VELARU MANDATE EXHIBIT PACK — DATA POISONING / SUPPLY CHAIN
Jurisdiction: New York (us_ny)
Modality: Text / Chat
Program: Velaru Data Poisoning / Supply Chain · New York (galactic_data_poisoning_us_ny)
Product ID: data_poisoning:us_ny:bundle:text
Vertical: data_poisoning
Generated: 2026-08-19T03:16:53.524259Z
Authority: New York AI Governance Pack — Data Integrity + Poisoning Detection Pack
Deadline: NYDFS AI guidance + Local Law 144 enforcement
Velaru verify: https://velaru-erra.onrender.com/verify

EXHIBIT A — AI INVENTORY
[]

EXHIBIT B — GOVERNANCE FRAMEWORK
{
  "framework": "Velaru Mandate Registry \u2014 Data Poisoning / Supply Chain",
  "exhibit_authority": "New York AI Governance Pack \u2014 Data Integrity + Poisoning Detection Pack",
  "regulatory_frameworks": [
    "EU AI Act Article 10",
    "NIST AI RMF",
    "ISO 42001",
    "NYDFS Circular Letter on AI",
    "NYC Local Law 144",
    "Martin Act"
  ],
  "standards_alignment": [
    "POSS-2",
    "DRP-1",
    "TCB",
    "FRE 707 pre-compliance",
    "ISO 42001"
  ],
  "human_oversight": "accept training batch",
  "third_party_verification": "https://velaru-erra.onrender.com/verify (operator-independent)",
  "data_lineage": "Hash-chained Ed25519 receipts; optional RFC3161 + external anchor",
  "mirror_trap": "Enterprise fine-tunes on poisoned dataset \u2014 enterprise owns breach, data vendor owns ToS. \u00b7 Local Law 144 requires annual bias audit \u2014 Velaru receipts are continuous audit, not annual snapshot.",
  "chain_integrity": {
    "depth": 4,
    "invariant_holds": true
  }
}

EXHIBIT D — DATA INPUTS & VALIDATION
{
  "data_validation_method": "Cryptographic receipt per AI decision; public verify without trusting deployer, vendor, or Velaru operator",
  "bias_testing_proxy": "Asymmetry score from live chain signals",
  "model_change_control": "Policy lock registry \u2014 criteria hash frozen pre-dispute",
  "logging_retention": "90-day pre-dispute window minimum; permanent verify permalinks",
  "external_validator": "Nisaba LLC / Velaru",
  "validator_independence": "Client-side Ed25519 verify; BYOK tri-receipt optional",
  "headline_stat": "Poisoned training data \u2014 prove data hash at ingestion or model compromise indefensible",
  "global_leaders_addressed": [
    "CISA",
    "NIST",
    "Microsoft",
    "Google",
    "OpenAI",
    "NYDFS",
    "NYC DCWP",
    "Goldman Sachs"
  ]
}

MIRROR TRAP (regulatory insight)
Enterprise fine-tunes on poisoned dataset — enterprise owns breach, data vendor owns ToS. · Local Law 144 requires annual bias audit — Velaru receipts are continuous audit, not annual snapshot.

NERVE CARDS — WHY GLOBAL LEADERS CARE
[
  {
    "title": "SolarWinds parallel",
    "body": "AI supply chain compromise \u2014 receipt at data ingestion is detection point.",
    "source": "vertical"
  },
  {
    "title": "ISO 42001",
    "body": "AI management system requires data integrity controls \u2014 receipt satisfies control.",
    "source": "vertical"
  },
  {
    "title": "Incident response",
    "body": "CISA reporting \u2014 poisoned model decision receipt proves attack vector.",
    "source": "vertical"
  },
  {
    "title": "[New York] Financial capital",
    "body": "NYDFS regulates insurers AND banks \u2014 same receipt primitive for both books.",
    "source": "jurisdiction"
  },
  {
    "title": "[New York] Hiring audit law",
    "body": "NYC AEDT law is template for other cities \u2014 receipt architecture scales municipally.",
    "source": "jurisdiction"
  },
  {
    "title": "[Text / Chat] Modality hook",
    "body": "Baseline \u2014 all frameworks apply to text decisions.",
    "source": "modality"
  }
]

BOOK SUMMARY: {
  "total_insureds": 0,
  "compliant": 0,
  "grace_period": 0,
  "non_compliant": 0,
  "expired": 0,
  "not_enrolled": 0,
  "compliant_pct": 0.0
}

TAM / EXPOSURE: Supply chain attacks on ML training · nation-state priority
INSURANCE LINES: Cyber, E&O, Crime

DISCLAIMER: External validation evidence pack — not legal advice, not filed rate approval.