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):

bash
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:

python
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:

yaml
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:

python
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:

python
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.