Automatic Policy Manager for Data Loss Prevention
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Solution Overview
Problem
Existing Data Loss Prevention (DLP) solutions require manual creation and management of policies, which is labor-intensive and often requires specialized knowledge, posing challenges for small businesses without dedicated IT Security Officers.
Innovation Solution
The Automatic Policy Manager (APM) system automatically creates and adjusts DLP policies by monitoring data flows, detecting sensitive data, and generating rules based on user justification, thereby reducing the need for manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual policy creation and management is used, then policy accuracy and customization can be achieved, but labor intensity and time consumption increase significantly
Solution Approach 1:
The system enables self-service by automatically monitoring data flows, detecting sensitive information, and generating DLP policies without requiring manual intervention. The policy management system performs self-configuration by analyzing organizational data patterns and autonomously creating appropriate protection rules, eliminating the need for dedicated security personnel to manually craft policies.
Solution Approach 2:
The system implements continuous feedback loops where policy effectiveness is monitored and measured. Data flows are tracked against existing policies, and the system automatically adjusts and refines policies based on observed data patterns, policy violations, and organizational changes. This feedback mechanism ensures policies remain accurate and up-to-date without requiring manual review.
2Adaptability or versatility
If manual policy creation is used, then policies can be customized to organizational needs, but specialized knowledge is required
Solution Approach 1:
The system performs self-configuration by automatically analyzing organizational data flows, identifying sensitive information types, and generating customized policies tailored to the specific organization's needs. The system adapts to organizational structures, data patterns, and compliance requirements autonomously, eliminating the need for specialized security knowledge to create customized policies.
Solution Approach 2:
The system performs preliminary analysis of organizational data flows, user roles, and compliance requirements before policy creation. By pre-configuring policy templates based on detected data patterns and organizational structure, the system prepares customized policy frameworks in advance, reducing the complexity of policy deployment and ensuring adaptability to organizational needs.
3Productivity
If DLP solutions are deployed without policies, then deployment speed increases, but data protection effectiveness decreases
Solution Approach 1:
The system performs preliminary monitoring and analysis of data flows immediately upon deployment, gathering information about organizational data patterns, sensitive information types, and user behaviors. This preliminary action enables the system to automatically generate appropriate policies without requiring manual configuration, achieving both fast deployment and effective protection from the outset.
Solution Approach 2:
The system implements continuous monitoring and feedback mechanisms that automatically detect sensitive data flows and trigger policy creation or adjustment. This real-time feedback ensures that protection effectiveness is maintained by dynamically adapting policies to actual data usage patterns, even during the initial deployment phase before manual policies exist.
4Ease of manufacture
If integration service is used to provide policies, then initial policy setup is simplified, but ongoing maintenance requires vendor dependency
Solution Approach 1:
The system enables organizations to independently manage and maintain their own DLP policies through automated policy creation and adjustment capabilities. The self-service architecture allows organizations to autonomously adapt policies to changing needs without requiring vendor intervention, eliminating ongoing dependency while maintaining ease of policy management through automation.
Data Source
AI summary
A system to create Data Loss Prevention (DLP) policies and adjust DLP policies over time in a computing system using agents running at an endpoint to intercept a data transfer in a network traffic. New data flow/DLP policy rules are created and updated with reference to behavior data of trusted and untrusted users.