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# Known Limitations and Future Work
## 1. Known limitations
| Limitation | Explanation | Suggested improvement |
|---|---|---|
| JSON storage | The prototype stores data in local JSON files | Replace with PostgreSQL, SQLite, or another database |
| Basic actor header | User identity is simulated through `X-Cosmic-User-Id` | Add full authentication with sessions or identity provider |
| Live connector credentials required | Trello, Jira, Asana, and Microsoft Planner live sync require external app registration | Complete provider-specific production setup |
| Scoring calibration | The scoring model is an implementation proposal | Validate weights with industrial case data |
| Report PDF generation | Reports are HTML and CSV, not server-generated PDF | Add server-side PDF generation if needed |
| No real-time collaboration | Updates require reload or user action | Add WebSocket or server-sent events |
| No notification workflow | Users are not notified of gate changes or overdue mitigations | Add email, Slack, Teams, or PM tool notifications |
| No advanced analytics | The dashboard focuses on operational risk tracking | Add trend analysis, forecasting, and model drift analytics |
## 2. Future work roadmap
### Phase 1: Production data layer
1. Add PostgreSQL schema.
2. Add migrations.
3. Add database indexes.
4. Add transaction-safe writes.
5. Add database backup and restore automation.
### Phase 2: Authentication
1. Add login.
2. Add secure sessions.
3. Add passwordless or SSO authentication.
4. Bind roles to authenticated users.
5. Add admin password reset workflow.
### Phase 3: Live integrations
1. Complete Trello production sync.
2. Complete Jira Cloud production sync.
3. Complete Asana production sync.
4. Complete Microsoft Planner production sync through Microsoft Graph.
5. Add retry and failure handling.
6. Add conflict resolution for bidirectional updates.
### Phase 4: Advanced governance
1. Add configurable gate policies.
2. Add organization-specific risk thresholds.
3. Add approval chains.
4. Add legal and ethical review templates.
5. Add model monitoring rules.
### Phase 5: Research validation
1. Apply the dashboard to a real AI project case.
2. Compare dashboard risk results to expert assessment.
3. Calibrate indicators and weights.
4. Document measurement validity and uncertainty.
5. Prepare academic publication or technical report.
## 3. Final note
The current system is complete enough for academic demonstration and instructor evaluation. It is not a finished enterprise product. Its main value is proving the transformation from framework to software artifact.