs for structured reporting
- Aligning your security program roadmap to specific adversary groups known to target your sector
Do not use this skill for real-time incident triage — ATT&CK mapping is an analytical activity best performed post-detection or during threat hunting planning.
Prerequisites
- Access to MITRE ATT&CK knowledge base (https://attack.mitre.org) or local ATT&CK STIX data bundle
- ATT&CK Navigator web app or local installation (https://mitre-attack.github.io/attack-navigator/)
- Inventory of existing detection rules (Sigma, Splunk, Sentinel KQL) to assess current coverage
- ATT&CK Python library:
pip install mitreattack-python
Workflow
Step 1: Obtain Current ATT&CK Data
Download the latest ATT&CK STIX bundle for the relevant matrix (Enterprise, Mobile, ICS):
curl -o enterprise-attack.json \
https://raw.githubusercontent.com/mitre/cti/master/enterprise-attack/enterprise-attack.json
Use the mitreattack-python library to query techniques programmatically:
from mitreattack.stix20 import MitreAttackData
mitre = MitreAttackData("enterprise-attack.json")
techniques = mitre.get_techniques(remove_revoked_deprecated=True)
for t in techniques[:5]:
print(t["external_references"][0]["external_id"], t["name"])
Step 2: Map Existing Detections to Techniques
For each SIEM rule or Sigma file, assign ATT&CK technique IDs. Sigma rules support native ATT&CK tagging:
tags:
- attack.execution
- attack.t1059.001 # PowerShell
- attack.t1059.003 # Windows Command Shell
Create a coverage matrix: list each technique ID and mark as: Detected (alert fires), Logged (data present but no alert), Blind (no data source).
Step 3: Prioritize Coverage Gaps Using Threat Intelligence
Cross-reference coverage gaps with adversary groups targeting your sector. Use ATT&CK Groups data:
groups = mitre.get_groups()
apt29 = mitre.get_object_by_attack_id("G0016", "groups")
apt29_techniques = mitre.get_techniques_used_by_group(apt29)
for t in apt29_techniques:
print(t["object"]["external_references"][0]["external_id"])
Prioritize adding detection for techniques used by high-priority threat groups where your coverage is blind.
Step 4: Build Navigator Heatmap
Export coverage scores as ATT&CK Navigator JSON layer:
import json
layer = {
"name": "SOC Detection Coverage Q1 2025",
"versions": {"attack": "14", "navigator": "4.9", "layer": "4.5"},
"domain": "enterprise-attack",
"techniques": [
{"techniqueID": "T1059.001", "score": 100, "comment": "Splunk rule: PS_Encoded_Command"},
{"techniqueID": "T1071.001", "score": 50, "comment": "Logged only, no alert"},
{"techniqueID": "T1055", "score": 0, "comment": "No coverage — blind spot"}
],
"gradient": {"colors": ["#ff6666", "#ffe766", "#8ec843"], "minValue": 0, "maxValue": 100}
}
with open("coverage_layer.json", "w") as f:
json.dump(layer, f)
Import layer into ATT&CK Navigator (https://mitre-attack.github.io/attack-navigator/) for visualization.
Step 5: Generate Executive Coverage Report
Summarize coverage by tactic category (Initial Access, Execution, Persistence, etc.) with counts and percentages. Provide a risk-ranked list of top 10 blind-spot techniques based on adversary group usage frequency. Recommend data source additions (e.g., "Enable PowerShell Script Block Logging to address 12 Execution sub-technique gaps").
Key Concepts
| Term | Definition |
|---|---|
| ATT&CK Technique | Specific adversary method identified by T-number (e.g., T1059 = Command and Scripting Interpreter) |
| Sub-technique | More granular variant of a technique (e.g., T1059.001 = PowerShell, T1059.003 = Windows Command Shell) |
| Tactic | Adversary goal category in ATT&CK: Initial Access, Execution, Persistence, Privilege Escalation, Defense Evasion, Credential Access, Discovery, Lateral Movement, Collection, C&C, Exfiltration, Impact |
| Data Source | ATT&CK v10+ component identifying telemetry required to detect a technique (e.g., Process Creation, Network Traffic) |
| Coverage Score | Numeric (0–100) representing detection completeness for a technique: 0=blind, 50=logged only, 100=alerted |
| MITRE D3FEND | Defensive countermeasure ontology complementing ATT&CK — maps defensive techniques to attack techniques they mitigate |
Tools & Systems
- ATT&CK Navigator: Browser-based heatmap visualization tool for layering coverage scores and annotations on the ATT&CK matrix
- mitreattack-python: Official MITRE Python library for programmatic access to ATT&CK STIX data (techniques, groups, software, mitigations)
- Atomic Red Team: MITRE-aligned test library providing atomic test cases to validate detection for each technique
- Sigma: Detection rule format with ATT&CK tagging support; translatable to Splunk, Sentinel, QRadar, Elastic
- ATT&CK Workbench: Self-hosted ATT&CK knowledge base for organizations maintaining custom technique extensions
Common Pitfalls
- Over-claiming coverage: Logging a data source (e.g., process creation events) does not mean the associated technique is detected — a rule must actually fire on malicious patterns.
- Mapping at tactic level only: Tagging a rule as "attack.execution" without a specific technique ID prevents granular gap analysis.
- Ignoring sub-techniques: Many adversaries use specific sub-techniques. Coverage of T1059 (parent) doesn't imply coverage of T1059.005 (Visual Basic).
- Static mapping without updates: ATT&CK releases major versions annually. Coverage maps go stale as techniques are added, revised, or deprecated.
- Not mapping to adversary groups: Generic coverage maps don't distinguish between techniques used by APTs targeting your sector vs. commodity malware.