Research Topic

Trust Validation Framework

Evidence weighting model, confidence labels, and signal taxonomy.

Research Question

Method v1.2 · Reviewed 2026-02-10

How should independent trust signals be weighted so verification outcomes remain consistent over time?

Scope: Defines a repeatable weighting model for identity, technical, safety, and evidence freshness dimensions.

Indicator	Low	Elevated	Critical
Identity & ownership evidence	Public and coherent entity record	Partial ownership visibility	Conflicting or missing entity signals
Technical integrity signals	Consistent transport and infrastructure hygiene	Intermittent hardening gaps	Frequent integrity failures
User-safety implementation	Functional safety controls	Controls exist but weak enforcement	No practical safety controls
Routing and impersonation resilience	Stable official references	Occasional route ambiguity	Frequent brand-clone confusion
Evidence freshness	Reviewed within 30 days	31-90 days old	Older than 90 days with no refresh

1 Classify each evidence source into quality tiers (primary, secondary, anecdotal).
2 Score each framework dimension independently before computing aggregate score.
3 Attach confidence levels based on source quality and freshness.
4 Publish score together with rationale snippets and unresolved gaps.
5 Re-run scoring on schedule or after incidents.

The framework prioritizes verifiable evidence over narrative claims.

Confidence depends on both source quality and recency.

Scores are directional and must be accompanied by evidence context.

Any critical state in identity or evidence freshness blocks high-confidence classification.

Three elevated states across dimensions trigger manual analyst review.

Confidence labels are downgraded if supporting evidence lacks source provenance.

Fresh but weak evidence can appear stronger than old high-quality evidence.

A single strong technical signal can hide legal or ownership uncertainty.

Temporary infrastructure incidents can inflate long-term risk estimates.