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Compliance & Auditing

QWED-Finance generates cryptographic proof of every verification for regulatory compliance.
“If an AI makes a mistake, the algorithm isn’t sued—the bank is. Your input_hash and signature provide Non-Repudiation.”

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Verification Receipts

Every verification generates a tamper-proof receipt: 
from qwed_finance import ReceiptGenerator, VerificationEngine

receipt = ReceiptGenerator.create_receipt(
    guard_name="ComplianceGuard.verify_aml_flag",
    engine=VerificationEngine.Z3,
    llm_output="Transaction approved",
    verified=False,
    violations=["AML_CTR_THRESHOLD"]
)

print(receipt.to_json())

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Receipt Fields

FieldDescriptionExample
receipt_idUnique identifier"a1b2c3d4-..."
timestampISO 8601 UTC"2026-01-18T14:30:00Z"
input_hashSHA-256 of LLM output"7f83b1657..."
engine_usedVerification engine"Z3"
verifiedPass/failtrue/false
proof_stepsSymbolic derivation["amount=15000", "threshold=10000", "15000>=10000"]

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Cryptographic Signature

# Get tamper-proof signature
signature = receipt.get_signature()
# "8f14e45f..."

# Verify hasn't been modified
expected = hashlib.sha256(json.dumps({
    "receipt_id": receipt.receipt_id,
    "timestamp": receipt.timestamp,
    "input_hash": receipt.input_hash,
    "verified": receipt.verified,
    "engine_used": receipt.engine_used.value
}, sort_keys=True).encode()).hexdigest()

assert signature == expected  # Proof of integrity

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Audit Log

Aggregate receipts for regulatory reporting: 
from qwed_finance import AuditLog

log = AuditLog()

# Log verifications
log.log(receipt1)
log.log(receipt2)

# Get summary
summary = log.summary()
# {
#     "total_verifications": 100,
#     "passed": 95,
#     "failed": 5,
#     "pass_rate": "95.0%",
#     "by_guard": {"ComplianceGuard": 40, "QueryGuard": 60}
# }

# Export for regulators
json_export = log.export_json()

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Query Failed Verifications

# Get all failures for investigation
failures = log.get_failures()

for receipt in failures:
    print(f"Failed: {receipt.guard_name}")
    print(f"  Input: {receipt.input_preview}")
    print(f"  Violations: {receipt.violations}")

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Regulatory Alignment

RegulationQWED Feature
RBI FREE-AIAudit trail with receipts
BSA/FinCEN CTRAML threshold verification
OFACSanctions screening
SOC 2Immutable verification logs
ISO 27001Input hashing & signatures

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Adversarial Defense

We test against “jailbroken” LLMs: 
# tests/adversarial/test_sql_jailbreaks.py

def test_delete_in_subquery():
    """DELETE hidden in subquery should be caught"""
    sql = "SELECT * FROM (DELETE FROM users RETURNING *)"
    result = guard.verify_readonly_safety(sql)
    assert result.safe == False  # ✅ Caught

def test_mixed_case_drop():
    """DrOp TaBlE should be caught"""
    result = guard.verify_readonly_safety("DrOp TaBlE users")
    assert result.safe == False  # ✅ Caught

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Test Suites

SuiteTestsCoverage
test_sql_jailbreaks.py20+SQL injection, UNION, comments
test_math_compliance_jailbreaks.py15+Float precision, AML boundaries

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For Compliance Officers

When a regulator asks: “How do you verify AI decisions?” Show them:
  1. Input Hash — Proof of what the LLM said
  2. Timestamp — When verification occurred
  3. Engine Signature — Which solver verified (Z3/SymPy)
  4. Proof Steps — Symbolic derivation of truth
  5. Receipt Signature — Tamper-proof integrity
{
  "receipt_id": "abc-123",
  "timestamp": "2026-01-18T14:30:00Z",
  "input_hash": "7f83b1657ff1fc53b92dc18148a1d65dfc2d4b1fa3d677284addd200126d9069",
  "guard_name": "ComplianceGuard.verify_aml_flag",
  "engine_used": "Z3",
  "verified": true,
  "proof_steps": [
    "amount = 15000",
    "threshold = 10000",
    "15000 >= 10000 → flag_required = True",
    "llm_flagged = True",
    "llm_flagged == flag_required → COMPLIANT"
  ],
  "signature": "8f14e45f..."
}
:::info GitHub Repository Source code and adversarial tests: github.com/QWED-AI/qwed-finance :::