Risk Model and Release Governance

Start with critical journeys

List complete business outcomes before listing test cases. For PeopleFlow AI these include creating or importing an employee, approving leave, calculating and approving payroll, sending results to banking/accounting, reconciling dashboards, answering policy questions safely, and preserving tenant isolation.

Score and tier risk

Begin with business impact × failure likelihood, then adjust upward for sensitive data, difficult recovery, architectural complexity, broad tenant exposure, dependency count, or regulatory impact.

Tier	Example	Minimum evidence
Critical	Payroll calculation, authorization, tenant isolation, migration reconciliation	Deep unit/API and negative coverage, full reconciliation, security evidence, rollback, business approval
High	Leave approval, banking integration, HR reports, AI policy answers	Unit/component/API/contract tests, selected E2E, data or AI evaluation, monitoring
Medium	Preferences and notification templates	Functional/API tests, targeted automation, exploratory testing
Low	Cosmetic or rarely used configuration	Review, smoke coverage, exploratory testing when justified

Under schedule pressure, protect critical transactions, data integrity, privacy, isolation, recovery, and rollback. Reduce low-risk browser combinations and cosmetic regression first, and document every omission with an owner and deadline.

Evidence-based release flow

flowchart TD
    A[Identify change and affected journeys] --> B[Score risk and assign tier]
    B --> C[Map each risk to evidence and owner]
    C --> D[Run PR integration and release gates]
    D --> E{Any critical control failed}
    E -->|Yes| F[Block release or safely disable capability]
    E -->|No| G[Review untested scope and open defects]
    G --> H{Authorized owners accept residual risk}
    H -->|No| F
    H -->|Yes| I[Progressive release and monitoring]

Release report checklist

• What was tested and deliberately not tested
• Status of every critical journey
• Open defects and security findings
• Data reconciliation and AI evaluation results
• Performance, resilience, and recovery evidence
• Monitoring, mitigation, rollback, owner, and decision deadline
• Explicit acceptance by authorized business and technical owners

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