1.6 Challenges in Securing Machine Learning Systems
Despite significant advances, securing ML systems remains a complex task due to the following challenges:
Summary of Challenges
- Dynamic Nature of Threats: Attack techniques continuously evolve, requiring adaptive defenses.
- Trade-offs Between Performance and Security: Improving security often comes at the cost of reduced model accuracy.
- Complexity of ML Pipelines: Multiple stages in the ML lifecycle provide various attack points.
- Lack of Standardized Practices: The field lacks universally accepted frameworks for ML security.
- Resource Constraints: Computational and time resources can limit the implementation of robust defenses.
