US 2025/0094600 A1 · MACHINE LEARNING-BASED FILTERING OF FALSE POSITIVE PATTERN MATCHES FOR PERSONALLY IDENTIFIABLE INFORMATION

Abstract

A data loss prevention (DLP) pipeline is presently disclosed filters non-sensitive documents from DLP with two stages—a first filtering stage that filters documents that do not match one or more patterns corresponding to personally identifiable information (PII) and a second filtering stage that filters documents matching the one or more patterns that are classified with non-sensitive verdicts by a machine learning (ML) ensemble. The ML ensemble comprises a character-level convolutional neural network that generates pattern-based embeddings, a language model that generates context-based embeddings, and a classification model that receives concatenated embeddings as input to generate sensitive/non-sensitive verdicts.

Claims20

Dependent claims

A method comprising:

identifying one or more sub-documents of one or more documents that match one or more patterns for sensitive data;
inputting at least subsets of the one or more sub-documents into one or more natural language models to obtain one or more embeddings as output;
based on concatenating the one or more embeddings, providing the concatenated embeddings as input to a classification model to obtain one or more verdicts for each of the one or more embeddings as output, wherein each verdict of the one or more verdicts indicates whether a corresponding sub-document in the one or more sub-documents comprises sensitive data;
and
filtering, from the one or more documents, those documents that do not comprise a sub-document in the one or more sub-documents having a verdict in the one or more verdicts that indicates sensitive data.

2 more elements

One or more non-transitory machine-readable media having program code stored thereon, the program code comprising instructions to:

classify a document as comprising sensitive data, wherein the program code to classify the document as comprising sensitive data comprises instructions to identify one or more sub-documents of the document that comprise sensitive data, wherein each of the one or more sub-documents comprises text data that matches one or more patterns of sensitive data;
input at least a subset of each of the one or more sub-documents into an ensemble of one or more natural language models and a classification model to obtain one or more verdicts for each of the one or more sub-documents as output;
and
based on a determination that each of the one or more verdicts indicates that a corresponding sub-document of the one or more sub-documents does not comprise sensitive data, indicating the document as having a false positive sensitive data classification.

1 more element

An apparatus comprising:

a processor;
and
a machine-readable medium having instructions stored thereon that are executable by the processor to cause the apparatus to,
preprocess documents to identify one or more documents comprising sensitive data for data loss prevention, wherein the instructions to identify the one or more documents comprise instructions executable by the processor to cause the apparatus to identify one or more patterns of sensitive data in text data of one or more sub-documents of the one or more documents;
input at least a subset of each of the one or more sub-documents into an ensemble comprising one or more natural language models and a classification model to obtain one or more verdicts of each of the one or more sub-documents as output;
and
filter, from the one or more documents, those documents that do not comprise a sub-document in the one or more sub-documents having a verdict in the one or more verdicts indicating sensitive data.

4 more elements

Dependencies are inferred from the claim text.

Machine learning-based filtering of false positive pattern matches for personally identifiable information

Life of this patent

Machine learning-based filtering of false positive pattern matches for personally identifiable information

Life of this patent

Abstract

Claims20