{
  "node_id": "automated-fact-verification",
  "title": "Deterministic RAG Verification",
  "domain": "AI Governance & Law",
  "version": "1.1.1",
  "last_updated": "2026-04-30",
  "bluf": "Deterministic RAG (Retrieval-Augmented Generation) verification is a systematic process for cross-referencing AI-generated claims against authoritative knowledge bases to detect and block hallucinated, fabricated, or unsupported outputs before they reach end users. The process extracts discrete factual claims from model outputs, retrieves supporting or contradicting evidence from verified knowledge sources, computes an entailment score for each claim, and either passes, flags, or blocks the response based on configurable confidence thresholds. This approach is aligned with NIST AI RMF MEASURE function requirements for AI output accuracy, the EU AI Act Article 13 transparency requirements, and emerging RAG security best practices addressing prompt injection and knowledge base poisoning. Failure to implement fact verification in high-stakes AI deployments (medical, legal, financial) can result in actionable misinformation, regulatory liability, and loss of user trust.",
  "paywall": {
    "status": "LOCKED",
    "unlock_cost_usd": "0.01",
    "skyfire_id": "41779894-ece2-4163-9761-b3b1b76e19b0"
  },
  "crosswalks": {
    "_available_keys": [
      "nist_framework",
      "iso_standard",
      "ai_overlay_2026",
      "industry_mapping"
    ],
    "_note": "Full crosswalk values included in vault response"
  },
  "dependencies": [
    "nist-ai-rmf-1-0",
    "eu-ai-act-high-risk",
    "iso-42001-risk-assess",
    "owasp-agentic-top10"
  ],
  "primary_citations_count": 6
}