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Use when SOC teams need to align detection engineering with threat landscape, conduct threat assessments for new environments, or justify security tool procurement.","Horonomos-laps\u002FYana-AI","Horonomos-laps","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F355299122?v=4","01M76B3QPXE88ET2SQPDPVATZT"," Horonomos-laps","01M72ZQRJPCYPSSWT4D00VJR2A",13,"Apache-2.0","https:\u002F\u002Fgithub.com\u002Fyanacuti1121\u002FYana-AI\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fimplementing-threat-modeling-with-mitre-attack\u002FLICENSE","https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fimplementing-threat-modeling-with-mitre-attack\u002FSKILL.md",[],[],"2026-11-11 15:11:14",false,"---\nname: implementing-threat-modeling-with-mitre-attack\ndescription: 'Implements threat modeling using the MITRE ATT&CK framework to map adversary\n  TTPs against organizational assets, assess detection coverage gaps, and prioritize\n  defensive investments. Use when SOC teams need to align detection engineering with\n  threat landscape, conduct threat assessments for new environments, or justify security\n  tool procurement.\n\n  '\ndomain: cybersecurity\nsubdomain: soc-operations\ntags:\n- soc\n- mitre-attack\n- threat-modeling\n- ttp\n- detection-coverage\n- attack-navigator\n- risk-assessment\nversion: '1.0'\nauthor: mahipal\nlicense: Apache-2.0\nnist_ai_rmf:\n- MEASURE-2.7\n- MAP-5.1\n- MANAGE-2.4\natlas_techniques:\n- AML.T0070\n- AML.T0066\n- AML.T0082\nd3fend_techniques:\n- File Metadata Consistency Validation\n- Application Protocol Command Analysis\n- Identifier Analysis\n- Content Format Conversion\n- Message Analysis\nnist_csf:\n- DE.CM-01\n- DE.AE-02\n- RS.MA-01\n- DE.AE-06\nmitre_attack:\n- T1078\n- T1685.002\n- T1685.005\n- T1566\nsource: https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\nsource_commit: 04450304b12645cb2b974ab96d28c0664758a88d\nnote: \"Vendored verbatim from an external Apache-2.0 security-skill library, pinned by commit. Exceeds the internal 300-line skill guideline (agent-code-constraints.md) -- kept as-is because this is vendored reference material (forensics\u002Fthreat-intel procedure), not Yana AI-authored content, and trimming would damage technical accuracy.\"\n---\n# Implementing Threat Modeling with MITRE ATT&CK\n\n## When to Use\n\nUse this skill when:\n- SOC teams need to assess detection coverage against relevant threat actors and their TTPs\n- Security leadership requires threat-informed defense prioritization\n- New environments (cloud migration, OT integration) need detection strategy planning\n- Purple team exercises require structured adversary emulation based on threat models\n- Annual risk assessments need ATT&CK-based threat landscape analysis\n\n**Do not use** as a one-time exercise — threat models must be continuously updated as adversary TTPs evolve and organizational attack surface changes.\n\n## Prerequisites\n\n- MITRE ATT&CK framework knowledge (Enterprise, ICS, Mobile, or Cloud matrices)\n- ATT&CK Navigator tool (web or local) for layer visualization\n- Current detection rule inventory mapped to ATT&CK technique IDs\n- Threat intelligence on adversary groups targeting your sector\n- Organizational asset inventory with criticality classifications\n\n## Workflow\n\n### Step 1: Identify Relevant Threat Actors\n\nResearch adversary groups targeting your sector using MITRE ATT&CK Groups:\n\n```python\nimport requests\nimport json\n\n# Download ATT&CK STIX data\nresponse = requests.get(\n    \"https:\u002F\u002Fraw.githubusercontent.com\u002Fmitre\u002Fcti\u002Fmaster\u002Fenterprise-attack\u002Fenterprise-attack.json\"\n)\nattack_data = response.json()\n\n# Extract groups and their techniques\ngroups = {}\nfor obj in attack_data[\"objects\"]:\n    if obj[\"type\"] == \"intrusion-set\":\n        group_name = obj[\"name\"]\n        aliases = obj.get(\"aliases\", [])\n        description = obj.get(\"description\", \"\")\n        groups[group_name] = {\n            \"aliases\": aliases,\n            \"description\": description[:200],\n            \"techniques\": []\n        }\n\n# Map techniques to groups via relationships\nrelationships = [obj for obj in attack_data[\"objects\"] if obj[\"type\"] == \"relationship\"]\ntechniques = {obj[\"id\"]: obj for obj in attack_data[\"objects\"]\n              if obj[\"type\"] == \"attack-pattern\"}\n\nfor rel in relationships:\n    if rel[\"relationship_type\"] == \"uses\":\n        source = rel[\"source_ref\"]\n        target = rel[\"target_ref\"]\n        for group_name, group_data in groups.items():\n            if source == group_data.get(\"id\") and target in techniques:\n                tech = techniques[target]\n                ext_refs = tech.get(\"external_references\", [])\n                for ref in ext_refs:\n                    if ref.get(\"source_name\") == \"mitre-attack\":\n                        group_data[\"techniques\"].append(ref[\"external_id\"])\n\n# Example: Financial sector threat actors\nfinancial_actors = [\"FIN7\", \"FIN8\", \"Carbanak\", \"APT38\", \"Lazarus Group\"]\nfor actor in financial_actors:\n    if actor in groups:\n        print(f\"{actor}: {len(groups[actor]['techniques'])} techniques\")\n        print(f\"  Top techniques: {groups[actor]['techniques'][:10]}\")\n```\n\n### Step 2: Build Threat Actor TTP Profile\n\nCreate ATT&CK Navigator layers for priority threat actors:\n\n```python\nimport json\n\ndef create_attack_layer(actor_name, techniques, color=\"#ff6666\"):\n    \"\"\"Generate ATT&CK Navigator JSON layer for a threat actor\"\"\"\n    layer = {\n        \"name\": f\"{actor_name} TTP Profile\",\n        \"versions\": {\n            \"attack\": \"15\",\n            \"navigator\": \"5.0\",\n            \"layer\": \"4.5\"\n        },\n        \"domain\": \"enterprise-attack\",\n        \"description\": f\"Techniques associated with {actor_name}\",\n        \"techniques\": [\n            {\n                \"techniqueID\": tech_id,\n                \"tactic\": \"\",\n                \"color\": color,\n                \"comment\": f\"Used by {actor_name}\",\n                \"enabled\": True,\n                \"score\": 1\n            }\n            for tech_id in techniques\n        ],\n        \"gradient\": {\n            \"colors\": [\"#ffffff\", color],\n            \"minValue\": 0,\n            \"maxValue\": 1\n        }\n    }\n    return layer\n\n# Create layers for top threat actors\nfin7_techniques = [\"T1566.001\", \"T1059.001\", \"T1053.005\", \"T1547.001\",\n                    \"T1078\", \"T1021.001\", \"T1003\", \"T1071.001\", \"T1041\"]\nlayer = create_attack_layer(\"FIN7\", fin7_techniques, \"#ff6666\")\n\nwith open(\"fin7_layer.json\", \"w\") as f:\n    json.dump(layer, f, indent=2)\n```\n\n### Step 3: Map Current Detection Coverage\n\nExport current detection rules mapped to ATT&CK:\n\n```spl\n--- Extract ATT&CK technique mappings from Splunk ES correlation searches\n| rest \u002Fservices\u002Fsaved\u002Fsearches\n  splunk_server=local\n| where match(title, \"^(COR|ESCU|RBA):\")\n| eval techniques = if(isnotnull(action.correlationsearch.annotations),\n                       spath(action.correlationsearch.annotations, \"mitre_attack\"),\n                       \"unmapped\")\n| stats count by techniques\n| mvexpand techniques\n| stats count by techniques\n| rename techniques AS technique_id, count AS rule_count\n```\n\nCreate detection coverage layer:\n\n```python\ndef create_coverage_layer(detection_rules):\n    \"\"\"Generate coverage layer from detection rule inventory\"\"\"\n    technique_counts = {}\n    for rule in detection_rules:\n        for tech in rule.get(\"techniques\", []):\n            technique_counts[tech] = technique_counts.get(tech, 0) + 1\n\n    layer = {\n        \"name\": \"SOC Detection Coverage\",\n        \"versions\": {\"attack\": \"15\", \"navigator\": \"5.0\", \"layer\": \"4.5\"},\n        \"domain\": \"enterprise-attack\",\n        \"techniques\": [\n            {\n                \"techniqueID\": tech_id,\n                \"color\": \"#31a354\" if count >= 2 else \"#a1d99b\" if count == 1 else \"\",\n                \"score\": count,\n                \"comment\": f\"{count} detection rule(s)\"\n            }\n            for tech_id, count in technique_counts.items()\n        ],\n        \"gradient\": {\n            \"colors\": [\"#ffffff\", \"#a1d99b\", \"#31a354\"],\n            \"minValue\": 0,\n            \"maxValue\": 3\n        }\n    }\n    return layer\n```\n\n### Step 4: Perform Gap Analysis\n\nOverlay threat actor TTPs against detection coverage:\n\n```python\ndef gap_analysis(threat_techniques, covered_techniques):\n    \"\"\"Identify detection gaps for specific threat actor\"\"\"\n    gaps = set(threat_techniques) - set(covered_techniques)\n    covered = set(threat_techniques) & set(covered_techniques)\n\n    print(f\"Threat Actor Techniques: {len(threat_techniques)}\")\n    print(f\"Detected: {len(covered)} ({len(covered)\u002Flen(threat_techniques)*100:.0f}%)\")\n    print(f\"Gaps: {len(gaps)} ({len(gaps)\u002Flen(threat_techniques)*100:.0f}%)\")\n\n    # Prioritize gaps by kill chain phase\n    priority_order = {\n        \"TA0001\": 1, \"TA0002\": 2, \"TA0003\": 3, \"TA0004\": 4,\n        \"TA0005\": 5, \"TA0006\": 6, \"TA0007\": 7, \"TA0008\": 8,\n        \"TA0009\": 9, \"TA0010\": 10, \"TA0011\": 11, \"TA0040\": 12\n    }\n\n    gap_details = []\n    for tech_id in gaps:\n        gap_details.append({\n            \"technique\": tech_id,\n            \"priority\": \"HIGH\" if tech_id.split(\".\")[0] in [\"T1003\", \"T1021\", \"T1059\"] else \"MEDIUM\",\n            \"recommendation\": f\"Build detection for {tech_id}\"\n        })\n\n    return {\n        \"total_actor_techniques\": len(threat_techniques),\n        \"covered\": len(covered),\n        \"gaps\": len(gaps),\n        \"coverage_pct\": round(len(covered)\u002Flen(threat_techniques)*100, 1),\n        \"gap_details\": sorted(gap_details, key=lambda x: x[\"priority\"])\n    }\n\n# Run analysis\nresult = gap_analysis(fin7_techniques, current_coverage)\n```\n\n### Step 5: Create Prioritized Remediation Plan\n\nBuild a detection engineering roadmap:\n\n```yaml\nthreat_model_remediation_plan:\n  assessed_date: 2024-03-15\n  primary_threats:\n    - FIN7 (Financial sector)\n    - APT38 (DPRK financial)\n    - Lazarus Group (Destructive)\n\n  current_coverage: 64%\n  target_coverage: 80%\n\n  priority_1_gaps: # 30-day target\n    - technique: T1021.002\n      name: SMB\u002FWindows Admin Shares\n      data_source: Windows Security Event 5140\n      effort: Low\n      detection_approach: Monitor admin share access from non-admin workstations\n\n    - technique: T1003.006\n      name: DCSync\n      data_source: Windows Security Event 4662\n      effort: Medium\n      detection_approach: Detect DS-Replication-Get-Changes from non-DC sources\n\n  priority_2_gaps: # 60-day target\n    - technique: T1055\n      name: Process Injection\n      data_source: Sysmon EventCode 8, 10\n      effort: High\n      detection_approach: Monitor cross-process memory access patterns\n\n    - technique: T1071.001\n      name: Web Protocols (C2)\n      data_source: Proxy\u002FFirewall logs\n      effort: Medium\n      detection_approach: Detect beaconing patterns in HTTP\u002FS traffic\n\n  priority_3_gaps: # 90-day target\n    - technique: T1070.004\n      name: File Deletion\n      data_source: Sysmon EventCode 23\n      effort: Low\n      detection_approach: Monitor mass file deletion in sensitive directories\n```\n\n### Step 6: Validate with Adversary Emulation\n\nTest coverage using MITRE Caldera or Atomic Red Team:\n\n```bash\n# Using Atomic Red Team to validate coverage for FIN7 techniques\n# T1566.001 — Spearphishing Attachment\nInvoke-AtomicTest T1566.001\n\n# T1059.001 — PowerShell\nInvoke-AtomicTest T1059.001 -TestNumbers 1,2,3\n\n# T1053.005 — Scheduled Task\nInvoke-AtomicTest T1053.005\n\n# T1547.001 — Registry Run Keys\nInvoke-AtomicTest T1547.001\n\n# T1003 — Credential Dumping\nInvoke-AtomicTest T1003 -TestNumbers 1,2\n\n# Verify detections\n# Check SIEM for corresponding alerts within 15 minutes\n```\n\nDocument emulation results to validate threat model accuracy.\n\n## Key Concepts\n\n| Term | Definition |\n|------|-----------|\n| **MITRE ATT&CK** | Knowledge base of adversary tactics, techniques, and procedures based on real-world observations |\n| **TTP** | Tactics, Techniques, and Procedures — the behavioral patterns of adversary groups |\n| **ATT&CK Navigator** | Web tool for visualizing ATT&CK matrices as layered heatmaps showing coverage or threat profiles |\n| **Gap Analysis** | Process of comparing threat actor TTPs against detection coverage to identify blind spots |\n| **Threat-Informed Defense** | Security strategy prioritizing defenses based on actual adversary behaviors rather than theoretical risks |\n| **Adversary Emulation** | Controlled simulation of threat actor TTPs to validate detection and response capabilities |\n\n## Tools & Systems\n\n- **MITRE ATT&CK Navigator**: Web-based visualization tool for creating and overlaying ATT&CK technique layers\n- **MITRE Caldera**: Automated adversary emulation platform for testing detection coverage at scale\n- **Atomic Red Team**: Open-source library of ATT&CK technique tests for security control validation\n- **CTID ATT&CK Workbench**: MITRE tool for customizing ATT&CK knowledge base with organizational context\n- **Tidal Cyber**: Commercial platform for threat-informed defense planning using ATT&CK framework\n\n## Common Scenarios\n\n- **Annual Threat Assessment**: Map top 5 threat actors to ATT&CK, overlay against detection, produce gap analysis\n- **Cloud Migration Planning**: Model cloud-specific threats (T1078.004, T1537) and plan detection coverage\n- **M&A Security Assessment**: Threat model the acquired company's environment against relevant threat actors\n- **Budget Justification**: Use gap analysis to demonstrate detection blind spots requiring tool investment\n- **Purple Team Planning**: Select adversary emulation scenarios based on highest-priority gaps from threat model\n\n## Output Format\n\n```\nTHREAT MODEL ASSESSMENT — Financial Services Division\n━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\nDate:             2024-03-15\nThreat Actors:    FIN7, APT38, Lazarus Group\nTechniques Total: 87 unique techniques across all actors\n\nDETECTION COVERAGE:\n  Covered:     56\u002F87 (64%)\n  Gaps:        31\u002F87 (36%)\n\n  Tactic Coverage Breakdown:\n    Initial Access:      78%  ████████░░\n    Execution:           82%  █████████░\n    Persistence:         71%  ████████░░\n    Priv Escalation:     65%  ███████░░░\n    Defense Evasion:     52%  ██████░░░░  \u003C-- Priority gap\n    Credential Access:   58%  ██████░░░░  \u003C-- Priority gap\n    Discovery:           45%  █████░░░░░\n    Lateral Movement:    61%  ███████░░░\n    Collection:          50%  ██████░░░░\n    Exfiltration:        55%  ██████░░░░\n    C2:                  67%  ███████░░░\n\nTOP PRIORITY GAPS (30-day remediation):\n  1. T1055 Process Injection — used by all 3 actors, 0 detections\n  2. T1003.006 DCSync — used by FIN7 and Lazarus, 0 detections\n  3. T1070.004 File Deletion — evidence destruction, 0 detections\n\nINVESTMENT RECOMMENDATION:\n  Closing top 10 gaps requires: 2 detection engineer FTEs, 60 days\n  Expected coverage improvement: 64% -> 76%\n```\n","# Implementing Threat Modeling with MITRE ATT&CK\n\n## When to Use\n\nUse this skill when:\n- SOC teams need to assess detection coverage against relevant threat actors and their TTPs\n- Security leadership requires threat-informed defense prioritization\n- New environments (cloud migration, OT integration) need detection strategy planning\n- Purple team exercises require structured adversary emulation based on threat models\n- Annual risk assessments need ATT&CK-based threat landscape analysis\n\n**Do not use** as a one-time exercise — threat models must be continuously updated as adversary TTPs evolve and organizational attack surface changes.\n\n## Prerequisites\n\n- MITRE ATT&CK framework knowledge (Enterprise, ICS, Mobile, or Cloud matrices)\n- ATT&CK Navigator tool (web or local) for layer visualization\n- Current detection rule inventory mapped to ATT&CK technique IDs\n- Threat intelligence on adversary groups targeting your sector\n- Organizational asset inventory with criticality classifications\n\n## Workflow\n\n### Step 1: Identify Relevant Threat Actors\n\nResearch adversary groups targeting your sector using MITRE ATT&CK Groups:\n\n```python\nimport requests\nimport json\n\n# Download ATT&CK STIX data\nresponse = requests.get(\n    \"https:\u002F\u002Fraw.githubusercontent.com\u002Fmitre\u002Fcti\u002Fmaster\u002Fenterprise-attack\u002Fenterprise-attack.json\"\n)\nattack_data = response.json()\n\n# Extract groups and their techniques\ngroups = {}\nfor obj in attack_data[\"objects\"]:\n    if obj[\"type\"] == \"intrusion-set\":\n        group_name = obj[\"name\"]\n        aliases = obj.get(\"aliases\", [])\n        description = obj.get(\"description\", \"\")\n        groups[group_name] = {\n            \"aliases\": aliases,\n            \"description\": description[:200],\n            \"techniques\": []\n        }\n\n# Map techniques to groups via relationships\nrelationships = [obj for obj in attack_data[\"objects\"] if obj[\"type\"] == \"relationship\"]\ntechniques = {obj[\"id\"]: obj for obj in attack_data[\"objects\"]\n              if obj[\"type\"] == \"attack-pattern\"}\n\nfor rel in relationships:\n    if rel[\"relationship_type\"] == \"uses\":\n        source = rel[\"source_ref\"]\n        target = rel[\"target_ref\"]\n        for group_name, group_data in groups.items():\n            if source == group_data.get(\"id\") and target in techniques:\n                tech = techniques[target]\n                ext_refs = tech.get(\"external_references\", [])\n                for ref in ext_refs:\n                    if ref.get(\"source_name\") == \"mitre-attack\":\n                        group_data[\"techniques\"].append(ref[\"external_id\"])\n\n# Example: Financial sector threat actors\nfinancial_actors = [\"FIN7\", \"FIN8\", \"Carbanak\", \"APT38\", \"Lazarus Group\"]\nfor actor in financial_actors:\n    if actor in groups:\n        print(f\"{actor}: {len(groups[actor]['techniques'])} techniques\")\n        print(f\"  Top techniques: {groups[actor]['techniques'][:10]}\")\n```\n\n### Step 2: Build Threat Actor TTP Profile\n\nCreate ATT&CK Navigator layers for priority threat actors:\n\n```python\nimport json\n\ndef create_attack_layer(actor_name, techniques, color=\"#ff6666\"):\n    \"\"\"Generate ATT&CK Navigator JSON layer for a threat actor\"\"\"\n    layer = {\n        \"name\": f\"{actor_name} TTP Profile\",\n        \"versions\": {\n            \"attack\": \"15\",\n            \"navigator\": \"5.0\",\n            \"layer\": \"4.5\"\n        },\n        \"domain\": \"enterprise-attack\",\n        \"description\": f\"Techniques associated with {actor_name}\",\n        \"techniques\": [\n            {\n                \"techniqueID\": tech_id,\n                \"tactic\": \"\",\n                \"color\": color,\n                \"comment\": f\"Used by {actor_name}\",\n                \"enabled\": True,\n                \"score\": 1\n            }\n            for tech_id in techniques\n        ],\n        \"gradient\": {\n            \"colors\": [\"#ffffff\", color],\n            \"minValue\": 0,\n            \"maxValue\": 1\n        }\n    }\n    return layer\n\n# Create layers for top threat actors\nfin7_techniques = [\"T1566.001\", \"T1059.001\", \"T1053.005\", \"T1547.001\",\n                    \"T1078\", \"T1021.001\", \"T1003\", \"T1071.001\", \"T1041\"]\nlayer = create_attack_layer(\"FIN7\", fin7_techniques, \"#ff6666\")\n\nwith open(\"fin7_layer.json\", \"w\") as f:\n    json.dump(layer, f, indent=2)\n```\n\n### Step 3: Map Current Detection Coverage\n\nExport current detection rules mapped to ATT&CK:\n\n```spl\n--- Extract ATT&CK technique mappings from Splunk ES correlation searches\n| rest \u002Fservices\u002Fsaved\u002Fsearches\n  splunk_server=local\n| where match(title, \"^(COR|ESCU|RBA):\")\n| eval techniques = if(isnotnull(action.correlationsearch.annotations),\n                       spath(action.correlationsearch.annotations, \"mitre_attack\"),\n                       \"unmapped\")\n| stats count by techniques\n| mvexpand techniques\n| stats count by techniques\n| rename techniques AS technique_id, count AS rule_count\n```\n\nCreate detection coverage layer:\n\n```python\ndef create_coverage_layer(detection_rules):\n    \"\"\"Generate coverage layer from detection rule inventory\"\"\"\n    technique_counts = {}\n    for rule in detection_rules:\n        for tech in rule.get(\"techniques\", []):\n            technique_counts[tech] = technique_counts.get(tech, 0) + 1\n\n    layer = {\n        \"name\": \"SOC Detection Coverage\",\n        \"versions\": {\"attack\": \"15\", \"navigator\": \"5.0\", \"layer\": \"4.5\"},\n        \"domain\": \"enterprise-attack\",\n        \"techniques\": [\n            {\n                \"techniqueID\": tech_id,\n                \"color\": \"#31a354\" if count >= 2 else \"#a1d99b\" if count == 1 else \"\",\n                \"score\": count,\n                \"comment\": f\"{count} detection rule(s)\"\n            }\n            for tech_id, count in technique_counts.items()\n        ],\n        \"gradient\": {\n            \"colors\": [\"#ffffff\", \"#a1d99b\", \"#31a354\"],\n            \"minValue\": 0,\n            \"maxValue\": 3\n        }\n    }\n    return layer\n```\n\n### Step 4: Perform Gap Analysis\n\nOverlay threat actor TTPs against detection coverage:\n\n```python\ndef gap_analysis(threat_techniques, covered_techniques):\n    \"\"\"Identify detection gaps for specific threat actor\"\"\"\n    gaps = set(threat_techniques) - set(covered_techniques)\n    covered = set(threat_techniques) & set(covered_techniques)\n\n    print(f\"Threat Actor Techniques: {len(threat_techniques)}\")\n    print(f\"Detected: {len(covered)} ({len(covered)\u002Flen(threat_techniques)*100:.0f}%)\")\n    print(f\"Gaps: {len(gaps)} ({len(gaps)\u002Flen(threat_techniques)*100:.0f}%)\")\n\n    # Prioritize gaps by kill chain phase\n    priority_order = {\n        \"TA0001\": 1, \"TA0002\": 2, \"TA0003\": 3, \"TA0004\": 4,\n        \"TA0005\": 5, \"TA0006\": 6, \"TA0007\": 7, \"TA0008\": 8,\n        \"TA0009\": 9, \"TA0010\": 10, \"TA0011\": 11, \"TA0040\": 12\n    }\n\n    gap_details = []\n    for tech_id in gaps:\n        gap_details.append({\n            \"technique\": tech_id,\n            \"priority\": \"HIGH\" if tech_id.split(\".\")[0] in [\"T1003\", \"T1021\", \"T1059\"] else \"MEDIUM\",\n            \"recommendation\": f\"Build detection for {tech_id}\"\n        })\n\n    return {\n        \"total_actor_techniques\": len(threat_techniques),\n        \"covered\": len(covered),\n        \"gaps\": len(gaps),\n        \"coverage_pct\": round(len(covered)\u002Flen(threat_techniques)*100, 1),\n        \"gap_details\": sorted(gap_details, key=lambda x: x[\"priority\"])\n    }\n\n# Run analysis\nresult = gap_analysis(fin7_techniques, current_coverage)\n```\n\n### Step 5: Create Prioritized Remediation Plan\n\nBuild a detection engineering roadmap:\n\n```yaml\nthreat_model_remediation_plan:\n  assessed_date: 2024-03-15\n  primary_threats:\n    - FIN7 (Financial sector)\n    - APT38 (DPRK financial)\n    - Lazarus Group (Destructive)\n\n  current_coverage: 64%\n  target_coverage: 80%\n\n  priority_1_gaps: # 30-day target\n    - technique: T1021.002\n      name: SMB\u002FWindows Admin Shares\n      data_source: Windows Security Event 5140\n      effort: Low\n      detection_approach: Monitor admin share access from non-admin workstations\n\n    - technique: T1003.006\n      name: DCSync\n      data_source: Windows Security Event 4662\n      effort: Medium\n      detection_approach: Detect DS-Replication-Get-Changes from non-DC sources\n\n  priority_2_gaps: # 60-day target\n    - technique: T1055\n      name: Process Injection\n      data_source: Sysmon EventCode 8, 10\n      effort: High\n      detection_approach: Monitor cross-process memory access patterns\n\n    - technique: T1071.001\n      name: Web Protocols (C2)\n      data_source: Proxy\u002FFirewall logs\n      effort: Medium\n      detection_approach: Detect beaconing patterns in HTTP\u002FS traffic\n\n  priority_3_gaps: # 90-day target\n    - technique: T1070.004\n      name: File Deletion\n      data_source: Sysmon EventCode 23\n      effort: Low\n      detection_approach: Monitor mass file deletion in sensitive directories\n```\n\n### Step 6: Validate with Adversary Emulation\n\nTest coverage using MITRE Caldera or Atomic Red Team:\n\n```bash\n# Using Atomic Red Team to validate coverage for FIN7 techniques\n# T1566.001 — Spearphishing Attachment\nInvoke-AtomicTest T1566.001\n\n# T1059.001 — PowerShell\nInvoke-AtomicTest T1059.001 -TestNumbers 1,2,3\n\n# T1053.005 — Scheduled Task\nInvoke-AtomicTest T1053.005\n\n# T1547.001 — Registry Run Keys\nInvoke-AtomicTest T1547.001\n\n# T1003 — Credential Dumping\nInvoke-AtomicTest T1003 -TestNumbers 1,2\n\n# Verify detections\n# Check SIEM for corresponding alerts within 15 minutes\n```\n\nDocument emulation results to validate threat model accuracy.\n\n## Key Concepts\n\n| Term | Definition |\n|------|-----------|\n| **MITRE ATT&CK** | Knowledge base of adversary tactics, techniques, and procedures based on real-world observations |\n| **TTP** | Tactics, Techniques, and Procedures — the behavioral patterns of adversary groups |\n| **ATT&CK Navigator** | Web tool for visualizing ATT&CK matrices as layered heatmaps showing coverage or threat profiles |\n| **Gap Analysis** | Process of comparing threat actor TTPs against detection coverage to identify blind spots |\n| **Threat-Informed Defense** | Security strategy prioritizing defenses based on actual adversary behaviors rather than theoretical risks |\n| **Adversary Emulation** | Controlled simulation of threat actor TTPs to validate detection and response capabilities |\n\n## Tools & Systems\n\n- **MITRE ATT&CK Navigator**: Web-based visualization tool for creating and overlaying ATT&CK technique layers\n- **MITRE Caldera**: Automated adversary emulation platform for testing detection coverage at scale\n- **Atomic Red Team**: Open-source library of ATT&CK technique tests for security control validation\n- **CTID ATT&CK Workbench**: MITRE tool for customizing ATT&CK knowledge base with organizational context\n- **Tidal Cyber**: Commercial platform for threat-informed defense planning using ATT&CK framework\n\n## Common Scenarios\n\n- **Annual Threat Assessment**: Map top 5 threat actors to ATT&CK, overlay against detection, produce gap analysis\n- **Cloud Migration Planning**: Model cloud-specific threats (T1078.004, T1537) and plan detection coverage\n- **M&A Security Assessment**: Threat model the acquired company's environment against relevant threat actors\n- **Budget Justification**: Use gap analysis to demonstrate detection blind spots requiring tool investment\n- **Purple Team Planning**: Select adversary emulation scenarios based on highest-priority gaps from threat model\n\n## Output Format\n\n```\nTHREAT MODEL ASSESSMENT — Financial Services Division\n━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\nDate:             2024-03-15\nThreat Actors:    FIN7, APT38, Lazarus Group\nTechniques Total: 87 unique techniques across all actors\n\nDETECTION COVERAGE:\n  Covered:     56\u002F87 (64%)\n  Gaps:        31\u002F87 (36%)\n\n  Tactic Coverage Breakdown:\n    Initial Access:      78%  ████████░░\n    Execution:           82%  █████████░\n    Persistence:         71%  ████████░░\n    Priv Escalation:     65%  ███████░░░\n    Defense Evasion:     52%  ██████░░░░  \u003C-- Priority gap\n    Credential Access:   58%  ██████░░░░  \u003C-- Priority gap\n    Discovery:           45%  █████░░░░░\n    Lateral Movement:    61%  ███████░░░\n    Collection:          50%  ██████░░░░\n    Exfiltration:        55%  ██████░░░░\n    C2:                  67%  ███████░░░\n\nTOP PRIORITY GAPS (30-day remediation):\n  1. T1055 Process Injection — used by all 3 actors, 0 detections\n  2. T1003.006 DCSync — used by FIN7 and Lazarus, 0 detections\n  3. T1070.004 File Deletion — evidence destruction, 0 detections\n\nINVESTMENT RECOMMENDATION:\n  Closing top 10 gaps requires: 2 detection engineer FTEs, 60 days\n  Expected coverage improvement: 64% -> 76%\n```\n",{"name":5,"note":25,"tags":26,"author":34,"domain":35,"source":36,"license":15,"version":37,"nist_csf":38,"subdomain":43,"description":44,"nist_ai_rmf":45,"mitre_attack":49,"source_commit":54,"atlas_techniques":55,"d3fend_techniques":59},"Vendored verbatim from an external Apache-2.0 security-skill library, pinned by commit. Exceeds the internal 300-line skill guideline (agent-code-constraints.md) -- kept as-is because this is vendored reference material (forensics\u002Fthreat-intel procedure), not Yana AI-authored content, and trimming would damage technical accuracy.",[27,28,29,30,31,32,33],"soc","mitre-attack","threat-modeling","ttp","detection-coverage","attack-navigator","risk-assessment","mahipal","cybersecurity","https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills","1.0",[39,40,41,42],"DE.CM-01","DE.AE-02","RS.MA-01","DE.AE-06","soc-operations","Implements threat modeling using the MITRE ATT&CK framework to map adversary TTPs against organizational assets, assess detection coverage gaps, and prioritize defensive investments. Use when SOC teams need to align detection engineering with threat landscape, conduct threat assessments for new environments, or justify security tool procurement.\n",[46,47,48],"MEASURE-2.7","MAP-5.1","MANAGE-2.4",[50,51,52,53],"T1078","T1685.002","T1685.005","T1566","04450304b12645cb2b974ab96d28c0664758a88d",[56,57,58],"AML.T0070","AML.T0066","AML.T0082",[60,61,62,63,64],"File Metadata Consistency Validation","Application Protocol Command Analysis","Identifier Analysis","Content Format Conversion","Message Analysis","main","481dcd7dd2e288b9d627a2bd017db70e64e6c6fa",".claude\u002Fskills\u002Fimplementing-threat-modeling-with-mitre-attack",2,"https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI","https:\u002F\u002Fyana.vutam.link\u002F","https:\u002F\u002Fgithub.com\u002FHoronomos-laps","https:\u002F\u002Fyana.vutam.link","An open-source governed runtime & execution environment for multi-agent AI systems.","2026-11-14 13:24:09",{"source":8,"skillName":5,"commands":76,"prompt":86,"zipAvailable":87},[77,80,83],{"tool":78,"command":79},"npx","npx skills add Horonomos-laps\u002FYana-AI --skill implementing-threat-modeling-with-mitre-attack",{"tool":81,"command":82},"bunx","bunx skills add Horonomos-laps\u002FYana-AI --skill implementing-threat-modeling-with-mitre-attack",{"tool":84,"command":85},"pnpm","pnpm dlx skills add Horonomos-laps\u002FYana-AI --skill implementing-threat-modeling-with-mitre-attack","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fimplementing-threat-modeling-with-mitre-attack\u002FSKILL.md",true,[89,93,99],{"path":90,"resourceType":90,"resourceTypeDesc":91,"sizeBytes":92,"sourceHtmlUrl":16},"LICENSE","许可证",11255,{"path":94,"resourceType":95,"resourceTypeDesc":96,"sizeBytes":97,"sourceHtmlUrl":98},"references\u002Fapi-reference.md","REFERENCE","参考文档",2264,"https:\u002F\u002Fgithub.com\u002Fyanacuti1121\u002FYana-AI\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fimplementing-threat-modeling-with-mitre-attack\u002Freferences\u002Fapi-reference.md",{"path":100,"resourceType":101,"resourceTypeDesc":102,"sizeBytes":103,"sourceHtmlUrl":104},"scripts\u002Fagent.py","SCRIPT","脚本",8297,"https:\u002F\u002Fgithub.com\u002Fyanacuti1121\u002FYana-AI\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fimplementing-threat-modeling-with-mitre-attack\u002Fscripts\u002Fagent.py",[106,114,121,129,137,145],{"skillNo":107,"name":108,"nameZh":6,"description":109,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":110,"licenseFileUrl":6,"sourceHtmlUrl":111,"categories":112,"keywords":113,"publishedAt":20},"01M735GKET5YVYKAY62W2P5YXB","openai--sentry--sentry","Use when the user asks to inspect Sentry issues or events, summarize recent production errors, or pull basic Sentry health data via the Sentry API; perform read-only queries with the bundled script and require `SENTRY_AUTH_TOKEN`.","MIT","https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fopenai--sentry--sentry\u002FSKILL.md",[],[],{"skillNo":115,"name":116,"nameZh":6,"description":117,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":118,"categories":119,"keywords":120,"publishedAt":20},"01M73DCAP83T17HY7SMGK7MGFE","ito-basket-compare","Compare Itô prediction-market baskets against a user's knowledge base, portfolio notes, financial context, watchlist, or research thesis. Use for read-only basket comparison and gap analysis without investment advice or live trading.","https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002Fcore\u002Fskills\u002Fito-basket-compare\u002FSKILL.md",[],[],{"skillNo":122,"name":123,"nameZh":6,"description":124,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":125,"sourceHtmlUrl":126,"categories":127,"keywords":128,"publishedAt":20},"01M734CYEMZFDR3KH6SW0JP8TD","implementing-network-traffic-baselining","Build network traffic baselines from NetFlow\u002FIPFIX data using Python pandas for statistical analysis, z-score anomaly detection, and hourly\u002Fdaily traffic pattern profiling","https:\u002F\u002Fgithub.com\u002Fyanacuti1121\u002FYana-AI\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fimplementing-network-traffic-baselining\u002FLICENSE","https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fimplementing-network-traffic-baselining\u002FSKILL.md",[],[],{"skillNo":130,"name":131,"nameZh":6,"description":132,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":133,"sourceHtmlUrl":134,"categories":135,"keywords":136,"publishedAt":20},"01M73ASZ6NJRJM44Z2P84NGQ3Q","detecting-evasion-techniques-in-endpoint-logs","Detects defense evasion techniques used by adversaries in endpoint logs including log tampering, timestomping, process injection, and security tool disabling. Use when investigating suspicious endpoint behavior, building detection rules for evasion tactics, or conducting threat hunting for stealthy adversary activity. Activates for requests involving evasion detection, defense evasion analysis, log tampering detection, or MITRE ATT&CK TA0005.","https:\u002F\u002Fgithub.com\u002Fyanacuti1121\u002FYana-AI\u002Fblob\u002Fmain\u002Fcore\u002Fskills\u002Fdetecting-evasion-techniques-in-endpoint-logs\u002FLICENSE","https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002Fcore\u002Fskills\u002Fdetecting-evasion-techniques-in-endpoint-logs\u002FSKILL.md",[],[],{"skillNo":138,"name":139,"nameZh":6,"description":140,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":141,"sourceHtmlUrl":142,"categories":143,"keywords":144,"publishedAt":20},"01M734AFNXD06Z78QRK0VKGT7Q","implementing-microsegmentation-with-guardicore","Implementing microsegmentation using Akamai Guardicore Segmentation to map application dependencies, create granular network policies, visualize east-west traffic flows, and enforce least-privilege communication between workloads across data centers and cloud.","https:\u002F\u002Fgithub.com\u002Fyanacuti1121\u002FYana-AI\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fimplementing-microsegmentation-with-guardicore\u002FLICENSE","https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fimplementing-microsegmentation-with-guardicore\u002FSKILL.md",[],[],{"skillNo":146,"name":147,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":148,"categories":149,"keywords":150,"publishedAt":20},"01M736TK27QMCXK0DVCH25SJN5","ramdisk-tmpfs-patterns","https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Framdisk-tmpfs-patterns\u002FSKILL.md",[],[]]