ML Training Data Monitoring & Auditing

This question asks if you keep track of what happens to your AI training data - who uses it, how it changes, and whether it's being used correctly. Think of it as having security cameras and activity logs for your data warehouse. You need to know if someone adds biased data, if data quality drops, or if unauthorized changes occur that could make your AI perform poorly or unfairly.

Without monitoring and auditing, you're flying blind with your AI quality and compliance. You can't prove to regulators or customers that your AI is fair and unbiased. When AI makes a bad decision, you can't trace back to identify if bad training data was the cause. This capability is often mandatory for enterprise contracts and regulated industries. Companies with strong audit trails can quickly address issues and maintain customer trust even when problems arise.

Teams often monitor data uploads but forget to audit data deletions or modifications, missing critical changes that affect model behavior. Another mistake is creating audit logs without establishing processes to regularly review them or set up alerts for anomalies. Many also fail to retain audit logs long enough to meet compliance requirements.