Enterprise DLP
About Enterprise DLP
Table of Contents
About Enterprise DLP
Enterprise DLP
Enterprise Data Loss Prevention (E-DLP)
is a set of tools and processes to protect sensitive information
from exfiltration.Where Can I Use This? | What Do I Need? |
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Or any of the following licenses that include the Enterprise DLP
license
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Enterprise Data Loss Prevention (E-DLP)
is a set of tools and processes that allow you to protect
sensitive information against unauthorized access, misuse, extraction, or sharing.Enterprise DLP
is a cloud-based service that uses supervised machine learning algorithms to
sort sensitive documents into Financial, Legal, Healthcare, and other categories for
document classification to guard against exposures, data loss, and data exfiltration.
These patterns can identify the sensitive information in traffic flowing through your
network and protect them from exposure. Enterprise DLP
allows you to protect sensitive data in the following ways:- Prevent uploads and downloads of file and non-file based traffic from leaking to unsanctioned web application—Discover and conditionally stop sensitive data from being leaked to untrusted web applications.
- Monitor uploads and downloads to sanctioned web applications—Discover and monitor sensitive data when it’s uploaded to sanctioned corporate applications.
To help you inspect content and analyze the data in the correct context so that you can
accurately identify sensitive data and secure it to prevent incidents,
Enterprise DLP
is enabled through a cloud service. Enterprise DLP
supports over 1,000
predefined data patterns and 20 predefined data profiles. Enterprise DLP
is
designed to automatically make new patterns and profiles available to you for use in
Security policy rules as soon they’re added to the cloud service.- Data Patterns—Help you detect sensitive content and how that content is being shared or accessed on your network.Predefined data patterns and built-in settings make it easy for you to protect data that contain certain properties (such as document title or author), credit card numbers, regulated information from different countries (such as driver’s license numbers), and third-party DLP labels. To improve detection rates for sensitive data in your organization, you can supplement predefined data patterns by creating custom data patterns that are specific to your content inspection and data protection requirements. In a custom data pattern, you can also define regular expressions and data properties to look for metadata or attributes in the file’s custom or extended properties and use it in a data profile.
- Data Profiles—Power the data classification and monitor capabilities available on your managed firewalls to prevent data loss and mitigate business risk.Data profiles are a collection of data patterns that re grouped together to scan for a specific object or type of content. To perform content analysis, the predefined data profiles have data patterns that include industry-standard data identifiers, keywords, and built-in logic in the form of machine learning, regular expressions, and checksums for legal and financial data patterns. When you use the data profile in a Security policy rule, the firewall can inspect the traffic for a match and take action.After you use the data patterns (either predefined or custom), you manage the data profiles from thePanorama™ management serverorStrata Cloud Manager. You can use a predefined data profile, or create a new profile, and add data patterns to it. You then create security policies and apply the profiles you added to the policy rules you create. For example, if a user uploads a file and data in the file matches the criteria in the policy rules, the managed firewall either creates an alert notification or blocks the file upload.
When traffic matches a data profile that a security rule is using, a data
filtering log is generated. The log entry contains detailed information
regarding the traffic that match one or more data patterns in the data profile. The log
details enable forensics by allowing you to verify when a matched data generated an
alert notification or was blocked.
You can view the snippets in the data filtering logs. By default, data masking partially
masks the snippets to prevent the sensitive data from being exposed. You can completely
mask the sensitive information, unmask snippets, or disable snippet extraction and
viewing.
Data Classification with Large Language Models and Context-Aware Machine
Learning
Data Classification with Large Language Models and Context-Aware Machine
Learning
Palo Alto Networks
' approach involves augmenting Enterprise DLP
data
patterns traditionally reliant on regular expression matching with machine learning
(ML) classifiers. These data patterns undergo training using diverse data sets,
leveraging Large Lange Models (LLM) to establish ground truth. This integration
significantly enhances accuracy and reduces false positives. Additionally, users can
report false positive detections against the DLP incident where
the false positive detection occurred to facilitate model retraining for improved
accuracy.For example, patterns like credit card numbers or bank account numbers can
vary in length and pose a challenge for strict content-matching approaches, often
yielding to a large number of false positive detections. In such cases all pattern
matches, such as the detection of a 12-digit credit card number, undergo further
processing by specialized ML models designed to comprehend the context of sensitive
data occurrences. Leveraging LLMs enables the generation of high-quality training
and testing data, resulting in best-in-class detection accuracy.
By combining the power of LLMs with context-aware ML models,
Palo Alto Networks
not only enhances detection accuracy of Enterprise DLP
but
also mitigate false positives, thereby fortifying data classification capabilities
and improving your overall security posture.Additional Detection Accuracy
To further improve detection accuracy and reduce false positives, you can also
specify:
- Proximity keywords—An asset is assigned a higher accuracy probability when a keyword is within a 200-character distance of the expression. If a document has a 16-digit number immediately followed byVisa, that's more likely to be a credit card number. But if Visa is the title of the text and the 16-digit number is on the last page of the 22-page document, that's less likely to be a credit card number.Proximity keywords are not case-sensitive. Multiple proximity keywords for a single data pattern are supported.
- Confidence levels—The confidence level reflects how confidentEnterprise DLPis when detecting matched traffic.Enterprise DLPdetermines the confidence level by inspecting the distance of regular expressions to proximity keywords.
- Low—Proximity keyword included in the custom or predefined regex data pattern isn't found within 200 characters of the regular expression match, or if a proximity keyword is included but isn't present in the inspected traffic.When the match criteria specifies a Low confidence level match criteria,Enterprise DLPstill inspects for up to three matches with a High confidence level.
- High—Proximity keyword included in the custom or predefined regex data pattern is within 200 characters of the regular expression match.When the match criteria specifies a High confidence level match criteria,Enterprise DLPstill inspects for up to three matches with a Low confidence level.
Additionally, custom data patterns that don't include any proximity keywords to identify a match always have both Low and High confidence level detections. - Basic and weighted regular expressions—A regular expression (regex for short) describes how to search for a specific text pattern and then display the match occurrences when a pattern match is found. There are two types of regular expressions—basicandweighted.
- Abasic regular expressionsearches for a specific text pattern. When a pattern match is found, the service displays the match occurrences.
- Aweighted regular expressionassigns a score to a text entry. When the score threshold is exceeded, the service returns a match for the pattern.To reduce false-positives and maximize the search performance of your regular expressions, you can assign scores using the weighted regular expression builder when you create data patterns to find and calculate scores for the information that’s important to you. Scoring applies a match threshold, and when a score threshold is exceeded, such as enough expressions from a pattern match an asset, the asset will be indicated as a match for the pattern.For more information, including a use case and best practices, see Configure Regular Expressions.