What governance controls exist over how candidate data is reused for rediscovery?

Governance controls over candidate data reuse typically include audit logging of every match and decision, human sign-off requirements before any hiring action, and restrictions on which attributes the matching engine can use to score candidates. These controls exist to answer a compliance team's core question: can the organization show exactly how and why a candidate got matched to a role?

Audit logging records every scan, match score, and hiring manager decision tied to a candidate. This creates a trail that a compliance team can review without having to reconstruct events from memory. Human sign-off requirements mean that no rejection or advancement happens automatically based purely on a match score. This keeps a person accountable for every hiring decision. Attribute restrictions prevent the matching engine from scoring candidates using protected characteristics like age, gender, or national origin. This is crucial for compliance with frameworks like the EEOC's Four-Fifths Rule and the EU AI Act's high-risk classification for recruitment AI.

Tools like Skima AI build governance around these three controls directly. It logs every match score and hiring manager decision for audit purposes. It requires human review at every stage, with no automatic rejections, and scores candidates using only job-relevant signals, never protected attributes. Bias evaluations cover five demographic splits, ensuring that every group remains above the EEOC's Four-Fifths Rule threshold.

Before adopting any rediscovery tool, ask specifically how it logs decisions, whether any step occurs without human review, and what data the matching engine uses to generate a score. A vendor that cannot answer these questions in specific detail has not integrated governance as a core feature.