Reasoning Engine

HunterX applies AI-assisted reasoning to security assessment. Its value is not raw AI belief — it is a disciplined reasoning pipeline over canonical observations that produces evidence-backed, explainable findings.

Reasoning over observations

Raw tool output is never a verdict. HunterX applies the canonical pipeline:

tool output → parser → normalizer → canonical observation → correlation →
hypothesis → verification → evidence → proof → PoC → replay → finding

The reasoning engine works over canonical observations — normalized, structured results from every tool in the orchestration layer — so reasoning is grounded in what was actually observed.

The reasoning workflow

DISCOVER → FINGERPRINT → REASON → HYPOTHESIZE → PROBE → VERIFY → PROVE →
POC → REPLAY → CORRELATE → REPORT

Evidence-gated confidence

Confidence is a versioned, weighted policy over named factors:

A high AI confidence score is never a substitute for evidence.

Known vs unknown behavior

For behavior that matches a known signature, HunterX applies the relevant proof contract and validates deterministically. For behavior that does not match a known signature, it follows a hypothesis-driven loop:

Unknown Behavior → Observation → Hypothesis → Experiment → Unexpected Result →
New Hypothesis → Minimal Proof Strategy → Replay → Evidence → Candidate Finding

Novel findings remain candidates until sufficient evidence exists. This is hypothesis-driven discovery and investigation — not a claim of guaranteed zero-day discovery.

Explainability

Every validation verdict and finding transition records its reason. Confidence calculations are weighted, auditable aggregates over named factors; reports trace every statement to observation, evidence, validation, proof, impact, tool result, target intelligence or explicit analyst reasoning.