Endpoint DLP Using Sensitivity Metadata Instead of Local Scans
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Solution Overview
Problem
Existing endpoint data loss prevention (DLP) solutions are resource-intensive, leading to false positives, performance issues, and complex user experiences due to computationally intensive content sensitivity scans, which are not feasible on all endpoints.
Innovation Solution
A small-footprint endpoint DLP (sf-EDLP) solution that enforces security policies using previously generated sensitivity metadata, reducing the need for content sensitivity scans by leveraging cloud-based or local metadata stores for classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If content sensitivity scans are performed at endpoints, then data loss prevention accuracy is improved, but CPU and memory consumption increase significantly
Solution Approach 1:
The patent applies preliminary action by generating sensitivity metadata in advance through cloud-based content sensitivity scans before data is accessed at the endpoint. The metadata store pre-classifies documents as sensitive or non-sensitive, so when data is accessed locally, the classification is already available without requiring intensive scanning at the endpoint moment.
Solution Approach 2:
The patent introduces an intermediary metadata store that sits between the endpoint and the sensitive data. This metadata store contains pre-generated sensitivity classifications and acts as a mediator, allowing the endpoint to make quick classification decisions without directly performing intensive content scanning, thus reducing local computational burden while maintaining accuracy.
2Measurement precision
If content sensitivity scans are performed at endpoints, then sensitivity classification accuracy is improved, but user experience deteriorates due to performance issues
Solution Approach 1:
The system performs sensitivity classification in advance through cloud-based scanning and stores the results in metadata. When users access data at the endpoint, the classification is already determined, eliminating performance delays and maintaining smooth user experience while preserving classification accuracy.
Solution Approach 2:
The patent creates a copy of the sensitivity classification information in the form of metadata that can be quickly retrieved at the endpoint. Instead of performing the heavy scanning operation locally, the system copies the classification results to the endpoint for rapid access, maintaining both accuracy and user experience.
3Reliability
If content sensitivity scans are performed at endpoints, then data loss prevention capability is improved, but device complexity increases
Solution Approach 1:
The metadata store serves as an intermediary layer that simplifies the endpoint system. Instead of implementing complex scanning algorithms at the endpoint, the system uses a simple metadata lookup mechanism that queries pre-generated sensitivity information, significantly reducing endpoint complexity while maintaining DLP capability.
Solution Approach 2:
The patent replaces the mechanical scanning process at the endpoint with a metadata-based query system. The complex content analysis is substituted with simple metadata retrieval and comparison operations, reducing the computational mechanics required at the endpoint while preserving data loss prevention functionality.
4Measurement precision
If content sensitivity scans are performed at endpoints, then sensitivity detection is improved, but runtime computation increases
Solution Approach 1:
The system performs sensitivity detection in advance through cloud-based scanning and caches the results in metadata. When data is accessed at the endpoint, the sensitivity detection is already complete, eliminating runtime computation overhead while maintaining detection accuracy.
Solution Approach 2:
The patent copies the sensitivity detection results into metadata that can be rapidly retrieved at the endpoint. This copying approach transfers the computational burden to a preliminary phase, allowing the endpoint to perform fast metadata-based sensitivity checks without repeating the intensive scanning process, thus reducing runtime computation time.
Data Source
AI summary
The technology disclosed relates to endpoint data loss prevention (DLP). In particular, the technology disclosed relates to enforcing data loss prevention policies at an endpoint without needing to perform content sensitivity scan at the endpoint.