End-to-end journey
Each stage links to the screen that should carry the same use case.
01
Opportunity capturedHR proposes an AI screening assistant to reduce recruiter review time by 32% and improve candidate triage consistency.
Pipeline02
Value-feasibility-risk prioritizationHigh business value, medium delivery feasibility, high people-impact risk. Decision: prioritize only with high-risk governance route.
Prioritize03
Governance route explainedHigh-Risk Review triggered by employment impact, personal data, automated recommendation, fairness exposure, and appeal requirement.
Route04
Use case and AI assets registeredUse case links to candidate-profile dataset, ranking model, prompt template, vendor API, and HR workflow integration.
Register05
Obligations and control gaps mappedDPIA, transparency notice, fairness validation, human oversight, appeal process, logging, and incident playbook required.
Obligate06
Backlog execution createdControl gaps become owned delivery tasks with due dates, evidence requirements, acceptance criteria, and reviewer gates.
Execute07
Evidence acceptedDPIA v1.3, bias test v2.0, human oversight UAT, transparency notice, and incident playbook are reviewed and accepted.
Evidence08
Conditional approvalDPO, Legal, HR Risk, Model Risk, and Security approve pilot with residual-risk conditions and monitoring requirements.
Approve09
Monitoring activePost-launch controls track drift, complaints, human overrides, candidate appeals, access events, and cost per screening.
Monitor10
Audit pack generatedAudit pack contains route rationale, obligation map, evidence trail, approvals, incidents, residual risk, and monitoring status.
Audit