Did you know that your AI data pipeline may be the largest and most vulnerable attack surface in your data stack? It’s a sobering thought, but it’s important to understand why this is the case.
While modern data stacks are designed for security and privacy, they are primarily focused on aggregate statistics that achieve strong differential privacy, ensuring the anonymity of individuals. However, AI systems must be trained before they can be deployed, and this requires data scientists to access detailed raw data, including sensitive information like addresses and medical conditions. To engineer this data in a machine learning training environment, it’s often necessary to move the sensitive data from your secure data warehouse to a less controlled environment, which can leave it vulnerable to theft and alteration.
Unencrypted sensitive data is stored in insecure locations, making it an easy target for malicious actors. To mitigate these risks, it’s crucial to implement strong security measures in your AI data pipeline. While modern data stacks use RBAC controls for data access and the same cannot be said for machine learning environments and AI data pipelines.
The consequences of using legacy data pipelines for AI can be severe. Here are the top 3 steps you can take to secure the movement and storage of sensitive data:
- Minimize data movement by processing feature engineering inside the database
- Use a feature engineering tool that enforces the same level of RBAC controls as you apply to your databases and data warehouses
- Use version control and keep an audit trail for transparency of how data was used and transformed
By upgrading your data pipeline and implementing these measures, you can protect your organization from potentially devastating security breaches.