Can we detect and prevent data tampering across the AI lifecycle?
Can we detect and prevent data tampering across the AI lifecycle?
Data integrity is critical to ensuring that AI systems function as intended. Tampered data, whether during ingestion, transformation, storage, or transfer, can introduce hidden errors, biases, or malicious payloads. AI models built on compromised data may behave unpredictably, yield incorrect results, or violate compliance requirements. Integrity threats may be unintentional (e.g., pipeline errors) or deliberate (e.g., insider sabotage or supply chain attacks).
If you answered No then you are at risk
If you are not sure, then you might be at risk too
Recommendations
- Implement data integrity checks (e.g., hashes, checksums) at critical stages of the data pipeline.
- Use tamper-evident storage (e.g., append-only logs, signed records).
- Employ data lineage and provenance tracking systems to trace the origin and transformation history of data.
- Apply anomaly detection to catch unexpected shifts or inconsistencies in inputs.
- Audit access to data and enforce change tracking on data sources used for training or inference.