Cloud Data Leakage Detection via Attribute Benchmarking
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current data leakage protection (DLP) solutions in cloud environments are coarse-grained and specific, failing to provide comprehensive protection against data breaches, particularly due to their reliance on expensive encryption methods that require users to access data through proxy solutions, which is not always feasible.
Innovation Solution
The combination of text scanners, API scanners, and cloud access security brokers (CASBs) is used to enhance data leakage protection by automating the discovery of altered data elements, improving data governance and classification, and reducing costs by enforcing DLP policies at gateways, while applying user access controls that are agnostic to specific SaaS providers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If encryption methods are used for data protection, then data security is improved, but cost increases
Solution Approach 1:
The system performs preliminary actions by collecting data attributes and creating benchmark data in advance through text scanners and API scanners. This preprocessing enables later detection operations to compare against pre-established benchmarks, avoiding the need for expensive real-time encryption while maintaining security through anomaly detection based on learned normal patterns.
2Reliability
If proxy solutions are used for encryption and decryption, then data protection is improved, but device complexity increases
Solution Approach 1:
The patent extracts the essential security function from complex encryption/decryption proxy systems. Instead of requiring full proxy infrastructure with encryption capabilities, the system extracts only the necessary attribute collection and benchmark comparison functions, implementing security through simplified attribute monitoring and anomaly detection against pre-created benchmarks.
3Reliability
If users must access data through proxy solutions, then data security is improved, but ease of operation deteriorates
Solution Approach 1:
The system implements self-service by automatically collecting data attributes, creating benchmarks, and performing detection operations without requiring user intervention or mandatory proxy access. Users can access data through normal channels while the system independently performs security monitoring by comparing data against pre-established benchmarks, eliminating the need for users to route through complex proxy infrastructure.
Data Source
AI summary
Embodiments describing an approach to receiving user data, and monitoring a user data transaction. Monitoring a user data transaction. Identifying a plurality of attribute elements associated with the user data and the user data transaction. Creating benchmark data based on one or more identified attributes and user data gathered from a user data transaction, and storing, by the one or more processors, benchmark data.