Risk Management
Given the project's technical complexity and the recency and fast evolution of many of the technologies and standards involved, a comprehensive risk assessment was conducted. The main goal is to identify potential risks, evaluate their likelihood and impact, and define preventive measures and contingency actions, ensuring project resilience and adaptability.
Risk Management Measures
| Risk Description | Likelihood | Impact | Prevention Measures / Contingency Actions |
|---|---|---|---|
| Lack of adequate network data for training ML models | Medium | High | Implement synthetic data generation; rely on public datasets. |
| Insufficient computing resources for MLOps pipelines | Medium | Medium | Use cloud resources or lightweight ML models; optimize training; rely on local servers provided by IT. |
| Security breaches in the MLOps pipeline (unauthorized access, data leakage) | Low | High | Implement strong authentication/authorization mechanisms, encryption, audit trails and other tools for governance. |
| Data privacy issues (provenance, auditability) | Medium | High | Define data contracts and privacy guarantees; track data provenance; integrate compliance checks in the pipeline. |
| Model integrity risks (data drift) | Medium | High | Continuously monitor drift; activate automatic retraining triggers; maintain model versioning and rollback mechanisms. |
| Integration failures between services (APIs, Kafka, network components) | Medium | High | Follow API development best practices; use centralized logging; define robust schema registries for Kafka topics. |
| Not enough time (Time Constraints) | Low | Medium | Partners plan the tasks timely. Work is organized into well-defined sprints with clear deliverables and responsibilities. If a task falls behind schedule, extra resources will be assigned to bring it back on track, maintaining progress within the available time. |
| Delays due to partners’ inability to complete tasks or lack of collaboration | Low | High | Redistribute tasks; enforce clear communication and documentation; hold milestone reviews and daily and/or weekly meetings to ensure the plan if being followed. |
| Competing solutions emerging with better performance or trust guarantees | Low | Medium | Monitor state-of-the-art continuously; refine planned features to maintain competitive advantage. |
| Changes in standards for AI governance, security, or telecommunications | Medium | High | Continuous awareness for system adaptations due to evolving requirements and standards. |