Multi-Context DLP Enforcement Across Decentralized SaaS Data Access
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Solution Overview
Problem
Traditional DLP technologies are narrowly focused and fail to consider contextual information when assessing data sharing or transmission events, particularly in decentralized environments like SaaS platforms, leading to inadequate enforcement of security policies and increased data security risks.
Innovation Solution
Implementing a multi-context data loss prevention (MCDLP) system that analyzes various contextual parameters, including user roles and statuses, to enforce comprehensive DLP policies across SaaS platforms and enterprise systems, using an electronic security platform that integrates with identity management and human resource systems to verify and authenticate data access events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional DLP technologies scan emails or files to detect sensitive information, then data transmission can be prevented, but contextual information about user roles, statuses, and authorization is not considered
Solution Approach 1:
The patent extends DLP from traditional single-context scanning to multi-context analysis by adding dimensions of user role, status, and authorization information. The system evaluates data sharing events not just based on content detection but also by analyzing the contextual dimensions of who is sharing, their role, their status, and whether authorization was granted, thereby resolving the contradiction between security reliability and enforcement complexity through structured contextual expansion
2Adaptability or versatility
If SaaS platforms are adopted to streamline organizational operations, then convenience and scalability are improved, but data security control and DLP policy enforcement are weakened due to decentralization
Solution Approach 1:
The patent introduces an intermediary layer between the decentralized SaaS platforms and the organization's security policies. This intermediary system collects contextual information from multiple SaaS platforms, evaluates it against DLP policies, and coordinates enforcement actions across platforms without requiring direct integration with each SaaS service, thereby maintaining SaaS versatility while restoring security control
Solution Approach 2:
The system creates a universal DLP enforcement mechanism that operates across multiple SaaS platforms simultaneously. By building a centralized context collection and evaluation system that can interface with various SaaS services, the patent enables single-policy-enforcement-across-multiple-platforms, resolving the contradiction between SaaS adaptability and data security reliability
3Reliability
If DLP protections are configured on an account-by-account basis for each SaaS platform, then policy enforcement can be implemented, but the configuration process becomes impractical for organizations with many users and platforms
Solution Approach 1:
The patent merges the DLP configuration and enforcement functions into a single centralized system that manages policies across all SaaS platforms and users simultaneously. Instead of configuring each account separately, the system consolidates policy definition, context collection, and enforcement logic into one unified platform, dramatically reducing configuration time while maintaining comprehensive policy enforcement
Solution Approach 2:
The system performs preliminary actions by pre-collecting and pre-processing contextual information from users, roles, and SaaS platforms before data sharing events occur. By maintaining up-to-date context databases and pre-evaluating user attributes, the system enables rapid automated enforcement decisions without requiring time-consuming real-time configuration for each event
Data Source
AI summary
The present disclosure relates to techniques for enforcing multi-context data loss prevention (MCDLP) policies. A security enforcement platform facilitates creation and enforcement of MCDLP security policies that are capable of assessing various contextual parameters relating to activity events that involve sharing, transmitting, or providing access to data assets that include sensitive or protected information. In some embodiments, the security enforcement platform can be configured to remotely monitor activity events originating across various SaaS platforms and to remotely enforce the MCDLP security policies on data assets stored the SaaS platforms. In response to detecting a violation of one or more MCDLP security policies, the security enforcement platform can execute various remediation functions associated with preventing or revoking access to data assets containing sensitive or protected information.