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)
|
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. Custom
document types uploaded to
Enterprise DLP are used in
data profiles as match criteria and can be used 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.
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.
