{
  "node_id": "mitre-atlas-mitigation-deepfake-detection",
  "title": "MITRE ATLAS Mitigation Deepfake Detection (AML.M0034) - Deepfake Detection at AI System Boundaries",
  "domain": "AI Governance & Law",
  "version": "1.0.1",
  "last_updated": "2026-07-22",
  "bluf": "This node operationalises MITRE ATLAS mitigation AML.M0034 (Deepfake Detection). Apply deepfake detection algorithms against any untrusted or user-provided data, especially in impactful applications such as biometric verification, to block generated content. Detectors may use a combination of approaches, including: - AI models trained to differentiate between real and deepfake content. - Identifying common inconsistencies in deepfake content, such as unnatural facial movements, audio mismatches, or pixel-level artifacts. - Biometrics analysis, such blinking, eye movements, and microexpressions. It maps the mitigation to EU AI Act, NIST AI RMF, and ISO/IEC 42001 obligations and drives a 10-gate verification workflow that an AI agent can execute against any production system.",
  "paywall": {
    "status": "LOCKED",
    "unlock_cost_usd": "0.01",
    "skyfire_id": "41779894-ece2-4163-9761-b3b1b76e19b0"
  },
  "crosswalks": {
    "_available_keys": [
      "nist_framework",
      "iso_standard",
      "industry_mapping",
      "ai_overlay_2026"
    ],
    "_note": "Full crosswalk values included in vault response"
  },
  "dependencies": [
    "nist-ai-rmf-1-0",
    "eu-ai-act-2024",
    "iso-iec-42001-ai-management-system-2023"
  ],
  "primary_citations_count": 6
}