Data remediation using an evolving model

A computing system comprising:

• a storage device;
• and
• processing circuitry, wherein the processing circuitry has access to the storage device and is configured to:
• identify, for a plurality of files in a storage system, a plurality of stale files;
• apply a classification model to each of the plurality of stale files;
• identify, based on applying the classification model to each of the stale files, a plurality of unclassified files, wherein each of the unclassified files is one of the plurality of stale files that the classification model was not able to classify with an associated confidence level that exceeds a threshold confidence level, wherein the confidence level indicates accuracy of classification;
• update the classification model over a period of time to generate an evolved classification model, wherein the evolved classification model is trained using training samples developed over time to improve the evolved classification model;
• apply the evolved classification model to each of the unclassified files;
• identify, based on applying the evolved classification model to each of the unclassified files, a subset of the unclassified files that the evolved classification model was not able to classify with a confidence level that exceeds the threshold confidence level;
• and
• delete each of the files in the subset of the unclassified files.

A non-transitory computer-readable medium comprising instructions that, when executed, configure processing circuitry of a computing system to:

• identify, for a plurality of files in a storage system, a plurality of stale files;
• apply a classification model to each of the plurality of stale files;
• identify, based on applying the classification model to each of the stale files, a plurality of unclassified files, wherein each of the unclassified files is one of the plurality of stale files that the classification model was not able to classify with an associated confidence level that exceeds a threshold confidence level, wherein the confidence level indicates accuracy of classification;
• update the classification model over a period of time to generate an evolved classification model, wherein the evolved classification model is trained using training sample developed over time to improve the evolved classification model;
• apply the evolved classification model to each of the unclassified files;
• identify, based on applying the evolved classification model to each of the unclassified files, a subset of the unclassified files that the evolved classification model was not able to classify with a confidence level that exceeds the threshold confidence level;
• and
• delete each of the files in the subset of the unclassified files.