What is a common CPMAI security risk in AI deployment, and how can it be mitigated?

• A. Overfitting due to small datasets; mitigated by data augmentation
• B. Data leakage or model inversion; mitigated by encryption, access controls, and secure inference environments
• C. High training costs; mitigated by smaller models
• D. Poor user interface design; mitigated by better dashboards

Answer: (B)

A common security risk in AI deployment is data leakage or model inversion, where sensitive information from the training data can be exposed through model outputs or the way the model behaves. This can happen if endpoints are accessible to untrusted users or if the model is reverse-engineered, potentially revealing parts of the training data or membership information about individuals in the dataset.

Mitigation centers on strong protections around data and access during inference. Use encryption for data at rest and in transit, enforce strict authentication and authorization, and run inference in secure environments such as trusted execution environments or confidential computing platforms. Additional safeguards include minimizing the amount of data exposed by the model, applying differential privacy where appropriate, and maintaining robust monitoring and auditing to detect unusual access patterns.

The other topics address model performance or usability issues (like overfitting or UI design) rather than the security threat of data exposure during deployment.