Control-Mapped AI Governance for High-Risk HR Decisions in SAP Success Factors: Audit-Ready Metrics for Recruiting, Performance Calibration, and Internal Mobility

Abstract

High-risk human resource decisions increasingly depend on AI-assisted recommendations across recruiting, performance calibration, and internal mobility, yet many enterprise HR environments lack a structured governance model that translates regulatory expectations, ethical principles, and operational controls into measurable system-level safeguards. This study proposes a control-mapped AI governance framework for SAP SuccessFactors that connects high-risk HR decision points with audit-ready metrics, fairness controls, human oversight checkpoints, and decision traceability mechanisms. The framework is designed to support AI-enabled candidate ranking, performance calibration support, skill-based mobility recommendations, and internal talent matching while maintaining alignment with enterprise governance requirements and responsible decision-making practices. The proposed model organizes governance into five layers: decision-risk classification, control mapping, fairness and bias evaluation, human review enforcement, and audit evidence generation. To evaluate the framework, the study defines measurable indicators including Control Coverage Score, Audit Readiness Index, Fairness Risk Index, Human Oversight Effectiveness, Decision Traceability Score, and Recommendation Consistency Ratio. These metrics are applied across simulated SAP SuccessFactors decision scenarios to compare unmanaged AI usage, policy-only governance, rule-based review, and the proposed control-mapped approach. The results demonstrate that structured control mapping improves audit readiness, reduces fairness exposure, strengthens review consistency, and increases transparency across HR decision workflows. The findings suggest that AI governance in SuccessFactors should not be treated as a static compliance checklist, but as a measurable operating model embedded into recruiting, performance, and mobility processes. By converting abstract governance principles into quantifiable controls and reviewable evidence, the framework provides a practical method for organizations seeking to use AI in HR decisions responsibly while preserving accountability, explainability, and trust in enterprise talent systems.