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/home/techb158/trellopowerup.abdallabala.com/docs

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00-design-study.md	3678	0644	edit dl rm
01-uml-class-diagram.puml	11869	0644	edit dl rm
02-use-case-diagram.puml	3763	0644	edit dl rm
03-sequence-diagrams.puml	9732	0644	edit dl rm
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07-design-checklist.md	3256	0644	edit dl rm
08-step-2-storage-layer.md	5325	0644	edit dl rm
09-step-3-risk-crud-ui.md	3380	0644	edit dl rm
10-step-4-mitigation-workflow.md	3996	0644	edit dl rm
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18-step-10-final-academic-submission.md	3199	0644	edit dl rm
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dashboard-spec.md	3691	0644	edit dl rm
methodology-mapping.md	2471	0644	edit dl rm
next-implementation-step.md	1304	0644	edit dl rm
trello-admin-setup.md	2175	0644	edit dl rm

Edit: /home/techb158/trellopowerup.abdallabala.com/docs/09-step-3-risk-crud-ui.md (3380B)

# Step 3: Risk CRUD UI Workflow

## Purpose

Step 3 connects the dashboard interface to the backend storage layer. Users can now manage risks and mitigation actions from the browser instead of using only API calls.

This step implements the use cases defined in the design package:

- Register AI risk.
- Update AI risk.
- Delete AI risk.
- Add mitigation action.
- Recalculate risk dashboard.
- Evaluate deployment gate.

## Source alignment

Source-derived concepts used by this step:

- AI risk must be measurable.
- Risk dimensions are organizational, technical, and human.
- Indicators, evidence, and interpretation rules support risk assessment.
- The software prototype should expose a REST API and integrate with project-management workflows.

Implementation extensions added by this step:

- Browser-based create, edit, delete forms.
- Score preview in the risk form.
- Action buttons inside the risk register.
- Toast notifications.
- Gate evaluation button.
- API workflow test.

## User workflows

### Create risk

1. User opens the Risk register page.
2. User clicks Add risk.
3. User enters title, dimension, domain, lifecycle phase, probability, impact, detectability, owner, status, approval state, due date, and evidence.
4. UI displays a score preview.
5. User saves.
6. API creates the risk in `data/database.json`.
7. Dashboard reloads and recalculates scores, top risks, lifecycle counts, and gate status.

### Edit risk

1. User clicks Edit in a risk row.
2. Existing risk values are loaded into the form.
3. User changes values.
4. API updates the risk.
5. Dashboard reloads and recalculates.

### Delete risk

1. User clicks Delete in a risk row.
2. UI asks for confirmation.
3. API deletes the risk and linked score, mitigation, evidence, and Trello mapping rows.
4. Dashboard reloads.

### Add mitigation

1. User clicks Mitigate in a risk row.
2. User enters mitigation title, owner, status, progress, effectiveness, due date, and description.
3. API creates the mitigation action.
4. Dashboard reloads.
5. Residual risk is recalculated from mitigation progress and effectiveness.

### Evaluate deployment gate

1. User opens the Deployment gate page.
2. User clicks Evaluate and persist gate.
3. API calculates the gate status.
4. API stores gate, criteria, decision, and risk score records.
5. Dashboard reloads.

## Files changed

- `public/index.html`
- `public/app.js`
- `public/styles.css`
- `server.js`
- `package.json`
- `README.md`
- `openapi.yaml`
- `tests/api-workflow.test.js`

## Acceptance criteria

| Check | Expected result |
|---|---|
| Add risk | Risk appears in the risk register after saving |
| Edit risk | Changed fields persist after reload |
| Delete risk | Risk is removed and dashboard total decreases |
| Add mitigation | Mitigation count appears and residual score recalculates |
| Evaluate gate | Gate result is persisted to the JSON database |
| Tests | `npm test` passes risk engine, storage, and API workflow tests |

## Test result

```text
All COSMIC AI-Risk engine tests passed.
All COSMIC AI-Risk storage layer tests passed.
All COSMIC AI-Risk API workflow tests passed.
```

## Development note

This is still a prototype storage design. The JSON database is acceptable for Step 3 because it preserves traceability and keeps the domain model visible. A later step should move this structure to SQLite or PostgreSQL.

# Step 3: Risk CRUD UI Workflow ## Purpose Step 3 connects the dashboard interface to the backend storage layer. Users can now manage risks and mitigation actions from the browser instead of using only API calls. This step implements the use cases defined in the design package: - Register AI risk. - Update AI risk. - Delete AI risk. - Add mitigation action. - Recalculate risk dashboard. - Evaluate deployment gate. ## Source alignment Source-derived concepts used by this step: - AI risk must be measurable. - Risk dimensions are organizational, technical, and human. - Indicators, evidence, and interpretation rules support risk assessment. - The software prototype should expose a REST API and integrate with project-management workflows. Implementation extensions added by this step: - Browser-based create, edit, delete forms. - Score preview in the risk form. - Action buttons inside the risk register. - Toast notifications. - Gate evaluation button. - API workflow test. ## User workflows ### Create risk 1. User opens the Risk register page. 2. User clicks Add risk. 3. User enters title, dimension, domain, lifecycle phase, probability, impact, detectability, owner, status, approval state, due date, and evidence. 4. UI displays a score preview. 5. User saves. 6. API creates the risk in `data/database.json`. 7. Dashboard reloads and recalculates scores, top risks, lifecycle counts, and gate status. ### Edit risk 1. User clicks Edit in a risk row. 2. Existing risk values are loaded into the form. 3. User changes values. 4. API updates the risk. 5. Dashboard reloads and recalculates. ### Delete risk 1. User clicks Delete in a risk row. 2. UI asks for confirmation. 3. API deletes the risk and linked score, mitigation, evidence, and Trello mapping rows. 4. Dashboard reloads. ### Add mitigation 1. User clicks Mitigate in a risk row. 2. User enters mitigation title, owner, status, progress, effectiveness, due date, and description. 3. API creates the mitigation action. 4. Dashboard reloads. 5. Residual risk is recalculated from mitigation progress and effectiveness. ### Evaluate deployment gate 1. User opens the Deployment gate page. 2. User clicks Evaluate and persist gate. 3. API calculates the gate status. 4. API stores gate, criteria, decision, and risk score records. 5. Dashboard reloads. ## Files changed - `public/index.html` - `public/app.js` - `public/styles.css` - `server.js` - `package.json` - `README.md` - `openapi.yaml` - `tests/api-workflow.test.js` ## Acceptance criteria | Check | Expected result | |---|---| | Add risk | Risk appears in the risk register after saving | | Edit risk | Changed fields persist after reload | | Delete risk | Risk is removed and dashboard total decreases | | Add mitigation | Mitigation count appears and residual score recalculates | | Evaluate gate | Gate result is persisted to the JSON database | | Tests | `npm test` passes risk engine, storage, and API workflow tests | ## Test result ```text All COSMIC AI-Risk engine tests passed. All COSMIC AI-Risk storage layer tests passed. All COSMIC AI-Risk API workflow tests passed. ``` ## Development note This is still a prototype storage design. The JSON database is acceptable for Step 3 because it preserves traceability and keeps the domain model visible. A later step should move this structure to SQLite or PostgreSQL.