A financial organization found that hostile elements are inserting hidden directives into the data used to improve a fraud detection system, leading to incorrect alerts. Which measure reduces the likelihood of these malicious directives?
Retain refineable datasets on personal devices for convenience in data updates
Measure system resource usage to locate unusual consumption patterns
Apply appropriate filtering and validation to datasets to detect potential manipulations
Use production data during training sessions without added preprocessing or security measures
Filtering and reviewing datasets prior to processing mitigates the risk of harmful instructions shaping the model. Watching system resource usage focuses on workload anomalies rather than protecting data inputs. Using production samples during training without extra protective steps may introduce hidden directives. Storing refineable datasets on personal equipment for convenience places them outside controlled spaces, increasing exposure to tampering.
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What are hidden directives, and how can they affect a system?
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Why is preprocessing and validation critical for datasets?
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What are the risks of using production data during training without preprocessing?