Technology
The science behind enterprise security intelligence. Deterministic ML, unified threat scoring, and real-time threat enrichment.
Unified Threat Scoring
A single Gradient Boosted Trees model trained on all 328 features at once. No human bias in feature partitioning — the model decides what matters.
We initially built five separate scoring axes and combined them with a linear blend. Rigorous backtesting showed one axis carried 95%+ of the predictive power — the axis boundaries were preventing cross-feature interactions. So we replaced the entire multi-axis architecture with a single unified model. Result: cleaner predictions, higher accuracy, and 3 consecutive clean temporal leakage audits.
328 Features
The model decides what matters — no human bias in feature partitioning
AUC 0.837
41% better than EPSS (0.594), the industry standard
90-Day Prediction
Forecasts which CVEs will be exploited within 90 days
4+ Years Backtested
Validated against 365M+ rows of historical EPSS data (2021–2026)
Deterministic
Same input = same output. No LLMs. No hallucination. Fully auditable.
Evidence Bands
CISA KEV — actively exploited in the wild
Observed exploitation attempts, PoC available
Technique overlap with known attacks
Theoretical risk, no observed exploitation
Why Not LLMs?
Large Language Models are powerful but non-deterministic. The same input can produce different outputs. For security scanning, this is unacceptable.
AuditROI uses Gradient Boosted Trees (YDF) compiled to WASM. Every prediction is deterministic, reproducible, and auditable. Run the same scan twice, get the exact same results.
Deterministic
Same input = same output. Always.
Auditable
Feature importance for every prediction. Full explainability.
Fast
WASM inference: <50ms per prediction at the edge.
18 Compliance Frameworks
Experience the Technology
See unified threat scoring, ML-powered detection, and threat enrichment in your first scan.
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