Bus factor 1
Losing Anna Mueller cuts off 3 people and 50% of connections.
Main driver: key-person risk — Bus factor 1. Losing Anna Mueller cuts off 3 people and 50% of connections.
Human dependency risks are manageable; the top fixes below are worth doing.
Bus factor 1
Losing Anna Mueller cuts off 3 people and 50% of connections.
3.5 effective approvers
of 5; Luca Rossi holds 44% of approval weight.
4 of 7 critical approvals
can be overridden through a bypass.
Remediations simulated on your data, ranked by how much they raise the score.
Doing all 3 raises the score from 52 to 76 (grade D → C).
Require a second person for Elena Popescu's bypass of Anna Mueller
bypass involving a critical person
Add a deputy for Anna Mueller
single point of failure, cuts off 3 people
Add a deputy for Luca Rossi
single point of failure, cuts off 2 people
Simulated on the model: a deputy shares the person's approvals and escalations; a bypass control means no single person can override, so the bypass is removed.
Who approves, escalates to and can bypass whom. Select a person for details.
Impact is the share of connections between the others that depend on the person.
| Person | Criticality | Impact | Cuts off | Approval share | Flags |
|---|---|---|---|---|---|
| Anna Mueller Security Lead | 0.85 | 50% | 3 | 23% | SPOFcritical |
| Luca Rossi Principal SRE | 0.92 | 39% | 2 | 44% | SPOFcritical |
| Johan Lindberg DevOps Engineer | 0.58 | 39% | 2 | 6% | SPOF |
| Marie Dubois Engineering Manager | 0.78 | 22% | 1 | 11% | SPOFcritical |
| Ines Silva Product Security Engineer | 0.55 | 22% | 1 | — | SPOF |
| Carlos Garcia Platform Architect | 0.74 | — | — | 16% | critical |
| Elena Popescu Incident Manager | 0.69 | — | — | — | |
| Peter Novak Backend Lead | 0.66 | — | — | — | |
| Sofia Papadopoulos Compliance Officer | 0.63 | — | — | — | |
| Tomas Kowalski Software Engineer | 0.50 | — | — | — |
3 errors · 6 warnings, with related ISO/IEC 27001, SOC 2 and DORA controls.
Related controls are informative pointers for audits, not an assessment of compliance.
Remediation. Name a deputy, cross-train them, and document the approvals and emergency procedures this person handles.
Remediation. Add secondary approvers for sensitive workflows and spread ownership of high-impact decisions.
Remediation. Require a second person for the bypass, log every use, and review each use after the fact.
Model hrg-2.
Composite risk = (1 − (1 − 0.4·K)(1 − 0.3·D)(1 − 0.3·B)) / (1 − (1 − 0.4)(1 − 0.3)(1 − 0.3)) = 0.476; score = 100·(1 − risk) = 52
| Metric | Value | Weight |
|---|---|---|
| Key-person risk | 0.425 | 40% |
| Approval concentration | 0.287 | 30% |
| Override exposure | 0.416 | 30% |
| Composite risk | 0.476 |
Grades: A ≥ 90, B ≥ 80, C ≥ 70, D ≥ 50, F below 50. Full definitions and properties: docs/model.md.
Enough to reproduce this result.
| Input file | example_organization.json |
|---|---|
| Input SHA-256 | 824985be282b635f6b341a91b8731172b1c7e8d0b62f723b4ef0c631da1e1034 |
| Generated | 2026-11-13T09:26:35 |
| Tool | human-risk-graph 0.14.0 |
| Model | hrg-2 |
| Weights | α 0.4 · β 0.3 · γ 0.3 |
| Critical threshold | 0.7 |
| Config | none |
| Unconfirmed dependencies | 0 |
| Data gaps | 0 |
| People / dependencies | 10 / 14 |