What should be included in AI system logging for effective security monitoring?

• A. Only Model Predictions
• B. Access Controls To Data And Models, API Calls, Authentication Events, Data Lineage Changes, Model Drift Events, Data Processing Steps, Anomaly Alerts, And Security Incidents
• C. Only Training Configurations
• D. No Logs

Answer: (B)

Comprehensive logging is essential for security monitoring of AI systems. You need records that capture who accessed data and models, when and from where, and what actions were taken. This includes API calls to interact with the system, authentication events to verify identities, and access controls to data and models to enforce permissions. Tracing data lineage and data processing steps gives visibility into how inputs are transformed and where data originates, which helps detect tampering or improper data handling. Monitoring model drift events and changes in the environment highlights when a model’s behavior shifts, signaling potential data quality issues or adversarial influence. Including anomaly alerts ensures automated visibility into unusual patterns, and logging security incidents supports incident response and forensics.

Logging only model predictions misses who accessed the system, how data flowed, and whether there were governance or security issues. Logging only training configurations misses runtime usage, access, and provenance details. No logs provides no visibility at all, leaving security monitoring blind.