Cyber Security

Tracing & Attribution EngineIngestion & Normalization

Tracing & Attribution EngineIngestion & Normalization

Victim-submitted data (TXID/hash, wallet addresses, fiat IBAN/BIC, approximate timestamps, amount) is parsed and normalized into a unified event graph.
Multi-Layer Graph Construction Crypto leg: EVM-compatible chains + Bitcoin → full transaction DAG built using Chainalysis/Elliptic/TRM upstream clusters .
Fiat leg: SEPA/SWIFT message reconstruction + downstream correspondent bank enrichment.

Cross-domain bridging: Heuristic linking of on-ramp/off-ramp points (e.g., exchange deposit addresses).

Behavioral & Typological Classification  GNN-based pattern matching against known scam typologies (pig-butchering cluster → centralized mixer → peel chain → gambling/exchange exit).

NLP classifier on victim narrative + transaction metadata for scam-type probability vector.

Exposure & Custodial Mapping  Wallet/exchange tagging returns highest-confidence custodial endpoints.

Fiat trails terminate at beneficiary IBAN/BIC + correspondent bank fields.

Automated Notification & Reporting EngineOutbound Alert Generation.

Structured SAR-like payloads are created per custodial entity (JSON + PDF):

Delivery Mechanism  Crypto exchanges & wallet providers → API POST to compliance endpoint (where available) or secure email drop .

Banks & EMIs → SWIFTNet FileAct / secure email / API (where partner integration exists)  

Fallback: templated PDF sent via encrypted email with DKIM/SPF alignment.

Our Data Sources