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3. Model Performance: Monitoring the performance of the model and looking for any sudden changes that could indicate the presence of poisoned data.
4. Data Provenance: Keeping track of the origin and history of the data to identify any potential sources of poisoning.
5. Data Quality Checks: Regularly checking the quality and consistency of the data to ensure it has not been tampered with.
