What's the difference between DLP dictionaries, engines, and policies in Zscaler?

Dictionaries define what content to protect (patterns and phrases), engines combine dictionaries with logical operators to create matching criteria, and policies determine the action taken (allow, block, or require confirmation) when engines detect matches.

How does exact data matching protect sensitive information while using cloud-based DLP?

EDM uses a customer-hosted index VM to process CSV data and generate hash-based templates. Only the hashes are shared with Zscaler's cloud, never the actual sensitive data, allowing the platform to identify matches without ever seeing the original content.

DLP Building Blocks: Dictionaries, Engines & Policy

Name: DLP Building Blocks: Dictionaries, Engines & Policy
Uploaded: 2026-05-11T18:05:30-04:00
Duration: 6 min 5 s
Description: About Zscaler Zscaler (NASDAQ: ZS) accelerates digital transformation so that customers can be more agile, efficient, resilient, and secure. The Zscaler Zero Trust Exchange protects thousands of customers from cyberattacks and data loss by securely con...

Zscaler

05/11/2026

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TL;DR

DLP dictionaries define what data to protect using patterns and phrases with confidence scores indicating detection accuracy from low (pattern-based) to high (phrase-proximity combinations)
DLP engines combine dictionaries with logical operators and match counts to create detection criteria, supporting advanced features like exact data matching and Microsoft MIP label integration
DLP policy connects engines to actions (allow, block, confirm) and enables sophisticated data protection through optical character recognition and index-based matching for structured and unstructured data

Summary

This technical tutorial explains the three fundamental components that form the foundation of data loss prevention policy in Zscaler Internet Access. DLP dictionaries define the content to protect through pattern matching and confidence scoring, ranging from low confidence pattern detection to high confidence phrase-proximity combinations. DLP engines combine multiple dictionaries with logical operators and match counts to create sophisticated detection criteria, supporting advanced features like sub-expressions and exclusions. Finally, DLP policy ties engines to actions—allow, block, or user confirmation—enabling granular control over data protection decisions. The video demonstrates how these building blocks work together through practical examples, including integration with Microsoft Purview Information Protection labels, exact data matching using CSV-based indexing, and index document matching for form detection. Organizations already using MIP labels in Microsoft 365 can import these classifications directly into Zscaler, eliminating duplicate effort while maintaining data security through hash-based matching that never exposes sensitive data to the cloud platform.

Chapters

0:00 - Introduction to DLP Building Blocks
0:47 - DLP Dictionaries and Confidence Scores
1:57 - DLP Engines and Logical Operators
3:05 - Microsoft MIP Label Integration
3:30 - Exact Data Matching (EDM)
5:00 - Index Document Matching and OCR
5:31 - Implementation Recommendations

Key Quotes

0:20 "The first of these are DLP dictionaries. These are the what. These describe content, which is the data you want to protect."
3:24 "This means if you've already done the work of classifying your data inside your M365 tenant, you don't need to do this work again inside Zscaler to benefit from these protections."
3:47 "This is to guarantee the safety and security of your personal data. This generates an index template. No data is ever shared with Zscaler, only hashes to allow the cloud to identify that data inside documents."

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