Request a Demo

TECHNICAL DEEP DIVE

The Engineering Behind Predictive Defense

A multi-layer machine learning stack combining Graph Neural Networks (GNNs), sequence modeling, unsupervised anomaly detection, privacy-aware architectures, and continuous adversarial AI simulations.

SISTAEYES PREDICTIVE FINANCIAL DEFENSE ENGINE

Real-Time Ingestion • Temporal Knowledge Graph • Graph ML • Counterfactual Interventions

01 — GRAPH INTELLIGENCE

Temporal Graph Machine Learning

Static graphs represent fixed connections. The SistaEyes Temporal Knowledge Graph computes edge dynamics, relationship emergence velocity, and temporal decay across millions of financial nodes.

  • Dynamic node embeddings capturing device, account, and IP proximity.
  • Sub-graph extraction for identifying mule ring topologies in sub-milliseconds.
  • Time-decay weighting to distinguish historic ties from sudden novel collusion.
GRAPH TOPOLOGY INFERENCE
// Multi-Hop Mule Ring Extraction
G_temporal = DynamicGraph(V_entities, E_decay_weighted)
NodeEmbedding = GNN_Layer(G_temporal, t_window=900s)
CentralityScore = ComputeSubnetRisk(NodeEmbedding)
>> Cluster #MC-882 identified (Degree Centrality: 0.894)
CONTINUOUS THREAT SIMULATION FEED
[ATTACKER AI] Synthesizing novel distributed UPI proxy split...
[DEFENSE ENGINE] Subnet entropy anomaly detected (p=0.982)
[ATTACKER AI] Introducing 14-day dormant interval latency...
[DEFENSE ENGINE] Temporal graph adjusted. Attack path blocked.
02 — ADVERSARIAL ADAPTATION

Continuous Threat Simulation (AI vs AI)

Fraudsters constantly mutate attack tactics. Rather than waiting for losses in production, SistaEyes deploys an internal Attacker AI that generates synthetic novel attack vectors against defense models to stress-test boundaries.

This continuous reinforcement stress-testing hardens risk vectors against automated script attacks, botnets, and zero-day collusion strategies.

03 — PRIVACY ARCHITECTURE

Privacy-Preserving Threat Intelligence

Financial institutions operate under strict regulatory boundaries. SistaEyes uses cryptographic pseudonymization, tokenized identifiers, secure multi-party aggregation, and zero-knowledge proofs.

  • Cross-institution threat signaling without exposing raw customer PII.
  • Homomorphic encryption for evaluating identity and card hashes.
  • Strict tenant isolation with dedicated hardware enclave execution.
PRIVACY ENCLAVE SPECIFICATION
✓ SHA-256 + Salted Tokenized Account Identifiers
✓ Zero-Knowledge Proofs for Cross-Lender Stacking
✓ Controlled On-Premise / Private Cloud VPC Deployment
✓ Full Audit Immutability Log

Deep Technical Architecture Whitepaper

Request access to the full mathematical whitepaper covering Graph GNN architectures, sequence Markov formulations, and benchmark latency tests.

Request Technical Documentation