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Enterprise DLP

About Custom Document Types

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About Custom Document Types

Learn more about how Enterprise Data Loss Prevention (E-DLP) uses custom documents you upload to prevent exfiltration of sensitive data.
Where Can I Use This?What Do I Need?
  • NGFW (Managed by Panorama or Strata Cloud Manager)
  • Prisma Access (Managed by Panorama or Strata Cloud Manager)
  • Enterprise Data Loss Prevention (E-DLP) license
    Review the Supported Platforms for details on the required license for each enforcement point.
Or any of the following licenses that include the Enterprise DLP license
  • Prisma Access CASB license
  • Next-Generation CASB for Prisma Access and NGFW (CASB-X) license
  • Data Security license
Enterprise Data Loss Prevention (E-DLP) supports upload and detection of custom documents containing intellectual property for which you want to prevent exfiltration. You can upload a custom document type to Enterprise DLP, or used a predefined document type, to classify and detect standardized documents and prevent exfiltration of sensitive data. You use the uploaded custom document types in data profiles as match criteria. Additionally, you can use custom document types along with predefined Machine Learning-based data patterns to apply additional ML-based detection algorithms complimented by confidential or sensitive data specific to your organization.
Enterprise DLP uses Indexed Document Matching and Trainable Classifiers to fingerprint and index uploaded custom documents to scan for and detect documents that completely or partially match what you have already uploaded.
  • Indexed Document Matching (IDM)—Used to fingerprint documents and create a document type for documents commonly used by your organization. Uploading multiple documents allows you to create a custom document repository that you can use in a data profile.
  • Trainable Classifiers—Supervised machine learning model that analyzes document types for classifications. As you upload more custom documents as types, Enterprise DLP is able to continuously train the ML model to accurately detect sensitive data matches to inspect for and prevent exfiltration (Positive Training Documents) and those to ignore (Negative Training Set). The upload of set of custom documents using Trainable Classifiers is referred to as a custom document model.
Using IDM and Trainable Classifiers for detection of sensitive data is powerful enables Enterprise DLP to continuously improve its detection capabilities by indexing unstructured text in your documents.
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  • IDM Examples
  • Trainable Classifier Examples
For example, your organization both buys and sells software. You want to only detect instances of sensitive customer data contained in invoices for software that you sell. In this case, you can upload a copy of your organization's invoice as a custom document types for fingerprinting.
However, custom document types will be less effective if you wanted to detect receipts for software your organization purchases. This is because there is too much variance in format between the various software vendors your organization purchases from. Greater document variance results in less accurate detection of matched traffic.

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