Day 1: Mapping HR AI Use and Risk

HR Use Case Inventory

• Ownership expectations for hr use
• Human oversight at high-impact decisions
• Escalation paths for control failures
• Audit evidence informing full traceability

Employee Data Sensitivity

• Performance signals around employee data sensitivity
• Accountability for approved AI outputs
• Governance roles throughout the lifecycle
• Oversight cadence for governance controls

Risk Classification and Ownership

• Validation checks for risk classification
• Policy limits limiting unauthorized use
• Independent review for high-impact cases
• Decision authority and approval thresholds

Day 2: Designing Responsible HR AI Controls

Privacy and Consent Controls

• Performance signals around consent controls
• Control testing prior to operational approval
• Evidence retention for later oversight
• Exceptions handled with documented rationale

Fairness and Non-Discrimination

• Review criteria for fairness and non-discrimination
• Access anomalies needing prompt investigation
• Recovery actions after security events
• Threat signals requiring rapid triage

Human Oversight and Appeals

• Review criteria for human oversight
• Containment steps before wider spread
• Privilege changes needing closer review
• Attack paths across trust boundaries

Day 3: Governing AI Across the HR Lifecycle

Vendor and Model Due Diligence

• Decision points for model due
• Adversarial tests before production release
• Detection gaps exposed by testing
• Identity risks tied to context

Monitoring and Incident Handling

• Practical tests for incident handling
• Escalation thresholds for high-impact threats
• Evidence preserved for incident review
• Malicious patterns across security telemetry

HR AI Governance Framework

• Validation checks for hr AI
• Uncertainty boundaries around analytical conclusions
• Query logic checked against requirements
• Missing values needing deliberate treatment

• Practical lab developing employee data sensitivity for operational use
• Decision simulation testing fairness and non-discrimination in context
• Hands-on workshop applying model due to realistic tasks
• Peer review challenging assumptions around hr AI