Which mechanisms involve data validation, cleaning, and anomaly detection to prevent data poisoning?

Which mechanisms involve data validation, cleaning, and anomaly detection to prevent data poisoning?

• A. Data quality in AI security
• B. Explainability in AI
• C. Human-in-the-loop in AI
• D. Data validation/cleaning and anomaly detection mechanisms

Answer: (D)

Data validation, cleaning, and anomaly detection are all about protecting the data that trains and updates an AI system. In data poisoning, an attacker tries to insert manipulated data to distort the model’s behavior. Validation checks ensure data records conform to expected formats, types, ranges, and cross-feature consistency, so bad entries don’t slip through. Cleaning goes further by removing or correcting noisy or obviously faulty data, reducing the chance of poisoned examples influencing learning. Anomaly detection looks for data points that don’t fit the normal patterns or distributions, flagging suspicious samples for review or rejection before they affect the model. Used together, these mechanisms strengthen the data pipeline, making it harder for poisoned data to contaminate training. Other options focus on different aspects, like explaining how models decide their outputs or adding human oversight, rather than the data-layer defenses described here.

Data validation, cleaning, and anomaly detection are all about protecting the data that trains and updates an AI system. In data poisoning, an attacker tries to insert manipulated data to distort the model’s behavior. Validation checks ensure data records conform to expected formats, types, ranges, and cross-feature consistency, so bad entries don’t slip through. Cleaning goes further by removing or correcting noisy or obviously faulty data, reducing the chance of poisoned examples influencing learning. Anomaly detection looks for data points that don’t fit the normal patterns or distributions, flagging suspicious samples for review or rejection before they affect the model. Used together, these mechanisms strengthen the data pipeline, making it harder for poisoned data to contaminate training. Other options focus on different aspects, like explaining how models decide their outputs or adding human oversight, rather than the data-layer defenses described here.