Engineering the truth
behind every answer.

1. Data Provenance & Immutable Auditing

We treat data like a high-security asset; if the origin cannot be verified, the AI must not learn it. We implement immutable audit logs to ensure that once a performance metric or training log is recorded, it cannot be altered by an adversary to “retrain” the AI’s logic.

2. Model Drift & Poisoning Detection

We monitor for “model drift”—sudden shifts in AI behavior that indicate the memory has been poisoned. We apply Root Cause Analysis (RCA) to distinguish natural change from deliberate injection of lies.

3. Sector-Agnostic Resilience

In Healthcare: protecting diagnostic integrity. In Finance: defending automated market systems from integrity attacks. In Infrastructure: securing industrial logs as the source of truth.

SOC Alert Triage Engine (SATE)

Problem: Probabilistic LLMs fail at intent validation, inducing catastrophic alert fatigue and allowing bypass payloads.

Architecture: A deterministic O(1) Cryptographic Gate. We suppress the 95% noise floor via Pinecone HNSW vector mapping (cos(θ) > 0.95) and constrain the anomaly tail into rigid JSON schemas before LLM token allocation.

Status: MVP Packaged | Latency < 5ms

AI Memory Defense (AMD)

Problem: Adversarial Context Injection, Poisoning, and IP Theft against Enterprise AI Memory banks.

Architecture: Cryptographic validation of data provenance. Intent invalidation triggers an immediate network signal if the Merkle proof fails. Built with high-grade, non-generic enterprise routing logic.

Status: AWS KMS Production Ready