Data freshness and provenance

Rule handbook metadata

Rule: AI.TRUST.DATA_FRESHNESS_PROVENANCE · lane ai
Page status: current
page_version: 0fa9c050f73cc64af6a8bb7cecade42644f585cf4769db4a1984907e04cc6fef
generated_at: 2026-08-13T02:31:57.154Z
registry_fingerprint: aef1de6082cf0f50d463783c843dee0ffb9132fbd5ed4ea6e5bb3f031f359c72

How this rule is fixed

This is an AI-enabled rule. Pass/fail requires model judgment; there is no deterministic fixer in the pilot registry.

Harness: invoke-ai-ruleset-harness.sh runs design-rules/ai/run-design-ai-rule.sh on the Before fixture and expects findings with matching principleId.
Remediation: Cursor agent plans from forge-ux-remediation.plan.md after sitewide audit — not handbook After copy.

Detection module: docs/design/ux-audit/ai-enabled-design-principles.md#ai-trust-data-freshness-provenance. Scroll down for Before / After examples and Evidence and remediation steps.

Purpose

Users can judge whether numbers are current, scoped, and trustworthy. Judgment overlay for AI.TRUST.DATA_FRESHNESS_PROVENANCE.

Required finding metadata

Each finding must include: principleId, severity, deterministicCoverage, candidateDeterministicRule, screenshotOrDomEvidence, hashesOrContractsAffected, confidence, recommendedFixScope, sourceFilesLikelyAffected.