Data Integrity (e57f5fe3-b0fd-593d-83ac-511d0e07bcce)
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).
Threat-modeling question: Can we detect and prevent data tampering across the AI lifecycle?
| Cluster A | Galaxy A | Cluster B | Galaxy B | Level |
|---|---|---|---|---|
| Poison Training Data (0ec538ca-589b-4e42-bcaa-06097a0d679f) | MITRE ATLAS Attack Pattern | Data Integrity (e57f5fe3-b0fd-593d-83ac-511d0e07bcce) | PLOT4ai | 1 |